A comprehensive survey on enhancement of system performances by using different types of FACTS controllers in power systems with static and realistic load models

Singh, Bindeshwar; Kumar, Rajesh Article A comprehensive survey on enhancement of system performances by using different types of FACTS controllers in power systems with static and realistic load models Energy Reports Provided in Cooperation with: Elsevier Suggested Citation: Singh, Bindeshwar; Kumar, Rajesh (2020) : A comprehensive survey on enhancement of system performances by using different types of FACTS controllers in power systems with static and realistic load models, Energy Reports, ISSN 2352-4847, Elsevier, Amsterdam, Vol. 6, pp. 55-79, https://doi.org/10.1016/j.egyr.2019.08.045 This Version is available at: https://hdl.handle.net/10419/244015 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ Energy Reports 6 (2020) 55–79 Contents lists available at ScienceDirect Energy Reports journal homepage: www.elsevier.com/locate/egyr Review article A comprehensive survey on enhancement of system performances by using different types of FACTS controllers in power systems with static and realistic load models Bindeshwar Singh ∗,Rajesh Kumar Kamla Nehru Institute of Technology, Sultanpur (U.P.) 228118, India article info Article history: Received 3 July 2019 Received in revised form 27 July 2019 Accepted 22 August 2019 Available online xxxx Keywords: FACTS controllers Load models Optimization techniques System performances Power systems abstract In present time the power demand increases more rapidly in the power generation and transmission and distribution system sectors. For maintaining the power system stability and steady state operation of power systems causes from disturbance, faults and suddenly changing of the load, voltage instability, voltage swell, voltage sag, harmonics, and change in frequency, stability is the most important factor for the power systems. Due to instability, different problems occur in power systems such as fluctuation in voltage and frequency, which may cause damage or failure of power systems. This article presents taxonomical survey on optimization techniques for enhancement system performance by FACTS controllers such as Thyristor Controlled Series Capacitor (TCSC), Thyristor Controlled Phase Angle Regulator (TC-PAR), Static VAR Compensator (SVC), Dynamic Voltage Restorer (DVR), Sub-Synchronous Series Compensator (SSSC), Static Synchronous Compensator (STATCOM), distributed-STATCOM, Unified Power Flow Controller (UPFC), Generalized Unified Power Flow Controller (GUPFC), Unified Power Quality Conditioners (UPQC), Unified Dynamic Quality Conditioners (UDQC), Interlink Power Flow Controller (IPFC), Generalized Interlink Power Flow Controller (GIPFC) and Hybrid Power Flow Controller (HPFC) etc. in power systems with static and realistic load models. The system performances such as real and reactive power losses, power quality, voltage profile, power system oscillations, loadability of system, reliability and security of system, power system stability, bandwidth of operations, flexible operations, available power transfer capacity, system power factor, environmental etc. are enhanced by optimally placed different types of FACTS controllers in power systems with static and realistic load models. In this survey article will be very much helpful to the researchers and practitioners for finding out the relevant references in the field of enhancement system performances by different types of FACTS controllers in power systems with static and realistic load models. ©2019 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Contents 1. Introduction......................................................................................................................................................................................................................... 56 2. A taxonomical survey on enhancement of system performances by different types of FACTS controllers in power systems with static and realistic load models .......................................................................................................................................................................................................... 57 2.1. Conventional methods........................................................................................................................................................................................... 57 2.2. Optimization techniques....................................................................................................................................................................................... 58 2.3. Artificial intelligence computational techniques................................................................................................................................................ 58 2.4. Hybrid techniques.................................................................................................................................................................................................. 63 3. Summary of paper.............................................................................................................................................................................................................. 69 3.1. Conventional techniques....................................................................................................................................................................................... 69 3.2. Optimization techniques ...................................................................................................................................................................................... 70 3.3. Artificial intelligence computational techniques................................................................................................................................................ 70 3.4. Hybrid techniques.................................................................................................................................................................................................. 70 3.5. Current techniques ................................................................................................................................................................................................ 71 ∗Corresponding author. E-mail addresses: [email protected] (B. Singh), [email protected] (R. Kumar). https://doi.org/10.1016/j.egyr.2019.08.045 2352-4847/©2019 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). 56 B. Singh and R. Kumar / Energy Reports 6 (2020) 55–79 3.6. Comparisons of all optimization techniques ..................................................................................................................................................... 71 4. Conclusions and future scopes of the survey article ..................................................................................................................................................... 73 4.1. Conclusions............................................................................................................................................................................................................. 73 4.2. Future scopes of survey article ............................................................................................................................................................................ 73 References ........................................................................................................................................................................................................................... 73 Abbreviations FACTS Flexible Alternating Current Transmission Systems ANN Artificial neural network ABC Ant Bee colony algorithm GA Genetic algorithm ABC Ant bees colony TCSC Thyrised controlled series capacitor TCPAR Thyrised controlled phase angle regulator TS Tabu search EP Evolutionary Programming MMPSs Multi-machine power systems PSO Particle swarm optimization MINLP Mixed integer non linear programming MILP Mixed integer linear programming SSSC Static synchronous series capacitor STATCOM Static synchronous compensator TCSC Thyrised controlled series capacitor UPFC Unified power flow controllers SVC Static var compensators SSSC Sub-series synchronous compensators SA Sensitivity Analysis STATCOM Static synchronous compensator DSTATCOM Distributed-Static synchronous compensator HPFC Hybrid power flow controllers Symbols fSupply frequency (50 Hz) 1. Introduction The different types of FACTS controllers are used for enhancement of system performances. These are discusses as follows: (i)Series connected FACTS controllers: Such as TCSC, SSSC, TCPAR and TCR etc. (ii)Shunt connected FACTS controllers: Such as SVC, DVR, STATCOM and DSTATCOM etc. (iii)Series–series connected FACTS controllers: Such as IPFC and GIPFC etc. (iv)Shunt-series connected FACTS controllers: Such as UPFC, GUPFC and HPFC etc. The above types of FACTS controllers are more effective for enhancement of system performances viewpoints. The system performances enhanced by FACTS Controllers in power systems with static and realistic load models. These different issues are considered by researchers are as follows: •Size and location of FACTS controllers •Size, location and type of FACTS controllers •Location and type of FACTS controllers •Size and type of FACTS controllers •Size, type and coordination of FACTS controllers •Size, location and coordination of FACTS controllers •Size, location, type and coordination of FACTS controllers The following conventional techniques are reviewed for the optimization techniques for voltage stability analysis by using different types of FACTS controllers in power systems with static and realistic load models such as Model analysis (Vasquez Arnez and Cevra,2008;Nam et al.,2000;Perkin and Ira,1997;Li et al.,2017;Hridya et al.,2015a;Jiang et al.,2010;Guo et al., 2018a;Kinjo et al.,2006;Alhasawi and Jovica,2012;Pilotto et al.,1997a;Gibbard and Vowles,2000;Rouco,1997;Gholipour, 2005;Mehouachi et al.,2019;Gao et al.,1992;Huang et al., 2000), Index method (Carpinelli et al.,2015;Chang et al.,2009b; M. M. Farsaang,2004;Okamoto and Kurita,1995;Bazanella and Shilva,2001;Pilotto et al.,1997b;Kalyan Kumar et al., 2007;Limyingcharoen et al.,1998;Hammad and Al Sadek,1989; Canizare et al.,1996;Gubina and Strmcnik,1995;Lof et al.,1993; Naoto et al.,2003), Controlling method (Ye et al.,2005;Zhu et al.,2010;Wei et al.,2005;Bedoya et al.,2008;Gabrijel et al., 2002;Padhiyaar et al.,1998), Residue analysis (Chen et al.,2003; Krishnan et al.,2018;Adnan et al.,2018), Numerical optimization (Monod and Mohan,2010;Li and Li,1993), Eigen value (Li et al.,2012a;Pilotto et al.,1997c;Wang et al.,2003;Hossain et al.,2012) and Sensitivity method (Leiu et al.,2000;Hridya et al.,2015b;Zhao and Jiang,1995;Ooi et al.,1997;Crisan,1994; Cutsem,1995;Subhanga et al.,2002;Mehraeen et al.,2010;Fang et al.,2009;Pal,2002;Morioka et al.,1999b;Manganuril et al., 2016;Wei et al.,2004;Ciofi et al.,2002;Bakar and Desa,2017; Hamed et al.,2013;Tiwari and Ajjarapu,2011;Ardhakhani et al., 2016). The following optimization techniques are reviewed for the optimization techniques for voltage stability analysis by using different FACTS controllers in power systems with static and realistic load models such as Linear programming (Ara et al., 2012;Chen et al.,1995;Wang and Tan,2002;Zhang et al.,2004; Panda and Ramnarayan,2006;Morioka et al.,1999a), MINLP (Cai et al.,2005;Saba et al.,2010;Nojavan et al.,2018;Tan and Wang,1997;Zarghami et al.,2010;Nikoobakht et al.,2018), Stochastic Load Flow (Wibowo et al.,2011;Rahimzadeh and Bina, 2011;Jadidbonab et al.,2019;Noruzian et al.,1997;Hamon et al.,2013), Adaptive control law (Tan,1996;Muzzammel et al., 2015), Analytical Approach (Araby et al.,2002;Orfano Gianni and Bacher,2003;Verma and Srivastava,2005;Huang et al.,2012; Orfanogianni and Bacher,2003;Wen et al.,2004;Tan and Wang, 1998;Gitizadeh,2009;Sode Yome et al.,2006;Limyingcaron et al.,1998;Gao et al.,2017), Dynamic Programming (Khalesi et al.,2011;Kumkhratuge et al.,2003), OPF (Gan et al.,2000; Kamarposhti et al.,2008;Acchha et al.,2000;Padhy and Abdel, 2004;Zhang et al.,2001;Lie and Deng,1997a;Xhiaon et al., 2002;Choudari et al.,2004;Perez et al.,2006;Kamel et al., 2015;Carpinelli et al.,2006;Ajjharapu and Crishty,1992;Hu et al.,2016;Goliphour and Shadate,2005;Noruzhian et al.,1997; Vahhedhi et al.,1999) and Mixed-Integer Optimization (Gharbex et al.,2001;Zharghami et al.,2010;Ghaliana et al.,1996;Sinh et al.,2006;W. Zang and Tholbhert,2007;Lie and Deng,1997b; Araby et al.,2002;Urdhanata et al.,1991;Zou et al.,1999;Kumar and Dhave,1995;Tahbhoub et al.,2018;Zhau,1993;Ajjarapu and Chrithy,1992;Ghyugi et al.,1999;Marthins and Lheema,1990; Lhu and Abhour,2002;Yorinho et al.,2003;Chang and Huang, 1997a;Lashkar et al.,2012;Parker et al.,1996;Araby et al.,2002; B. Singh and R. Kumar / Energy Reports 6 (2020) 55–79 57 Table 1 Different types of FACTS controllers and their controlled parameters. Types of FACTS Characteristics Controlled parameters Examples Series Control reactance Real & reactive power TCSC, SSSC & TC-PAR etc. Shunt Control susceptance Reactive power SVC, STATCOM & DSTATCOM etc. Series– Series Control V and δReal & reactive power IPFC & GIPFC etc. Shunt-Series Control X, V and δ Real & reactive power UPFC, UPQC, UDQC, GUPFC & HPFC etc. Xie et al.,2003;Abdel-Magid et al.,2000;Mihilhac et al.,1996; Ghyugyi et al.,1999;Wildenhues et al.,2014;Paramasivam et al., 2013;Deng et al.,2018;Qader,2015;Orfano Gianni and Bacher, 2003;Kumar et al.,2007a). The following artificial intelligence techniques are reviewed for the optimization techniques for voltage stability analysis by using different types of FACTS controllers in power systems with static and realistic load models such as GA (Dhas et al.,2000; Hazhra et al.,2007;Yashida et al.,2000b;Vijaykumar and Kumudini Devi,2007;Chandrasekaran et al.,2005-2009;Yang et al., 2007;Accha et al.,2004;Morris et al.,2003;Bakhirtzis et al., 2002;Vijayakumar and Kumudini Devi,2008;Priti et al.,2011; Tukharam et al.,1996;Gerbhex et al.,2001;Vijaya kumar and Kumudini devi,2007;Abdel-Magid et al.,1999), Symbiotic organism search (SOS) algorithm (Pandey and Gupta,2018;Verbic and Gubina,2004;Dillon,1991;Pandha and Ardhil,2007), Computational simulations (Lima et al.,2003;Vijaykumar and Kumudini Devi,0000;Sadhegh et al.,2005;Renzh et al.,1999), Tabu search based method (Mori and Goto,2000), Monte Carlo simulation (Etingov et al.,2007;Blanco et al.,2011), Intelligent programming (Shadhegh et al.,2005;Chang and Huang, 1997b), ABC algorithm (Mohamad Idris et al.,2010;Shyh and Xian,2013), ANN method (Feurthe Eshquivel and Accha,1997; Kheib et al.,1995;Mozhgan,2012;Zeynelgil and Demiroren, 2002;Nguyen et al.,2008;Daskh and Paanda,2000;Nar et al., 1990), Fuzzy linear programming (Tomsovics,1992;Cai and Erlich,2006;Venkatesh et al.,1999;Mishra et al.,2000;Daas et al., 2000;Limchron et al.,1998), and PSO method (Benabidh et al., 2009;Panda and Ardil,2008;Rashidi and Hawary,2009;Abido, 2002;Yoshida et al.,2000;Kannan et al.,2004;Yashida et al., 2000a;Shayeghi et al.,2009) (see Table 1). The hybrid techniques are reviewed for the optimization techniques for voltage stability analysis by using different types of FACTS controllers in power systems with static and realistic load models such as hybrid of ABC and ANN (Satheesh and Manigandan,2013), hybrid of GA and OPF (Chung and Li,2001;Phalliate et al.,1997;Mustafa and Chiew,2008;Subhanga and Kulkaarni, 2002;Gerbex et al.,2001;Xu and Ahmad Zaid,1995;Liu et al., 2006;Abhou Elaa et al.,2010;Mithulantan et al.,2003;Mahdad et al.,2009;Ippolito et al.,2006), hybrid of OPF and SA (Chatterji et al.,2007;So et al.,2005), hybrid of GA and FZ (Jeevarathinam, 2006;Li et al.,2012b), hybrid of SA and EP (Alamhelu et al., 2008), hybrid of PSO And MINLP (Ko et al.,2009), hybrid of SA and MILP (Chung et al.,2003), hybrid of OPF and PSO (Chandra sekaran et al.,2009;Niknam et al.,2012), hybrid of ABC and OPF (Rezaei and Karami,2013), hybrid of Lyapunov Theory and Fuzzy Logic (Kimkhratug,2012) and hybrid of OPF and Fuzzy Logic (Nurzian et al.,1996). Some other techniques are reviewed for the optimization techniques for voltage stability analysis by using different types of FACTS controllers in power systems with static and realistic load models such as Energy approach (Hoque,2004,2008;Wenzel and Leibfried,2012;Goksu et al.,2014;Pillottho et al.,1997;Yeu et al.,2001;Kundur and Morison,1997;Zhou,1993;Kobayashi et al.,2003;Milhalic and Zhunko,1996;Farsangi et al.,2007; Niaki and Irhavani,1996;Manshour et al.,1994;Khederzadeh and Ghorbani,2012a;Aghaei et al.,2016;Angqhuist et al.,1993; Azhbe et al.,2005), Active control technique (Khederzadeh and Ghorbani,2012b;Gonzhalez et al.,2010;Shong et al.,2010; Baek et al.,2013;Guo et al.,2018b;Kanan et al.,2004;Edris, 2000;Whang and Shwift,1996;Paphic et al.,1997;Kumar et al., 2007b;Reis and Maciel Barbosa,2006;Mohsen et al.,2013; Zenghyu et al.,2000;Venkataramanan and Johnson,2002;Mancilla David and Venkataramanan,2000;Khederzadeh and Sidhu, 2006;Pilotto et al.,1997;Canizares et al.,1999;Park and Baek, 2012;Noruzian et al.,1999;Shukla and Mili,2017), Passivity method (Yorinno et al.,2003;Gui et al.,2016;Zheao et al.,1998; Sauer and Pai,1990;Conejo et al.,2006;Chang et al.,2009a;Rhao et al.,2000;Kundhur et al.,2004;Potamianakis and Vournas, 2006), Hybrid technique (Kejani and Gholipour,2017;Hu et al., 2018;Dhong et al.,0000;Hajagos and Danai,1998;Kumkrahug et al.,2003;Vahidna et al.,2016;Hua et al.,2019), Pole placement techniques (Faaershangi et al.,2007;Kesshel and Ghlavitsch, 1986;Thukaram et al.,2005;Ahsaee and Sadeh,2011), Graph search algorithm (Madhtharad et al.,2005;Niaki and Irhvani, 1996;Larshen et al.,1995), Decomposition coordination methods (DCM) (Olivheira,1994;Whei et al.,2005), Whale optimization algorithm (Sahu et al.,2017), Gray Wolf Optimizer (Ladhumaor et al.,2017a), Meta-heuristic algorithm (Ladhumaor et al.,2017b; Wildheunhues et al.,2015), Salp Swarm Optimizer (Kuyu and Vatansever), Grasshopper Optimization Algorithm (Hamour et al.) and Ant Lion Optimization Technique (George et al.), Spider Monkey Optimization (Nayak et al.;Hazrati et al.;Kaur et al.; Behera et al.,2018). The enhancement of system performances by different types of FACTS controllers in power systems with static and realistic load models are presented in Tables 2–6. This article organized as follows: Section 2discusses the survey on optimization techniques for system performances by different types of FACTS controllers. Section 3introduces the summary of the article. Section 4discusses the conclusions and future scope of survey article. 2. A taxonomical survey on enhancement of system performances by different types of FACTS controllers in power systems with static and realistic load models A taxonomical survey on optimization techniques for enhancement of system performances by different types of FACTS controllers in power systems with static and realistic load models.The taxonomical survey of these optimization techniques such as conventional techniques, optimization techniques, artificial computational intelligence, hybrid and current optimization techniques are presented in Tables 2–6, respectively. The conventional techniques, optimization techniques, artificial computational techniques, hybrid techniques and current techniques are discussed in Sections 2.1–2.5, subsequently. 2.1. Conventional methods The conventional methods for enhancement of system performances by different types of FACTS controllers in power systems with static and realistic load models are discussed as follows: Model analysis method: Modal analysis is used for the dynamic properties of systems in the frequency domain. These are the collection of (1) sensors (transducers) like accelerometers, load cells, or stereo photogrammetric cameras (2) data acquisition 58 B. Singh and R. Kumar / Energy Reports 6 (2020) 55–79 system and an A/D converter front end for digitize analog instrumentation signals and (3) host Personal computer to view the data and analyze it. Modal analysis method uses the overall mass and stiffness of a structure to find out the various time periods at which it will naturally resonate. Index method: This method is a composite, statistic a measure of changes in a group of individual data points or a compound measures that aggregate multiple indicators. Indexes summarize and rank specific observations. Indexes are usually weighted equally, unless there are some reasons against it (i.e. if two items reflect each other essentially in same aspect of a variable, they could have a weight of 0.5 each). Controlling method: The objective of this method is to develop a control model for controlling such systems in an optimum manner without delay or overshoot and ensuring control stability. This type of controller monitors the controlled process variable and compares it with the reference or set point. The error signal is the difference between actual and desired value of the process variable. Therefore major application of controlling method is in control systems, which deals with the design of process control systems for industry, other applications range far beyond this. Residue analysis method: Residues method can be computed very easily and once known, allow the determination of general contour integrals via the residue theorem. The residue theorem is used in the integral is extended to the complex plane and its residues are computed, and real axis is extended to a closed curve enclosed by a half circle in the upper or lower half plane and forming a semicircle. Therefore the half part of the integral will tend towards zero as the radius of the half-circle increases, leaving only the real-axis part of the integral. Numerical optimization method: In this method algorithms that use numerical approximation for the problems of mathematical analysis. These methods are finds application in all fields of engineering and the physical sciences, but in 21st century also the life sciences, social sciences, medicine, business. Maths and computer that generates, analyzes and implements algorithms the growth in power and the revolution in computing has raised the use of realistic mathematical models. This method continues this long tradition of practical mathematical calculations. Eigen value method: Eigen value algorithms may also find eigenvectors. Algorithms produce every eigen value; others will produce a few, or only one. Algorithms can also be used to find all Eigen values. When an Eigen value of a matrix has been chosen, it can be used to either direct the algorithm towards a different solution next time. When an Eigen value does not produce eigenvectors, then use an inverse iteration based algorithm with µset to a close approximation to the Eigen value. Sensitivity based method: Sensitivity analysis is useful for following purposes: •Testing the robustness of a model or system. •Increased the relationships between input and output variables in a system or model. •Searching for errors in the model. •Model simplification. 2.2. Optimization techniques The optimization techniques for enhancement of system performances by different types of FACTS controllers in power systems with static and realistic load models are discussed as follows: Linear programming method: Linear programming is a method to achieve the best outcome in a mathematical model which is represented by linear relationships. Linear programming is a technique used for the optimization of a linear objective function, subject to linear equality and linear inequality constraints. Its objective is a real valued function. A linear programming algorithm finds a point where this function has the smallest (or largest) value if such a point exists. Mixed integer non linear programming (MINLP): Mixed integer nonlinear programming is used to solve optimization problems with continuous and discrete variables. MINLPs arise in applications in a wide range of fields including chemical engineering, finance and manufacturing. Stochastic load flow method: Stochastic is an adjective word in English that describes something was randomly determined. The word to describe a mathematical object called a stochastic process, but now in mathematics the terms stochastic process and random process are considered interchangeable. Adaptive control law method: Adaptive control is a method used to controlled system with parameters which vary or are initially uncertain. E.g. as an aircraft flies, its mass will slowly decreases as a result of fuel consumption, a control law is needed that adapts itself to such changing conditions. This method is different from robust control method that it does not requires prior information about the bounds on these time varying parameters, robust control changes are within given value the control method need not to be changed, while adaptive control method is concerned with control law changing itself. Analytical approach: Analytical approach is used for analysis to break a known problem into the sub parts necessary to solve it. When the approach is ready to solve a problem, then it determines the probability of solving it. Analytical approach is used only for approach that works on difficult problems. Analytical approach is also called as ‘‘structuring one’s analysis’’. In this analysis a problem is break down into different smaller problems, by which they can be solved individually. Therefore a good analysis uses a process to direct the analysis. Dynamic programming: This programming method is used for solving a problem by breaking it into different simpler sub problems, after solving these sub problems and stored their results using a memory-based data structure. These sub problem results is indexed in some way, typically based on the values of its input parameters, so as to facilitate its lookup. So same sub problem occurs at next time, in place of recomputing its solution, one simply looks up the previously computed solution, thereby saving computation time. This technique of storing solutions to sub problems instead of recomputing them is called memorization. Optimum power flow (OPF) method: Power flow method is important for planning future expansion of power systems as well as in determining the best operation of existing systems. The main information collected from the power flow study is the magnitude and phase angle of the voltage at each bus and the real and reactive power flowing in each line. There are some programs which used the linear programming to find out the optimal power flow and the conditions which give the lowest cost per kilowatt hour delivered. Mixed-integer optimization method: An integer problem in optimization method is a mathematical solution, where some or all of the variables are limited to be integers. The term refers to integer linear programming, in which the objective function and the constraints are linear. If some decision variables are not discrete, then the problem is known as mixed-integer programming problem. 2.3. Artificial intelligence computational techniques The artificial intelligence computational techniques for enhancement of system performances by different types of FACTS controllers in power systems with static and realistic load models are discussed as follows: B. Singh and R. Kumar / Energy Reports 6 (2020) 55–79 59 Table 2 A survey on conventional methods for enhancement of system performances by different types of FACTS controllers in power systems with static and realistic load models. Ref. No. Author Year Power system performance FACTS controllers used Parameters for optimization Load models Techniques Future work Vasquez Arnez and Cevra (2008) R. Leon Vasquez et al. 2008 System stability SVC Location & size Static Model analysis Multi-objective task Nam et al. (2000) Hae-Kon Nam et al. 2000 Power loss reduction TCSC Location & size Static Model analysis Hybrid techniques Perkin and Ira (1997) B. K. Perkins 1997 Power system stability STATCOM Location & size Static Model analysis Practical implementation Li et al. (2017) Can Li et al. 2017 System protection DSTATCOM Location & size Static Model analysis Robustness Hridya et al. (2015a) Hridya.K.R et al. 2015 System stability UPFC Location & size Static Model analysis Hybrid techniques Jiang et al. (2010) Xia Jiang et al. 2010 System stability SVC & TCSC Location & size Static Model analysis Realistic load models Guo et al. (2018a) Yifei Guo et al. 2018 System reliability SVC & STATCOM Location & size Static & realistic Model analysis Environmental friendness Kinjo et al. (2006) T. Kinjo et al. 2006 System stability SVC & UPFC Location, size & type Static Model analysis Practical implementation Alhasawi and Jovica (2012) F. B. Alhasawi et al. 2012 Improve the reliability of supply TCPAR Location, size & type Static Model analysis Environmental friendness Pilotto et al. (1997a) L. A. S. Pilotto et al. 1997 Power quality SVC & UPQC Location, size & type Static Model analysis Realistic load models Gibbard and Vowles (2000) M. J. Gibbard et al. 2000 System protection TCSC Location, size & type Static Model analysis Practical implementation Rouco (1997) L. Rouco et al. 1997 Power quality GUPFC Location, size & type Static Model analysis Robustness Gholipour (2005) E. Gholipour et al. 2005 System protection IPFC Location & size Static Model analysis Realistic load models Mehouachi et al. (2019) Ines Mehouachi et al. 2019 System reliability HPFC Location & size Static Model analysis Robustness Gao et al. (1992) B.Gao et al. 1992 Power system stability GIPFC Location & size Static Model analysis Robustness Huang et al. (2000) Z. Huang et al. 2000 Power quality SVC Location, size & type Static Model analysis Practical implementation Carpinelli et al. (2015) G. Carpinelli et al. 2015 Power quality TCSC Location, size & type Static Index method Realistic load models Chang et al. (2009b) B. Chang et al. 2009 Power system reliability STATCOM Location, size & type Static Index method Practical implementation M. M. Farsaang (2004) M. M. Farsang et al. 2004 Active power loss DSTATCOM Location, size & type Static Index method Robustness Okamoto and Kurita (1995) H. Okamoto et al. 1995 System reliability UPFC & UPQC Location, size & type Static Index method Environmental friendness Bazanella and Shilva (2001) A. S. Bazanella et al. 2001 Harmonic reduction SVC & TCSC Location & size Static Index method Hybrid techniques Pilotto et al. (1997b) Pilotto L et al. 1997 Power system control SVC & STATCOM Location & size Static Index method Practical implementation Kalyan Kumar et al. (2007) B. Kalyan Kumar et al. 2007 System protection SVC & UPFC Location & size Static Index method Environmental friendness Limyingcharoen et al. (1998) S. Limyingcharoen et al. 1998 Power system stability TCPAR Location, size & type Static Index method Hybrid techniques Hammad and Al Sadek (1989) A.E. Hammad, et.al 1989 System protection SVC Location, size & type Static Index method Realistic load models Canizare et al. (1996) C. A. Canizares et al. 1996 Voltage collapse TCSC Location, size & type Static Index method Hybrid techniques Gubina and Strmcnik (1995) Gubina F et al. 1995 Voltage collapse GUPFC Location, size & type Static Index method Realistic load models Lof et al. (1993) Lof PA et al. 1993 System stability IPFC Location, size & type Static Index method Environmental friendness Naoto et al. (2003) Naoto Y et al. 2003 Voltage collapse HPFC Location & size Static Index method Hybrid techniques Ye et al. (2005) Yang Ye et al. 2005 System stability GIPFC Location & size Static Controlling method Robustness Zhu et al. (2010) Jizhong Zhuet al. 2010 Power distribution planning SVC Location & size Static Controlling method Realistic load models Wei et al. (2005) Xuan Wei et al. 2005 System protection TCSC & SSSC Location, size & type Static Controlling method Practical implementation Bedoya et al. (2008) D. Bedoya et al. 2008 Power quality STATCOM Location, size & type Static Controlling method Realistic load models (continued on next page) 60 B. Singh and R. Kumar / Energy Reports 6 (2020) 55–79 Table 2 (continued). Ref. No. Author Year Power system performance FACTS controllers used Parameters for optimization Load models Techniques Future work Gabrijel et al. (2002) U. Gabrijel et al. 2002 System stability DSTATCOM Location, size & type Static Controlling method Practical implementation Padhiyaar et al. (1998) K. R. Padiyar et al. 1998 System stability UPFC Location, size & type Static Controlling method Environmental friendness Chen et al. (2003) J. Chen et al. 2003 System stability SVC & TCSC Location, size & type Static Residue analysis Robustness Krishnan et al. (2018) V. V. G. Krishnan et al. 2018 System protection SVC & STATCOM Location & size Residue analysis Robustness Adnan et al. (2018) M. Adnan et al. 2018 Power quality SVC & UPFC Location & size Realistic Residue analysis Realistic load models Monod and Mohan (2010) S. W. Mohod et al. 2010 Active power loss TCPAR & DVR Location & size Static Numerical Optimization Robustness Li and Li (1993) B. H. Lee et al. 1993 Power system stability SVC Location, size & type Static Numerical Optimization Hybrid techniques Li et al. (2012a) Yong L et al. 2012 Power oscillation TCSC Location, size & type Static Eigen value Environmental friendness Pilotto et al. (1997c) L.A.S. Pilotto et al. 1997 Harmonic reduction GUPFC Location, size & type Static Eigen value Environmental friendness Wang et al. (2003) Hai Feng Wang et al. 2003 System control IPFC Location, size & type Static Eigen value Practical implementation Hossain et al. (2012) M. J. Hossain et al. 2012 Power quality HPFC Location, size & type Static Eigen value Environmental friendness Leiu et al. (2000) G. Li et al. 2000 Power system stability GIPFC & UPQC Location & size Static Sensitivity method Realistic load models Hridya et al. (2015b) Hridya. K.R. et al. 2015 System stability SVC & TCSC Location, size & type Static Sensitivity method Environmental friendness Zhao and Jiang (1995) O. Zhao et al. 1995 Power distribution planning SVC & STATCOM Location, size & type Static Sensitivity method Realistic load models Ooi et al. (1997) Ooi B et al. 1997 System stability SVC & UPFC Location, size & type Static Sensitivity method Robustness Crisan (1994) Crisan et al. 1994 Voltage collapse TCPAR Location, size & type Static Sensitivity method Hybrid techniques Cutsem (1995) T. Van Cutsem et al. 1995 Power quality SVC & DVR Location, size & type Static Sensitivity method Environmental friendness Subhanga et al. (2002) K. N. Shubhanga et al. 2002 System stability TCSC Location & size Static Sensitivity method Realistic load models Mehraeen et al. (2010) Mehraeen S et al. 2010 Power quality GUPFC Location & size Static Sensitivity method Hybrid techniques Fang et al. (2009) Fang X et al. 2009 Power quality IPFC Location & size Static Sensitivity method Realistic load models Pal (2002) Pal BC 2002 System stability HPFC Location, size & type Static Sensitivity method Robustness Morioka et al. (1999b) Y. Morioka et al. 1999 System stability GIPFC Location, size & type Static Sensitivity method Environmental friendness Manganuril et al. (2016) Yogasree Manganuril et al. 2016 System stability SVC & SSSC Location, size & type Static Sensitivity method Realistic load models Wei et al. (2004) Xuan Wei et al. 2004 System stability TCSC Location, size & type Static Sensitivity method Environmental friendness Ciofi et al. (2002) Carmine Ciofi et al. 2002 System stability GUPFC & DVR Location, size & type Static Sensitivity method Hybrid techniques Bakar and Desa (2017) Noorsakinah Abu Bakar et al. 2017 System stability IPFC & DVR Location & size Static Sensitivity method Robustness Hamed et al. (2013) Hamed HD et al. 2013 Power quality HPFC Location & size Static Sensitivity method Realistic load models Tiwari and Ajjarapu (2011) S. Greene et al. 1997 Voltage collapse TCSC & SSSC Location & size Static Sensitivity method Environmental friendness Ardhakhani et al. (2016) Mostafa Sahraei Ardakani et al. 2016 Power quality GUPFC & UPQC Location & size Static Sensitivity method Robustness General algorithm: Genetic algorithm is the process of selection that belongs to the larger class of evolutionary algorithms. These techniques are basically used to generate solutions for optimization and search problems depending upon bio inspired operators such as crossover and selection. Genetic algorithms are introduced by John Holland in 1960 based on the concept of Darwin’s theory of evolution. Symbiotic organism search (SOS) algorithm: This algorithm simulates the symbolic interaction strategies adopted by organisms to survive and propagate in the ecosystem. Twenty-six unconstrained mathematical problems and four structural engineering design problems are tested and obtained results compared with other well-known optimization methods. Obtained results confirm the excellent performance of the SOS method in solving various complex numerical problems. Computational simulations: These methods are used for the behavior of a system using a computer to simulate the output of a mathematical model associated with system. Simulation of a system is given by the running of the system’s model. These are realized by the computer programs that can be either small running instantly on small devices or large scale programs that run for hours or days on network based on groups of computers. B. Singh and R. Kumar / Energy Reports 6 (2020) 55–79 61 Table 3 A survey on optimization techniques for enhancement of system performances by different types of FACTS controllers in power systems with static and realistic load models. Ref. No. Author Year Power system performance FACTS controllers used Parameters for optimization Load models Techniques Future work Ara et al. (2012) A. Lashkar Ara et al. 2012 Power loss reduction TCSC Location & size Static Linear programming Environmental friendness Chen et al. (1995) X. Chen et al. 1995 System stability GUPFC Location & size Static Linear programming Environmental friendness Wang and Tan (2002) Y. Wang et al. 2002 System Improvement IPFC Location, size & type Static Linear programming Realistic load models Zhang et al. (2004) Zhang X.-P et al. 2004 System reliability HPFC Location, size & type Static Linear Programming Environmental friendness Panda and Ramnarayan (2006) Sidhartha Panda et al. 2006 Voltage regulation improvement GIPFC Location, size & type Static Linear programming Hybrid techniques Morioka et al. (1999a) Y. Morioka et al. 1999 Power loss reduction SVC & UPQC Location, size & type Static Linear programming Robustness Cai et al. (2005) L.J. Cai et al. 2005 System stability TCSC Location, size & type Static MINLP Environmental friendness Saba et al. (2010) K. Sebaa et al. 2010 Power quality SVC & TCSC Location & size Static MINLP Robustness Nojavan et al. (2018) Morteza Nojavan et al. 2018 Minimization of the total annual energy losses SVC & STATCOM Location & size Static & realistic MINLP Realistic load models Tan and Wang (1997) Yoke Lin Tan et al. 1997 Power system stability SVC & UPFC Location & size Static MINLP Robustness Zarghami et al. (2010) Zarghami M et al. 2010 Power system stability TCPAR Location & size Static MINLP Realistic load models Nikoobakht et al. (2018) Ahmad Nikoobakht et al. 2018 Power system stability SVC & UPDC Location & size Static MINLP Realistic load models Wibowo et al. (2011) Rony Seto Wibowo et al. 2011 Power System Improvement TCSC &UPDC Location & size Static Stochastic Load Flow Practical implementation Rahimzadeh and Bina (2011) Sajad Rahimzadeh et al. 2011 System stability GUPFC Location, size & type Static Stochastic Load Flow Environmental friendness Jadidbonab et al. (2019) M. Jadidbonab et al. 2019 System protection IPFC & SSSC Location, size & type Static Stochastic Load Flow Hybrid techniques Noruzian et al. (1997) M. Noroozian et al. 1997 System protection HPFC Location, size & type Static Stochastic Load Flow Environmental friendness Hamon et al. (2013) Camille Hamon et al. 2013 Power loss reduction GIPFC Location, size & type Static Stochastic Load Flow Practical implementation Tan (1996) Y. L. Tan et al. 1996 Power capacity maximization SVC Location, size & type Static Adaptive control law Environmental friendness Muzzammel et al. (2015) Raheel Muzzammel et al. 2015 System reliability TCSC Location & size Static Adaptive control law Realistic load models Araby et al. (2002) E. E. El-Araby et al. 2002 System stability GUPFC Location & size Static Analytical Approach Practical implementation Orfano Gianni and Bacher (2003) T. Orfano Gianni 2003 Power quality IPFC Location & size Static Analytical Approach Realistic load models Verma and Srivastava (2005) M. K. Verma et al. 2005 System stability HPFC Location & size Static Analytical Approach Practical implementation Huang et al. (2012) Jiansheng Huang et al. 2012 System reliability TCSC Location & size Static Analytical Approach Practical implementation Orfanogianni and Bacher (2003) Tina Orfanogianni et al. 2003 System protection GUPFC Location & size Static Analytical Approach Practical implementation Wen et al. (2004) Wen et al. 2004 Power system stability GIPFC Location, size & type Static Analytical Approach Hybrid techniques Tan and Wang (1998) Y.L. Tan et al. 1998 Power system stability SVC Location, size & type Static Analytical Approach Practical implementation Gitizadeh (2009) M. Gitizadeh et al. 2009 Power quality TCSC Location, size & type Static Analytical Approach Environmental friendness Sode Yome et al. (2006) A. Sode-Yome et al. 2006 System stability GUPFC Location, size & type Static Analytical Approach Hybrid techniques Limyingcaron et al. (1998) S. Limyingcharoen et al. 1998 Power system stability IPFC Location, size & type Static Analytical Approach Practical implementation (continued on next page) Tabu search based method: Tabu search method proposed by Fred W. Glover in 1986 and formalized in 1989. These searches have a potential to solve a problem and check its immediate neighbors for finding an improved solution. These search methods have a tendency to become stuck in sub optimal regions. Tabu search method increases the performance of local search by 62 B. Singh and R. Kumar / Energy Reports 6 (2020) 55–79 Table 3 (continued). Ref. No. Author Year Power system performance FACTS controllers used Parameters for optimization Load models Techniques Future work Gao et al. (2017) Fei Gao et al. 2017 Power system stability HPFC Location & size Static Analytical Approach Hybrid techniques Khalesi et al. (2011) Khalesi N et al. 2011 Power system reliability TCSC Location & size Static Dynamic Programming Realistic load models Kumkhratuge et al. (2003) P. Kumkratug et al. 2003 Power quality GUPFC Location & size Static Dynamic Programming Practical implementation Gan et al. (2000) De. Gan et al. 2000 System stability GIPFC Location & size Static OPF method Environmental friendness Kamarposhti et al. (2008) Kamarposhti et al. 2008 System stability SVC Location & size Static OPF method Practical implementation Acchha et al. (2000) E. Acha et al. 2000 Power System protection DSTATCOM Location & size Static OPF method Environmental friendness Padhy and Abdel (2004) Padhy NP et al. 2004 Power system stability GUPFC Location, size & type Static OPF method Environmental friendness Zhang et al. (2001) Zhang XP et al. 2001 Power system reliability IPFC Location, size & type Static OPF method Hybrid techniques Lie and Deng (1997a) T.T. Lie et al. 1997 Power quality HPFC & UPFC Location, size & type Static OPF method Hybrid techniques Xhiaon et al. (2002) Y. Xia et al. 2002 Power quality TCSC & DVR Location, size & type Static OPF method Realistic load models Choudari et al. (2004) B. Chaudhari et al. 2004 Power quality GUPFC Location, size & type Static OPF method Realistic load models Perez et al. (2006) Ambriz-Perez et al. 2006 Power quality GIPFC Location & size Static OPF method Realistic load models Kamel et al. (2015) Kamel et al. 2015 System stability SVC & SSSC Location & size Static OPF method Realistic load models Carpinelli et al. (2006) Carpinelli et al. 2006 Power system stability DSTATCOM Location & size Static OPF method Environmental friendness Ajjharapu and Crishty (1992) V. Ajjarapu et al. 1992 Power quality GUPFC Location & size Static OPF method Robustness Hu et al. (2016) F. Hu et al. 2016 System stability IPFC Location & size Static OPF method Realistic load models Goliphour and Shadate (2005) E. Gholipour et al. 2005 System stability HPFC Location & size Static OPF method Environmental friendness Noruzhian et al. (1997) M. Noroozian et al. 1997 Power quality TCSC Location, size & type Static OPF method Realistic load models Vahhedhi et al. (1999) E. Vaahedi et al. 1999 System stability GUPFC Location, size & type Static OPF method Robustness Gharbex et al. (2001) Stephane Gerbex et al. 2001 Power system stability SVC & SSSC Location, size & type Static Mixed-Integer Optimization Practical implementation Zharghami et al. (2010) Mahyar Zarghami et al. 2010 System stability DSTATCOM Location, size & type Static Mixed-Integer Optimization Robustness Ghaliana et al. (1996) F.D. Galiana et al. 1996 Power loss reduction GUPFC Location, size & type Static Mixed-Integer Optimization Hybrid techniques Sinh et al. (2006) J.G. Singh et al. 2006 Power loss reduction IPFC Location & size Static Mixed-Integer Optimization Robustness W. Zang and Tholbhert (2007) W. Zhang et al. 2007 System protection HPFC Location & size Static Mixed-Integer Optimization Environmental friendness Lie and Deng (1997b) Tjing T. Lie et al. 1997 Power quality TCSC Location & size Static Mixed-Integer Optimization Environmental friendness Araby et al. (2002) E. E. El-Araby et al. 2002 Power loss reduction GUPFC Location & size Static Mixed-Integer Optimization Realistic load models Urdhanata et al. (1991) A.J. Urdanata et al. 1991 Power system reliability TCSC Location & size Static Mixed-Integer Optimization Robustness Zou et al. (1999) X. Zhou et al. 1999 Power system stability GUPFC Location & size Static Mixed-Integer Optimization Robustness Kumar and Dhave (1995) N. Kumar et al. 1995 power loss reduction SVC & SSSC Location, size & type Static Mixed-Integer Optimization Robustness Tahbhoub et al. (2018) Ahmad M. Tahboub et al. 2018 System stability DSTATCOM Location, size & type Static Mixed-Integer Optimization Realistic load models Zhau (1993) E.Z. Zhou et al. 1993 System stability GUPFC & STATCOM Location, size & type Static Mixed-Integer Optimization Practical implementation Ajjarapu and Chrithy (1992) V. Ajjarapu et al. 1992 Power system stability IPFC Location, size & type Static Mixed-Integer Optimization Hybrid techniques Ghyugi et al. (1999) L. Gyugyi et al. 1999 System protection HPFC Location, size & type Static Mixed-Integer Optimization Practical implementation (continued on next page) relaxing its basic rule. Firstly, at each step worsening moves can be accepted if no improving move is available. Monte carlo simulation method: Monte Carlo is a method used to understand the impact of risk and uncertainty in financial, project management, cost, and other forecasting models. When there are different ranges of values as a result, you are beginning to understand the risk and uncertainty in the model. Monte Carlo simulation is told you based on how you create the ranges of estimates how likely the resulting outcomes are. Monte Carlo B. Singh and R. Kumar / Energy Reports 6 (2020) 55–79 69 Table 6 (continued). Ref. No. Author Year Power system performance FACTS controllers used Parameters for optimization Load models Techniques Future work Hua et al. (2019) Pengfei Hua et al. 2019 Power quality GUPFC Location, size & type Static Hybrid technique Hybrid techniques Sahu et al. (2017) Preeti Ranjan Sahu et al. 2017 Power system stability GIPFC Location, size, type & coordination Static Whale optimization algorithm Practical implementation Ladhumaor et al. (2017a) Dilip P. Ladumor et al. 2017 Power quality DSTACOM Location, size & type Static Gray Wolf Optimizer Environmental friendness Ladhumaor et al. (2017b) Dilip P Ladumor et al. 2017 System stability HPFC Location, size & type Static Meta-heuristic algorithm Hybrid techniques Wildheunhues et al. (2015) Sebastian Wildenhues et al. 2015 Power quality DVR Location, size & type Static Meta-heuristic algorithm Robustness Kuyu and Vatansever Yigit Cagatay Kuyu et al. 2018 Controlling Of Power Loss TCSC Location, size, type & coordination Static Salp Swarm Optimizer Practical implementation Hamour et al. Hanan Hamour et al. 2018 Controlling Of Power Loss TCPAR Location, size & type Static Grasshopper Optimization Algorithm Environmental friendness George et al. Thomas George et al. 2018 Controlling Of Power Loss GUPFC Location, size & type Static Ant Lion Optimization Technique Hybrid techniques Nayak et al. Niranjan Nayak et al. 2016 System stability GIPFC Location, size & type Static Spider Monkey Optimization Hybrid techniques Hazrati et al. Garima Hazrati et al. 2016 Power quality UPQC Location, size & type Static Spider Monkey Optimization Robustness Kaur et al. Avinash Kaur et al. 2017 Power quality DVR Location & type Static Spider Monkey Optimization Environmental friendness Behera et al. (2018) Tanmaya Kumar Behera et al. 2018 Controlling of Power Loss TCSC Location, size & type Static Spider Monkey Optimization Hybrid techniques or limited computation capacity. Meta-heuristics sample is a set of solutions which is too large for completely sampled. Metaheuristics may make few assumptions about the optimization problem being solved, and so they may be usable for many problems. Salp Swarm Optimizer: Salp Swarm Algorithm and Multi objective Salp Swarm Algorithm are used for solving optimization problems with single and multiple objectives. The main inspiration of Salp Swarm Algorithm and Multi objective Salp Swarm Algorithm is the swarming behavior of salps when navigating and foraging in oceans. Salp Swarm Algorithm and Multi objective Salp Swarm Algorithm are tested on some mathematical optimization functions to observe and confirm their effective behaviors in finding the optimal solutions for optimization problems. The result shows that the Salp Swarm Algorithm is able to improve the initial random solutions effectively and converge towards the optimum. The results of Multi objective Salp Swarm Algorithm show that this algorithm can approximate optimal solutions with high convergence and coverage. Grasshopper Optimization Algorithm: Grasshopper is insects. They are considered a pest due to their damage to crop production and agriculture. So grasshoppers are rarely seen individually in nature and they join in one of the largest swarm of all creatures. The swarm size may be of continental scale and a nightmare for farmers. The main objective of the grasshopper swarm is that the swarming behavior is found in both nymph and adulthood. Millions of nymph grasshoppers jump and move like rolling cylinders. In their path, they eat almost all vegetation. After this behavior, when they become adult, they form a swarm in the air. This is how grasshoppers migrate over large distances. In contrast, long-range and abrupt movement is the essential feature of the swarm in adulthood. Ant Lion Optimization Technique: The Ant Lion Optimization (ALO) algorithm mimics the hunting mechanism of ant lions in nature. The proposed algorithm is described in three phases. First one a set of 19 mathematical functions is employed to test different characteristics of ALO. Another one are three classical engineering problems are solved by ALO. And last, the shapes of two ship propellers are optimized by ALO as challenging real problems. So after the calculations test functions prove that the proposed algorithm is able to provide very accurate results in terms of improved exploration, exploitation, and convergence. The ALO algorithm also finds superior optimal designs for the majority of classical engineering problems, showing that this algorithm has merits in solving constrained problems with diverse search spaces. Spider monkey optimization: In this technique, a new approach for optimization is proposed by modeling the social behavior of spider monkeys. Spider monkeys have been categorized as fission–fusion social structure based animals. The animals which follow fission–fusion social systems, initially work in a large group and based on need after some time, they divide themselves in smaller groups led by an adult female for foraging. Therefore, the proposed strategy broadly classified as inspiration from the intelligent foraging behavior of fission–fusion social structure based animals. 3. Summary of paper The summary of this article from conventional techniques, optimization techniques, artificial computational intelligence, hybrid and current techniques for enhancement of system performances by different FACTS controllers in power systems with static and realistic load models viewpoints are discussed in Sections 3.1–3.5, subsequently and comparison between these techniques are also discussed in Section 3.6, subsequently. 3.1. Conventional techniques Table 7 and Fig. 1, shows the summary of this article from conventional optimization techniques for enhancement of system performances by different FACTS controllers in power systems with static and realistic load models viewpoints. 70 B. Singh and R. Kumar / Energy Reports 6 (2020) 55–79 Fig. 1. % Literature reviewed from conventional optimization techniques viewpoints. Table 7 Conventional optimization techniques. Conventional techniques Literatures reviewed % Literatures reviewed Model analysis (MA) 16 25.81 Index method (IM) 13 20.97 Controlling method (CM) 6 9.68 Residue analysis (RA) 3 4.84 Numerical Optimization (NO) 2 3.23 Eigen value (EV) 4 6.45 Sensitivity Method (SM) 18 29.03 Table 8 Optimization techniques. Optimization techniques Literatures reviewed % Literatures reviewed Linear programming (LP) 6 7.60 Mixed-Integer Nonlinear Programming (MINLP) 6 7.60 Stochastic Load Flow (SLF) 5 6.33 Adaptive control law (ACL) 2 2.53 Analytical Approach (AA) 11 13.92 Dynamic Programming (DP) 2 2.53 OPF method 16 20.25 Modified Mixed-Integer Non-Linear Programming (MMINLP) 31 39.24 Fig. 1, it is concluded that the MA, IM, CM, RA, NO, EV and SM are 25.81%, 20.97%, 9.68%, 4.84%, 3.23%, 6.45% and 29.03% literatures reviewed from conventional optimization techniques viewpoints, respectively. 3.2. Optimization techniques Table 8 and Fig. 2, shows the summary of this article from optimization techniques for enhancement of system performances by different FACTS controllers in power systems with static and realistic load models viewpoint. Fig. 2, it is concluded that the LP, MINLP, SLF, ACL, AA, DP, OPF and MMINLP are 7.60%, 7.60%, 6.33%, 2.53%, 20.25% and 39.24% literatures are reviewed from optimization techniques viewpoint, respectively. Table 9 Artificial intelligence computational techniques. AI techniques Literatures reviewed % Literatures reviewed GA 15 29.41 Symbiotic organism search (SOS) algorithm 4 7.84 Computational Simulations (CS) 4 7.84 Tabu search based method (TS) 1 1.96 Monte Carlo Simulation (MCS) 2 3.92 Intelligent Programming (IP) 2 3.92 Artificial bee colony algorithm (ABC) 2 3.92 ANN method 7 13.73 Fuzzy linear programming (FZ) 6 11.77 PSO 8 15.69 3.3. Artificial intelligence computational techniques Table 9 and Fig. 3, shows the summary of this article from artificial intelligence computational techniques for enhancement of system performances by different FACTS controllers in power systems with static and realistic load models viewpoint. Fig. 3, it is concluded that the GA, SOS, CS, TS, MCS, IP, ABC, ANN, FZ, LP and PSO method are 29.41%,7.84%, 7.84%, 1.96%, 3.92%,3.92%, 3.92%, 13.73%, 11.77% and 15.69% literatures are reviewed from AI techniques viewpoint, respectively. 3.4. Hybrid techniques Table 10 and Fig. 4, shows the summary of this article from hybrid techniques for enhancement of system performances by different FACTS controllers in power systems with static and realistic load models viewpoint. Fig. 4, it is concluded that the hybrid of ABC and ANN, hybrid of GA and OPF, hybrid of OPF and SA, hybrid of GA and FZ, hybrid of SA and EP, hybrid of PSO And MINLP, hybrid of SA and MILP, hybrid of OPF and PSO, hybrid of ABC and OPF, hybrid of Lyapunov Theory and Fuzzy Logic and hybrid of OPF and Fuzzy Logic are 4.17%, 45.83%, 8.33%, 8.33%, 4.17%, 4.17%, 4.17%, 4.17%, 8.33%, 4.17% and 8.33% literatures are reviewed from hybrid technique viewpoint, respectively. B. Singh and R. Kumar / Energy Reports 6 (2020) 55–79 71 Fig. 2. Optimization techniques. Fig. 3. Artificial intelligence computational techniques. 3.5. Current techniques Table 11 and Fig. 5, shows the summary of this article from current techniques for enhancement of system performances by different FACTS controllers in power systems with static and realistic load models viewpoint. Fig. 5, it is concluded that the EA, ACT, PM, HT, PP, GS, DC, WOA, GWO, MHA, SSO, GOA, ALO and SMO are 22.97%, 28.38%, 12.16%, 9.46%, 5.41%, 4.05%, 2.70%, 1.35%, 1.35%, 2.70%, 1.35%, 1.35%, 1.35% and 5.41% literatures are reviewed from current techniques viewpoint respectively. 3.6. Comparisons of all optimization techniques Fig. 6, show the summary of this article from conventional, optimization, artificial intelligence, hybrid and current techniques for enhancement of system performances by different FACTS controllers in power systems with static and realistic load models, respectively (see Table 12). Finally, it is concluded that, presently researchers and practitioners are used hybrid techniques (GA+PSO; GA+ANN+FZ; 72 B. Singh and R. Kumar / Energy Reports 6 (2020) 55–79 Fig. 4. Hybrid techniques. Fig. 5. Others techniques (Current techniques). B. Singh and R. Kumar / Energy Reports 6 (2020) 55–79 73 Fig. 6. Comparisons of all optimization techniques. Table 10 Hybrid techniques. Hybrid techniques Literatures reviewed % Literatures reviewed ABC and ANN 1 4.17 GA and OPF 11 45.83 OPF and SA 2 8.33 GA and FZ 2 8.33 SA and EP 1 4.17 PSO And MINLP 1 4.17 SA and MILP 1 4.17 OPF and PSO 2 8.33 ABC and OPF 1 4.17 OPF and FZ 2 8.33 Table 11 Others techniques (current techniques) Others techniques Literatures reviewed % Literatures reviewed Energy approach (EA) 17 22.97 Active Control Technique (ACT) 21 28.38 Passivity method (PM) 9 12.16 Hybrid technique (HT) 7 9.46 Pole placement techniques (PP) 4 5.41 Graph search algorithm (GS) 3 4.05 Decomposition coordination (DC) 2 2.70 Whale optimization algorithm (WO) 1 1.35 Gray Wolf Optimizer (GW) 1 1.35 Meta-heuristic algorithm (MH) 2 2.70 Salp Swarm Optimizer (SS) 1 1.35 Grasshopper Optimization Algorithm (GO) 1 1.35 Ant Lion Optimization Technique (AL) 1 1.35 Spider Monkey Optimization (SMO) 4 5.41 Table 12 Comparisons of all optimization techniques. Techniques Literatures reviewed % Literatures reviewed Conventional 62 21.38 Optimization 79 27.24 Artificial Intelligence 51 17.59 Hybrid 24 28.62 Current 74 25.52 PSO+ANN; GA+MCS; GA+ABC; TS+MCS etc.) and current techniques (Grasshopper optimization techniques, Whale optimization techniques, Ant lion optimization techniques, Elephant heard optimization techniques, Gray wolf optimization techniques, Gray swarm optimization techniques etc.) for enhancement of system performances. 4. Conclusions and future scopes of the survey article The conclusions and future scopes of taxonomical survey article are presented in Sections 4.1–4.2, subsequently. 4.1. Conclusions The following conclusions are made from this survey article as follows:- •The enhancement of system performances such as loadability, real and reactive power losses, voltage profiles, power system stability, bandwidth of operations, flexibility of operations, available power transfer capacity, bandwidth of operations, power system oscillations, system power factors etc. by FACTS controllers in power systems with static and realistic load models. •The power system reliability and security also enhanced by different types of FACTS controllers in power systems with static and realistic load models. •The cost of electricity also reduced by different types of FACTS controllers in power systems. 4.2. Future scopes of survey article The following future scopes of paper are as follows:- •The practical systems are implemented with different types of FACTS controllers in multi-machines power systems with static and realistic load models. •Multi-task objective also achieved by different types of FACTS controllers in multi-machines power systems with static and realistic load models. •Environmental friendness achieved by different types of FACTS Controllers in multi-machines power systems with static and realistic load models. References Abdel-Magid, Y.L., Abido, M.A., Al-Baiyat, S., Mantawy, A.H., 1999. Simultaneous stabilization of multi machine power systems via genetic algorithms. IEEE Trans. Power Syst. 14 (4), 1428–1439. 74 B. Singh and R. Kumar / Energy Reports 6 (2020) 55–79 Abdel-Magid, Y.L., Abido, M.A., Mantawy, A.H., 2000. Mantawy AH robust tuning of power system stabilizers in multi machine power systems. IEEE Trans. Power Syst. 15 (2), 735–740. Abhou Elaa, A., et al., 2010. Maximal optimal benefits of distributed generation using genetic algorithms. Electr. Power Syst. Res. 80, 869–877. Abido, M.A., 2002. Optimal design of power system stabilizers using PSO. IEEE Trans. Energy Convers. 17 (3), 406–413. Accha, E., et al., 2004. FACTS Modelling and Simulation in Power Network. John Wiley & Sons Ltd. Acchha, E., et al., 2000. Advanced SVC model for Newton–raphson load flow and Newton optimal power flow studies. IEEE Trans. Power Syst. 15 (1), 129–136. Adnan, M., Tariq, M., Zhou, Z., Vincent Poor, H., 2018. Load flow balancing and transient stability analysis in renewable integrated power grids. Electr. Power Energy Syst. 104, 744–771. Aghaei, J., Zarei, M., Asban, M., Ghavidel, S., Heidari, A., Agelidis, V.G., 2016. Determining potential stability enhancements of flexible ac transmission system devices using corrected transient energy function. IET Gen. Trans. Distrib. 10 (2), 470–476. Ahsaee, M.G., Sadeh, J., 2011. A novel fault-location algorithm for long transmission lines compensated by series FACTS devices. IEEE Trans. Power Del. 26 (4), 2299–2308. Ajjarapu, V., Chrithy, C., 1992. The continuation power flow: A tool for steady state voltage stability analysis. IEEE Trans. Power Syst. 7 (1), 416–423. Ajjharapu, V., Crishty, C., 1992. The continuation power flow: A tool for steady state voltage stability analysis. IEEE Trans. Power Syst. 7 (1), 416–423. Alamhelu, S., et al., 2008. Novel optimal placement UPFC based on sensitivity analysis and evolutionary programming. J. Eng. Appl. Sci. 3 (1), 59–63. Alhasawi, Faisal B., Jovica, V.M., 2012. Techno-economic contribution of FACTS devices to the operation of power systems with high level of wind power integration. IEEE Trans. Power Syst. 27 (3), 1414–1421. Angqhuist, L., et al., 1993. Power oscillation damping using controlled reactive power compensation – A comparison between series and shunt approaches. IEEE Trans. Power Syst. 8 (2), 687–695. Ara, A.L., Kazemi, A., Niaki, S.A., 2012. Multi objective optimal location of FACTS shunt-series controllers for power system operation planning. IEEE Trans. Power Deliv. 27 (2), 481–490. Araby, E.E.E., Yorino, N., Saraki, H., 2002. A comprehensive approach of FACTS devices optimal allocation to mitigate voltage collapse In: Proc2002, IEEE trans. and distrib. conf Exhibits; 33 62–67. Ardhakhani, M.S., et al., 2016. Transfer capability improvement through marketbased operation of series FACTS devices. IEEE Trans. Power Syst. 31 (5), 3702–3714. Azhbe, V., Ghabrijel, U., Povh, D., Milhalic, R., 2005. The energy function of a general multi machine system with a unified power flow controller. IEEE Trans. Power Syst. 20 (3), 1478–1485. Baek, Seung Mook, Nam, Suchul, Song, Jiyoung, Lee, Jaegul, Kim, Taekyun, Shin, Jeonghoon, 2013. Design of advanced voltage management system including manual operation mode via real-time digital simulator. IEEE Trans. Ind. Appl. 49 (4), 1817–1826. Bakar, Noorsakinah Abu, Desa, Mohd Khairunaz Mat, 2017. Optimal Placement of TCSC in Transmission Network using Sensitivity Based Method for Multi objective Optimization IEEE Conf. on Energy Conv. (CENCON) 321-329. Bakhirtzis, A.G., Biskhas, P.N., Zhouma, C.E., Pethridish, V., 2002. Optimal power flow by enhanced genetic algorithm. IEEE Trans. Power Syst. 17 (2), 229–236. Bazanella, A.S., Shilva, A.S., 2001. Coordinated design of damping controllers for robustness of power systems stability. Int. J. Electr. Power Energy Syst. 23 (1), 69–79. Bedoya, D., Castro, C., de Silva, L., 2008. A method for computing minimum voltage stability margins of power systems. IET Gen. Trans. Distrib. 2 (5), 676–689. Behera, Tanmaya Kumar, Behera, Manoja Kumar, Nayak, Niranjan, 2018. Spider Monkey Based Improve P & O MPPT Controller for Photovoltaic Generation System, In: Int. Conf. on Tech. for Smart-City Energy Security and Power (ICSESP-2018). Benabidh, R., Badhour, M., Abidho, M.A., 2009. Optimal location and setting of SVC and TCSC devices using non-dominated sorting particle swarm optimization. Elect. Power Sys. Res. 79 (12), 1668–1677. Blanco, G., Olsina, F., Garces, F., Rehtanz, C., 2011. Real option valuation of FACTS investments based on the least square Monte Carlo method. IEEE Trans. Power Syst. 26 (3), 1389–1398. Cai, L.J., Erlich, I., 2006. Fuzzy coordination of FACTS controllers for damping power oscillations. IEEE Trans. Power Syst. 23–30. Cai, L.J., Erlich, I., Stamtsis, G., 2005. Optimal Choice and Allocation of FACTS Devices in Deregulated Electricity Market Using Genetic Algorithms In Proc., IEEE Power Engg. Soc., General Meeting, 201-207. Canizare, C.A., et al., 1996. Comparison of performance indices for detection of proximity of voltage collapse. IEEE Trans. Power Syst. 11 (3), 1441–1450. Canizares, C.A., et al., 1999. Analysis of SVC and TCSC controllers in voltage collapse. IEEE Tran. Power Syst. 14 (1), 158–165. Carpinelli, G., Caramia, Pierluigi, Russo, Angela, Varilone, Pietro, 2015. Voltage stability in unbalanced power systems: new complementarity constraints based approach. IET Gener. Trans. Distrib. 9 (14), 2014–2023. Carpinelli, G., Lauria, D., Varilone, P., 2006. Voltage stability analysis in unbalanced power systems by optimal power flow. IEE Proc. Gen. Trans. Distrib. 153 (3), 261–268. Chandra sekaran, K., Arul, K.J.A., Sahayasen thamil, L., Saravanan, M., 2009. A new method to incorporate FACTS devices in optimal power flow using particle swarm optimization. J. Theor. Appl. Inf. Technol. 14, 1–34. Chandrasekaran, K., Arul Jey Araj, K., Sahayasenthamil, L., Saravanan, M., 2005- 2009. A new method to incorporate FACTS devices in optimal power flow using particle swarm optimization. J. Theor. Appl. Inf. Technol. 67–74. Chang, C.S., Huang, J.S., 1997a. Optimal SVC placement for voltage stability reinforcements. Electr. Power Syst. Res. 45 (3), 165–172. Chang, C.S., Huang, J.S., 1997b. Optimal SVC placement for voltage stability reinforcements. Electr. Power Syst. Res. 42, 165–172. Chang, B., Lee, B., Chow, J.H., 2009a. A novel operation strategy for shunt-type FACTS controllers in the KEPCO system. IEEE Trans. Power Syst. 24 (3), 1639–1640. Chang, B., Lee, Byongjun, Joe, H.C., 2009b. A novel operation strategies for shunttype FACTS controllers in the KEPCO system. IEEE Trans. Power Syst. 24 (3), 1639–1640. Chatterji, D., et al., 2007. Transient stability assessment of power systems containing series and shunt compensators. IEEE Tran. Power Sys. 22 (3), 1210–1220. Chen, J., Lie, T.T., Vilathgamuwa, D.M., 2003. Enhancement of power system damping using VSC-based seriesconnected FACTS controller. IEEE Proc.-Gen. Trans. Distrib. 150 (3), 353–359. Chen, X., Pahalawaththa, N., Annakkage, U., Kumble, C., 1995. Controlled series compensation for improving the stability of multi machine power systems. IEEE Proc. Pt. C 142, 361–366. Choudari, B., et al., 2004. Robust damping of multiple swing modes employing global stabilizing signals with a TCSC. IEEE Trans. Power Syst. 19 (1), 499–506. Chung, T.S., Li, Y.Z., 2001. A hybrid GA approaches for OPF with consideration of FACTS devices. IEEE Power Eng. Rev. 21 (2), 47–50. Chung, C.Y., Wang, K.W., Tse, C.T., Bian, X.Y., David, A.K., 2003. Probabilistic eigen value sensitivity analysis and PSS design in multi-machine power systems. IEEE Trans. Power Syst. 18 (4), 1439–1445. Ciofi, Carmine, Crupi, Felice, Pace, Calogero, 2002. A new method for high-sensitivity noise measurements. IEEE Trans. Instrum. Meas. 51 (4), 656–659. Conejo, A.J., Milano, F., Garcia-Bertrand, R., 2006. Congestion management ensuring voltage stability. IEEE Trans. Power Syst. 21 (1), 357–364. Crisan, Liu M., 1994. Voltage collapse prediction using an improved sensitivity approach. Electr. Power Syst. Res. 28 (3), 181–190. Cutsem, T.V., 1995. An approach to corrective control of voltage instability using simulation and sensitivity. IEEE Trans. Power Syst. 10 (2), 616–622. Daas, P.K., et al., 2000. Damping multimodal power system oscillation using hybrid fuzzy controller for series connected FACTS devices. IEEE Trans. Power Syst. 15 (4), 1360–1366. Daskh, P.K.S.M., Paanda, G., 2000. A radial basis function neural network controller for UPFC. IEEE Trans. Power Sys. 15 (4), 1293–1299. Deng, Zhuoming, Liu, Mingbo, Ouyang, Yifeng, Lin, Shunjiang, Xie, Min, 2018. Multi-objective mixed-integer dynamic optimization method applied to optimal allocation of dynamic VAr sources of power systems. IEEE Trans. Power Syst. 33 (2), 1683–1697. Dhas, P.K., et al., 2000. A radial basis function neural network controller for UPFC. IEEE Trans. Power Syst. 15 (4), 1293–1299. Dhong, Z., Wang, Y., Hill, D.J., Makharov, Y.V., 0000. A New Approach to Power Systems VAr Planning Aimed at Voltage Stability Enhancement with Feedback Control, In: Proc. 1999, Electric Power Engineering Power Tech. Budapest ’99, pp. 33-39. Dillon, T.S., 1991. Artificial neural network applications to power systems and their relationship to symbolic methods. Electr. Power Energy Syst. 13 (2), 66–72. Edris, A., 2000. FACTS technology development: an update. IEEE Power Eng. Rev. 20 (3), 4–9. Etingov, Pavel, Oudalov, Alexandre, Voropai, Nikolai, Cherbaoui, Rachid, Germond, Alain, 2007. PSS Enhancement Using ANN Based Coordinated Emergency Control System, In: Proc. of the IEEE Power Tech. 2007, Lausanne, pp. 232-237. Faaershangi, M., et al., 2007. Placement of SVCs and selection of stabilizing signals in power systems. IEEE Trans. Power Syst. 22 (3), 1061–1071. Fang, X., Chow, J.H., Xia, J., Fardanesh, B., 2009. Sensitivity methods in the dispatch and siting of FACTS controllers. IEEE Trans. Power Syst. 24 (2), 713–720. Farsangi, M.M., Pour, N.H., Song, Y.H., Lee, K.Y., 2007. Placement of SVCs and selection of stabilizing signals in power systems. IEEE Trans. Power Syst. 22 (3), 1061–1071. B. Singh and R. Kumar / Energy Reports 6 (2020) 55–79 75 Feurthe Eshquivel, C.R., Accha, E., 1997. A Newton-type algorithm for the control of power flow in electrical power networks. IEEE Trans. Power Sys. 12, 1474–1481. Gabrijel, U., et al., 2002. Direct methods for transient stability assessment in power systems comprising controllable series devices. IEEE Trans. Power Syst. 17 (4), 1116–1122. Gan, De, Thomas, R.J., Zimmerman, R.D., 2000. Stability-constrained optimal power flow. IEEE Trans. Power Syst. 15 (2). Gao, Fei, Bozhko, Serhiy, Costabeber, Alessandro, Patel, Chintan, Wheeler, Pat, Hill, Christopher, Asher, Greg, 2017. Comparative stability analysis of droop control approaches in voltage source converters-based DC micro grids. IEEE Trans. Power Electron. 32 (3), 2395–2415. Gao, B., Morison, G.K., Kund, P., 1992. Voltage stability evaluation using modal analysis. IEEE Trans. Power Syst. 7 (4), 1529–1542. George, Thomas, Youssef, Abdel Raheem, Ebeed, Mohamed, Kamel, Salah, 2018. Ant Lion Optimization Technique for Optimal Capacitor Placement Based on Total Cost and Power Loss Minimization, In: Int. Conf. on Innovative Trends in Computer Engg. (ITCE), pp. 350-356. Gerbex, S., Charkaui, R., Garmand, A.J., 2001. Optimal location of multi-type FACTS devices in a power system by means of genetic algorithms. IEEE Trans. Power Syst. 16 (3), 537–544. Gerbhex, S., Cherkhaoui, R., Garamond, A.J., 2001. Optimal location of multi-type FACTS devices in power system by means of genetic algorithm. IEEE Trans. Power Syst. 16 (3), 537–544. Ghaliana, F.D., Almeidha, K., Toussaint, M., Griphin, J., Atanackovic, D., Ooi, B.T., MachGillis, D.T., 1996. Assessment and control of the impact of FACTS devices on power system performance. IEEE Trans. Power Syst. 11 (4), 1931–1936. Gharbex, Steephane, Chherkaou, Racid, Gormond, Alhein J., 2001. Optimal location of multi-type FACTS devices in a power system by means of genetic algorithms. IEEE Trans. Power Syst. 16 (3), 537–544. Gholipour, E.S.S., 2005. Improving of transient stability of power systems using UPFC. IEEE Trans. Power Deliv. 20 (2), 1677–1682. Ghyugi, L., Shen, K.K., Schaudher, C.D., 1999. The interline power flow controller concept: a new approach to power flow management in transmission systems. IEEE Trans. Power Deliv. 14 (3), 1115–1123. Ghyugyi, L., et al., 1999. The interline power flow controller concept: a new approach to power flow management in transmission line system. IEEE Trans. Power Deliv. 14 (3), 1115–1123. Gibbard, M.J., Vowles, D.J.P.P., 2000. Interactions between and effectiveness of power systems stabilizers and FACTS controllers in multi-machine systems. IEEE Trans. Power Syst. 15 (2), 748–755. Gitizadeh, M.M.K., 2009. A novel approach for optimum allocation of FACTS devices using multi-objective function. Energy Convers. Manage. 50 (3), 682–690. Goksu, Omer, Teodorescu, Remus, Bak, Claus Leth, Iov, Florin, Kjaer, Philip Carne, 2014. Instability of wind turbine converters during current injection to low voltage grid faults and PLL frequency based stability solution. IEEE Trans. Power Syst. 29 (4), 1683–1691. Goliphour, E., Shadate, S., 2005. Improving transient stability of power systems using UPFC. IEEE Trans. Power Deliv. 20 (2), 1677–1682. Gonzhalez, J., et al., 2010. Stability modelling and comparative study of series vectorial compensators. IEEE Trans. Power Del. 25 (2), 1093–1103. Gubina, F., Strmcnik, B., 1995. Voltage collapse proximity index determination using voltage phasor approach. IEEE Trans. Power Syst. 10 (2), 788–793. Gui, Yonghao, Kim, Wonhee, Chung, Chung Choo, 2016. Passivity-based control with nonlinear damping for type 2 STATCOM systems. IEEE Trans. Power Syst. 31 (4), 2824–2833. Guo, Y., Gao, H., Wu, Qiuwei, Ostergaard, Jacob, Dachuan, Shahidehpour, M., 2018a. Distributed coordinated active and reactive power control of wind farms based on model predictive control. Electr. Power Energy Syst. 104, 78–88. Guo, C., Jiang, Wen, Zhao, Chengyong, 2018b. Small-signal instability and supplementary coordinated damping-control of LCC-HVDC system with STATCOM under weak AC grid conditions. Electr. Power Energy Syst. 104, 246–254. Hajagos, L., Danai, B., 1998. Laboratory measurements and models of modern loads and their effect on voltage stability studies. IEEE Trans. Power Syst. 13 (2), 584–592. Hamed, H.D., Masoud, M.S., Hossein, A.A., Gevork, G., Mehdi, G., 2013. A new method based on sensitivity analysis to optimize the placement of SSSCs. Turk. J. Electr. Eng. Comput. Sci. 21, 1956–1971. Hammad, A.E., Al Sadek, M.Z., 1989. Prevention of transient voltage instabilities due to induction motor loads by static var compensators. IEEE Power Eng. Rev. 9 (8). Hamon, Camille, Perninge, Magnus, Soder, Lennart, 2013. A stochastic optimal power flow problem with stability constraints, Part I: Approximating the stability boundary. IEEE Trans. Power Syst. 28 (2), 1839–1848. Hamour, Hanan, Kamel, Salah, mawgoud, Hussein Abdel, Korashy, Ahmed, Jurado, Francisco, 2018. Distribution Network Reconfiguration Using Grasshopper Optimization Algorithm for Power Loss Minimization, In: Int. Conf. on Smart Energy Systems and Tech. (SEST), pp. 1-5. Hazhra, J., et al., 2007. Congestion management using multi objective particle swarm optimization. IEEE Trans. Power Syst. 22 (4), 1726–1734. Hazrati, Garima, Sharma, Harish, Sharma, Nirmala, Adaptive Step-size based Spider Monkey Optimization Adaptive Step-size based Spider Monkey Optimization, In: Int. Conf. on Power Electronics, Intelligent Control and Energy Systems (ICPEICES-2016), pp. 1-5. Hoque, M., 2004. Improvement of first swing stability limit by utilizing full benefit of shunt FACTS devices. IEEE Tran. Power Syst. 19 (4), 1894–1902. Hoque, M., 2008. Evaluation of first swing stability of a large power system with various FACTS devices. IEEE Tran. Power Sys. 23 (3), 1144–1151. Hossain, M.J., Pota, Hemanshu R., Apel Mahmud, M.d., Rodrigo, A.R., 2012. Investigation of the impacts of large-scale wind power penetration on the angle and voltage stability of power systems. IEEE Syst. J. 6 (1), 76–84. Hridya, K.R., Mini, V., Visakhan, R., Kurian, Asha Anu, 2015a. Analysis of Voltage Stability Enhancement of a Grid and Loss Reduction Using Series FACTS Controllers, IEEE Int. Conf. on Power, Instru. Control and Computing (PICC), 1-5. Hridya, K.R., Mini, V., Visakhan, R., Kurian, Asha Anu, 2015b. Analysis of Voltage Stability Enhancement of a Grid and Loss Reduction Using Series FACTS Controllers Int. Conf. on Power, Instru. Control and Computing (PICC). Hu, Pengfei, Guerrer, Josep M., He, Zhengxu, 2018. Design and analysis of a transformerless STATCOM based on hybrid cascaded multilevel converter. Electr. Power Energy Syst. 104, 78–88. Hu, Fengkai, Sun, Kai, Rosso, Alberto Del, Farantatos, Evangelos, Bhatt, Navin, 2016. Measurement-based real-time voltage stability monitoring for load areas. IEEE Trans. Power Syst. 31 (4), 2787–2798. Hua, Pengfei, Guerrerob, Josep M., He, Zhengxu, 2019. Design and analysis of a transformer less STATCOM based on hybrid cascaded multilevel converter. Electr. Power Energy Syst. 104, 694–704. Huang, J., Jiang, Z., Negnevitsky, M., 2012. Optimal SVC Placement for Voltage Stability Reinforcement of Power Systems with Most Critical and N-1 Security Criteria, IEEE Energycon. Conf. & Exhib. Huang, Z., Ni, Y., Shen, C.M., Wu, F.F., Shousun, C., Baolin, Z., 2000. Application of unified power flow controller in interconnected power systems modeling, interface, control strategy, and case study. IEEE Trans. Power Syst. 15 (2), 817–824. Ippolito, L., Cortiglia, Antonio La, Petrocelli, Michele, 2006. Optimal allocation of FACTS devices by using multi-objective optimal power flow and genetic algorithms. Int. J. Emerg. Electric. Power Sys. 7 (2). Jadidbonab, M., et al., 2019. Stochastic assessment, enhancement of voltage stability in multi carrier energy systems considering wind power. Int. J. Electr. Power Energy Syst. 106, 572–584. Jeevarathinam, B., 2006. Genetic algorithm and Fuzzy logic based optimal location of FACTS device in a power system network. Int. J. Emerg. Electric. Power Syst. 5 (2). Jiang, Xia, Chow, Joe H., Edris, A.A., Fardanesh, B.E.U., 2010. Transfer path stability enhancement by voltage-sourced converter-based FACTS controllers. IEEE Trans. Power Deliv. 25 (2), 1019–1025. Kalyan Kumar, B., et al., 2007. Placement of FACTS controllers using modal controllability indices to damp out power system oscillations. IET Gen. Trans. Distrib. 1 (2), 209–217. Kamarposhti, M.A., Alinezhad, M., Lesani, H., Talebi, N., 2008. Comparison of SVC, STATCOM, TCSC, and UPFC controllers for static voltage stability evaluated by continuation power flow method, IEEE Canada Electric Power Conference pp. 1-8. Kamel, S., Jurado, F., Lopes, J.P., 2015. Comparison of various UPFC models for power flow control. Int. J. Electr. Power Syst. Res. 121, 243–251. Kanan, S., et al., 2004. Real and reactive power coordination for a UPFC. IEEE Trans. Power Syst. 19 (3). Kannan, S., Mary, R.S.S., Subbaraj, P., Pandhay, N.P., 2004. Application of PSO technique and its variants to generation expansion planning problem. Electric Power Sys. Res. 70, 203–210. Kaur, Avinash, Sharma, Harish, Sharma, Nirmala, Disruption operator based Spider Monkey Optimization Algorithm, In: Int. Conf. on Computing, Communication and Automation (ICCCA2017), pp. 216-225. Kejani, Mohsen Basiri, Gholipour, Eskandar, 2017. Holomorphic embedding loadflow modeling of thyristor-based FACTS controllers. IEEE Trans. Power Syst. 32 (6), 4871–4879. Kesshel, P., Ghlavitsch, H., 1986. Estimating the voltage stability of a power system. IEEE Trans. Power Del. 1 (3), 346–354. Khalesi, N., Rezaei, N., Haghifam, M.R.D.G., 2011. Allocation with application of dynamic programming for loss reduction and reliability improvement. Int. J. Electr. Power Energy Syst. 33 (2), 288–295. Khederzadeh, M., Ghorbani, A., 2012a. Impact of VSC-based multiline facts controllers on distance protection of transmission lines. IEEE Trans. Power Del. 27 (1), 32–39. Khederzadeh, Mojtaba, Ghorbani, Amir, 2012b. Impact of VSC-based multiline FACTS controllers on distance protection of transmission lines. IEEE Trans. Power Del. 27 (1), 32–39. Khederzadeh, M., Sidhu, T.S., 2006. Impact of TCSC on the protection of transmission lines. IEEE Trans. Power Del. 21 (1), 80–87. 76 B. Singh and R. Kumar / Energy Reports 6 (2020) 55–79 Kheib, A., et al., 1995. Application of artificial neural networks in voltage stability assessment. IEEE Trans. Power Sys. 10 (4), 1890–1895. Kimkhratug, P., 2012. Application of lyapunov theory and fuzzy logic to control shunt FACTS devices for enhancing transient stability in multi machine system. J. Electr. Eng. Technol. 7 (5), 672–680. Kinjo, T., Senjyu, T., Urasaki, N.H.F., 2006. Output levelling of renewable energy by electric double-layer capacitor applied for energy storage system. IEEE Trans. Energy Convers. 21 (1). Ko, Chia Nan, Chang, Ying-Pin, Wu, Chia Ju, 2009. A PSO method with nonlinear time-varying evolution for optimal design of harmonic filters. IEEE Trans. Power Syst. 24 (1), 437–444. Kobayashi, K., Goto, M., Wu, K., Yokomizu, Y., Matsumura, T., 2003. PSS improvement by energy storage type STATCOM. IEEE Power Soc. Meet. 0–7803–7967–5. Krishnan, Vignesh Venkata Gopala, Srivastava, Suresh Chandra, Chakrabarti, Saikat, 2018. A robust decentralized wide area damping controller for wind generators and FACTS controllers considering load model uncertainties. IEEE Trans. Smart Grid 9 (1), 360–372. Kumar, N., Dhave, M.P., 1995. Application of static VAr system auxiliary controllers to improve the transient performance of series compensated line transmission lines. Electr. Power Syst. Res. 34, 75–83. Kumar, B.K., Singh, S.N., Srivastava, S.C., 2007a. Placement of FACTS controllers using modal controllability indices to damp out power system oscillations. IET Gen. Trans. Distrib. 1 (2), 209–217. Kumar, B.K., et al., 2007b. Placement of FACTS controller using modal controllability indices to dampout power system oscillations proc. Inst. Eng. Tech. Gen. Trans. Distr. 1 (2), 252–260. Kumkhratuge, P., et al., 2003. Versatile model of a unified power flow controller in a simple power system. Proc. Inst. Electr. Eng. Gen. Trans. Distrib. 150 (2), 155–1161. Kumkrahug, P., et al., 2003. Versatile model of a unified power flow controller in a simple power system. IEEE Proc. Gen. Trans. Distrib. 150 (2), 155–161. Kundhur, P., et al., 2004. Definition and classification of power system stability. IEEE Trans. Power Sys. 19 (2), 1387–1401. Kundur, P., Morison, G., 1997. A review of definitions and classification of stability problems in today’s power systems, In: Proc. IEEE PES Winter Meeting, Panel Session on Stability Terms and Definitions, New York, USA. Kuyu, Yigit Cagatay, Vatansever, Fahri, 2018. Real Loss Minimization in Power Systems via Recent Optimization Techniques, In: Int. Symposium on Multidisciplinary Studies and Innovative Techno. (ISMSIT), pp. 1-4. Ladhumaor, Dilip, et al., 2017. Optimal power flow with Shunt flexible AC transmission system (FACTS) device using Grey Wolf Optimizer, In: Int. Conf. on adv. in Electrical, Electronics, Inf. Comm. and Bio Informatics (AEEICB17), pp. 387-391. Ladhumaor, Dilip P., et al., 2017. Optimal power flow problems solution with SVC using meta-heuristic algorithm, In: Int. Conf. on Advances in Electrical, Electronics, Inf. Comm. and Bio-Inf. (AEEICB), pp. 283-288. Larshen, E.V., Sanchezh Ghasca, J., Chaow, J.H., 1995. Concepts for design of FACTS controllers to damp power swings. IEEE Trans. Power Syst. 10 (2), 948–956. Lashkar, A.A., Kazemi, A., Niaki, S.A.N., 2012. Multi objective optimal location of FACTS shunt-series controllers for power system operation planning. IEEE Trans. Power Deliv. 27 (2), 481–490. Leiu, G., et al., 2000. Design and application of coordinated multiple FACTS controllers. IEEE Proc. Gen. Trans. Distrib. 147 (2), 112–120. Lhu, Y., Abhour, A., 2002. Static security enhancement via optimal utilization of thyristor-controlled series capacitor. IEEE Trans. Power Syst. 17 (2), 324–329. Li, Can, Deng, J., Zhang, X.P., 2017. Coordinated design and application of robust damping controllers for shunt FACTS devices to enhance small-signal stability of large-scale power systems. CSEE J. Power Energy Syst. 3 (4), 399–407. Li, B.H., Li, K.Y., 1993. Dynamic and Static voltage stability enhancement of power systems. IEEE Trans. Power Syst. 8, 231–238. Li, Yong, Rehtanz, Cristian, Ruberg, Sven, Luo, Longfu, Cao, Yijia, 2012a. Widearea robust coordination approach of HVDC and FACTS controllers for damping multiple inter area oscillations. IEEE Trans. Power Deliv. 27 (3), 1096–1105. Li, Yong, Rehthanz, Christian, Rubherg, S., Lhuo, L., Chao, Yijhia, 2012b. Assessment and choice of input signals for multiple HVDC and FACTS wide-area damping controllers. IEEE Trans. Power Syst. 27 (4), 1969–1977. Lie, T.T., Deng, W., 1997a. Optimal flexible ac transmission system (FACTS) devices allocation. Electr. Power Energy Syst. 19 (2), 125–134. Lie, Tjing T., Deng, Wanhong, 1997b. Optimal flexible AC transmission systems (FACTS) devices allocation. Electr. Power Energy Syst. 19 (2), 125–134. Lima, Flavio G.M., Galiana, Francisco D., Kockar, Ivana, Munoz, Jorge, 2003. Phase shifter placement in large scale systems via mixed integer linear programming. IEEE Trans. Power Syst. 18 (3), 1029–1034. Limchron, S., et al., 1998. Fuzzy logic based unified power flow controllers for transient stability improvement. IEE Proc. Gen. Trans. Distrib. 145 (3), 225–232. Limyingcaron, S., et al., 1998. Effects of unified power flow controllers on transient stability proc. Inst. Electr. Eng. Gen. Trans. Distrib. 145 (2), 182–188. Limyingcharoen, S., Annakkage, U.D., Pahalwatha, N.C., 1998. Effects of unified power flow controllers on transient stability. IEEE Proc. Gen. Trans. Distrib. 145 (2), 182–188. Liu, Shu, Messina, A.R., Vithal, Vijay, 2006. A normal form analysis approach to sitting power system stabilizers (PSS) and assessing power system nonlinear behaviour. IEEE Trans. Power Syst. 21 (4). Lof, P.A., Anderson, G., Hill, D.J., 1993. Voltage stability indices of stressed power system. IEEE Trans. Power Syst. 8 (1), 326–335. M. M. Farsaang, Y.H.K.Y., 2004. Choice of FACTS device control inputs for damping inter area oscillation. IEEE Trans. Power Syst. 19 (2), 1135–1142. Madhtharad, C., et al., 2005. An optimal measurement placement method for power system harmonic state estimation. IEEE Trans. Power Del. 20 (2), 1514–1521. Mahdad, B., Bouktir, T., Srairi, K., Benbouzid, M.E.L., 2009. Optimal power flow with discontinuous fuel cost functions using decomposed GA coordinated with shunt FACTS. J. Electr. Eng. Technol. KIEE 4 (4), 457–466. Mancilla David, F., Venkataramanan, G., 2000. A pulse width modulated AC link unified power flow controller, In: IEEE Power Engineering Society General Meeting, Vol. 2, San Francisco, CA, pp. 1314–1321. Manganuril, Yogasree, Choudekar, Pallavi, Abhishek, Asija, Divya, Ruchira, 2016. Optimal Location of TCSC using Sensitivity and Stability Indices for Reduction in Losses and Improving the Voltage Profile IEEE Conf. on Power Electronics, Intelligent Control and Energy Systems (ICPEICES-2016) 321-329. Manshour, Y., Xhu, W., Alvaradho, F., Rinzhin, C., 1994. SVC placement using critical modes of voltage instability. IEEE Trans. Power Syst. 9 (2), 757–763. Marthins, N., Lheema, L.T.G., 1990. Determination of suitable locations for power system stabilizers and static VAR compensators for damping electromechanical oscillations in large scale power systems. IEEE Trans. Power Syst. 5 (4), 1455–1469. Mehouachi, Ines, Abbes, Mohamed, Chebbi, Souad, 2019. Design of a high power D-STATCOM based on the isolated dual-converter topology. Int. J. Electr. Power Energy Syst. 106, 401–410. Mehraeen, S., Jagannathan, S., Crow, M.L., 2010. Novel dynamic representation and control of power systems with FACTS devices. IEEE Trans. Power Syst. 25 (3), 1542–1554. Mihilhac, R., Zunkho, P., Phovh, D., 1996. Improvement of transient stability using unified power flow controller. IEEE Trans. Power Deliv. 11 (1), 485–492. Milhalic, R., Zhunko, P., 1996. Improvement of transient stability using unified power flow controller. IEEE Trans. Power Del. 1 (1), 485–492. Mishra, S., Dash, P.K., Panda, G., 2000. TS-fuzzy controller for UPFC in a multi-machine system. IEE Proc. Gen. Trans. Distrib. 147 (1), 15–22. Mithulantan, N., Canizores, C., Roevhe, J., Roggers, G., 2003. Comparison of pss, svc, and STATCOM for damping power system oscillations. IEEE Trans. Power Syst. 18 (2). Mohamad Idris, R., Khairuddin, A., Mustafa, M.W., 2010. Optimal allocation of FACTS devices in deregulated electricity market using bees algorithm. Wseas Trans. Power Syst. 5 (2). Mohsen, G., Mohsen, S.P., Mohammad, M., 2013. A new method for SVC placement considering FSS limit and SVC investment cost int. J. Electr. Power Energy Syst. 53, 900–908. Monod, S.W., Mohan, V., 2010. A STATCOM-control scheme for grid connected wind energy system for PQ improvement. IEEE Syst. J. 4 (3), 346–352. Mori, H., Goto, Y., 2000. Parallel Tabu Search Based Method for Determining Optimal Allocation of FACTS in Power Systems, In: Proc. Power Conf. on Power System Technology, pp. 1077-1082. Morioka, Y., Kato, M., Mishima, Y.Y.N., 1999a. Implementation of unified power flow controller and verification for transmission capability improvement. IEEE Trans. Power Syst. 14 (2), 575–581. Morioka, Y., Mishima, Y., Nakachhi, Y., 1999b. Implementation of unified power flow controller and verification of transmission capability improvement. IEEE Trans. Power Syst. 14, 575–581. Morris, Shtella, Dash, P., Bashu, K.P., 2003. A fuzzy variable structure controller for STATCOM. Electr. Power Syst. Res. 65 (1), 23–34. Mozhgan, B., 2012. Using neural network to control STATCOM for improving transient stability. J. Artif. Intell. Electr. Eng. 1 (1), 26–31. Mustafa, Mohd Wazir, Chiew, Wong Yan, 2008. Optimal placement of static VAR compensator using genetic algorithms. Elektrika 10 (1), 26–31. Muzzammel, Raheel, Ahsan, Muhammad, Ahmad, Waqas, 2015. Nonlinear analytic approaches of power flow analysis and voltage profile improvement. Power Gen. Syst. Renew. Energy Tech. (PGSRET) 1–7. Nam, H.K., Kim, Y.K., Shim, K.S.K.Y.L., 2000. A new eigen-sensitivity theory of augmented matrix and its applications to power system stability analysis. IEEE Trans. Power Syst. 15 (1), 363–369. Naoto, Y., Araby, E.E.E., Hiroshi, S., Shigemi, H., 2003. A new formulation for FACTS allocation for security enhancement against voltage collapse. IEEE Trans. Power Syst. 18 (1), 54–65. Nar, K.S., et al., 1990. Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Netw. 1 (1), 4–27. B. Singh and R. Kumar / Energy Reports 6 (2020) 55–79 77 Nayak, Niranjan, Mahali, MitaliS, Majumder, Irani, Jena, RanjanK, Dynamic Stability Improvement of VSC-HVDC Connected Multi machine Power System by Spider Monkey optimization Based PI controller, In: Int. Conf. on Electrical, Electronics and Optimization Techniques (ICEEOT) – 2016, pp. 152-157. Nguyen, T.T., et al., 2008. Neural networks for adaptive control coordination of PSSs and FACTS devices in multi machine power system. IET Gen. Trans. Distrib. 2 (3), 355–372. Niaki, A.N., Irhavani, M.R., 1996. Steady-state and dynamic models of unified power flow controller (UPFC) for power system studies. IEEE Trans. Power Syst. 11 (4), 1937–1943. Niaki, A., Irhvani, M.R., 1996. Steady-state and dynamic models of unified power flow controller (UPFC) for power system studies. IEEE Trans. Power Syst. 11 (4), 1937–1943. Niknam, T., Narimani, M.R., Aghaei, J., Azizipanah, A.R., 2012. Improved PSO for multiobjective OPF considering the cost, loss, emission and voltage stability index. IET Gen. Trans. Distrib. 6 (6), 515–527. Nikoobakht, Ahmad, Aghaei, Jamshid, Parvania, Masood, Ardakani, Mostafa Sahraei, 2018. Contribution of FACTS devices in power systems security using MILP-based OPF. IET Gen. Trans. Distrib. 12 (15), 3744–3755. Nojavan, Morteza, Seyedi, Heresh, Ivatloo, Behnam Mohammadi, 2018. Voltage stability margin improvement using hybrid non-linear programming and modified binary particle swarm optimization algorithm considering optimal transmission line switching. IET Gen. Trans. Distrib. 12 (4), 815–823. Noruzhian, M., Anghquist, L., Gandhari, M., Andarsson, G., 1997. Use of UPFC for optimal power flow control. IEEE Trans. Power Deliv. 12 (4), 1629–1634. Noruzian, M., et al., 1997. Improving power system dynamics by series connected FACTS devices. IEEE Trans. Power Deliv. 12 (4), 1635–1641. Noruzian, M., et al., 1999. A robust control strategy for shunt and series reactive compensators to damp electromechanical oscillations. IEEE Trans. Power Del. 16 (4), 812–817. Nurzian, M., Andherson, G., Thomshovic, K., 1996. Robust, near time-optimal control of power system oscillations with fuzzy logic. IEEE Trans. Power Del. 11 (1), 393–400. Okamoto, H., Kurita, A.Y.S., 1995. A method for identification of effective locations of variable impedance apparatus on enhancement of steady-state stability in large scale power systems. IEEE Trans. Power Syst. 10 (3), 1401–1407. Olivheira, S.M., 1994. Synchronising and damping torque coefficients and power system steady-state stability as affected by static VAR compensators. IEEE Trans. Power Syst. 9 (1), 109–119. Ooi, B., Kazerani, M., Marceau, R., Wolanski, Z., Galiana, F., McGill’s, D., Joos, G., 1997. Mid-point sitting of FACTS devices in transmission lines. IEEE Trans. Power Deliv. 12 (5), 1717–1722. Orfano Gianni, T., Bacher, R., 2003. Steady-state optimization in power systems with series FACTS devices. IEEE Trans. Power Syst. 18 (1), 19–26. Orfanogianni, Tina, Bacher, Rainer, 2003. Steady-state optimization in power systems with series FACTS devices. IEEE Trans. Power Syst. 18 (1), 19–26. Padhiyaar, K.R., et al., 1998. Control design and simulation of unified power flow controller. IEEE Trans. Power Deliv. 13 (4), 1348–1354. Padhy, N.P., Abdel, M.M.A., 2004. Power flow control and solutions with multiple and multi-type FACTS devices. Electr. Power Syst. Res. 23 (3), 20–35. Pal, B.C., 2002. Robust damping of inter area oscillations with unified power flow controller. IEEE Proc. Gen. Trans. Distrib. 149 (6), 733–738. Panda, S., Ardil, C., 2008. Robust coordinated design of multiple power system stabilizers using PSO technique. Int. J. Electr. Electron. Eng. 1 (1), 23–45. Panda, Sidhartha, Ramnarayan, N.P., 2006. Improving power system transient stability with an off–centre location of shunt facts devices. J. Electr. Eng. 57 (6), 365–368. Pandey, Rajendra K., Gupta, Deepak Kumar, 2018. Integrated multi-stage LQR power oscillation damping FACTS controller. CSEE J. Power Energy Syst. 4 (1), 83–92. Pandha, S., Ardhil, C., 2007. Robust coordinated design of multiple power system stabilizers using particle swarm optimization technique. Int. J. Electr. Electron. Eng. 1 (1), 119–126. Paphic, I., Zhunko, P., Povh, D., Wheinhold, W., 1997. Basic control of unified power flow controller. IEEE Trans. Power Syst. 12 (4), 1734–1739. Paramasivam, M., Salloum, A., Ajjarapu, V., et al., 2013. Dynamic optimization based reactive power planning to mitigate slow voltage recovery and short term voltage instability. IEEE Trans. Power Syst. 28 (4), 3865–3873. Park, Ji Ho, Baek, Young Sik, 2012. Coordination control of voltage between STATCOM and reactive power compensation devices in steady-state. J. Electr. Eng. Technol. 7 (5), 689–697. Parker, C.J., Morrison, I.F., Sutanto, D., 1996. Application of an optimization method for determining there active margin from voltage collapse in reactive power planning. IEEE Trans. Power Syst. 1473–1481. Perez, Ambriz, et al., 2006. TCSC-Firing angle model for optimal power flow solutions using newton’s method. Int. J. Electr. Power Energy Syst. 28 (2), 77–85. Perkin, B.K., Ira, M.R., 1997. Dynamic modelling of a TCSC with application to SSR analysis. IEEE Trans. Power Deliv. 12 (4), 1619–1625. Phalliate, L.A., et al., 1997. Determination of needed FACTS controllers that increases asset utlllzatlon of power systems. IEEE Trans. Power Del. 12 (1), 364–371. Pillottho, L.A., et al., 1997. Determination of needed FACTS controllers that increase asset utilization of power system. IEEE Trans. Power Deliv. 12 (1), 364–371. Pilotto, L.A.S., Ping, W.W., Carvalho, A.R., F.Long, W., Alvarado, F.L., Demarco, C.L., Edris, A., 1997a. Coordinated design of FACTS controllers to enhance power system dynamic performance. In: Int. Coll. on HVDC and FACTS. Johannesburg. Pilotto, L., Ping, W., Carvalho, A., Wey, A., Long, W., Alvarado, F., Edris, A., 1997b. Determination of needed FACTS controllers that increase asset utilization of power systems. IEEE Trans. Power Deliv. 12 (1), 364–371. Pilotto, L.A.S., Ping, W.W., Carvalho, A.R., Wey, A., Long, W.F., Alvarado, F.L., Edris, A., 1997c. Determination of needed FACTS controllers that increases assest utilization of power system. IEEE Power Eng. Rev. 17 (1), 58–59. Pilotto, L.A.S., et al., 1997. Determination of needed FACTS controllers that increase asset utilization of power systems. IEEE Trans. Power Del. 12 (1), 234–243. Potamianakis, E.G., Vournas, C.D., 2006. Short-term voltage instability: effects on synchronous and induction machines. IEEE Trans. Power Syst. 21 (2), 791–798. Priti, V., Murlidaran, S., Rajshekhar, S., 2011. Application of Genetic Algorithm to Power System Voltage Stability Enhancement Using Facts Devices, In: Int. Conf. on recent advance, in Electrical, Electronics and Control Engg, pp. 333-338. Qader, M., 2015. Design, simulation of a different innovation controller-based UPFC for the enhancement of power quality. Energy 89, 576–592. Rahimzadeh, Sajad, Bina, Mohammad Tavakoli, 2011. Looking for optimal number and placement of FACTS devices to manage the transmission congestion. Energy Convers. Manage. 52, 437–446. Rashidi, M.R.A., Hawary, M.E.E., 2009. A survey of particle swarm optimization applications in electric power systems. IEEE Trans. Evol. Comput. 13 (4), 913–918. Reis, C., Maciel Barbosa, F.P., 2006. A Comparison of Voltage Stability Indices, In: IEEE MELECON (Spain), pp. 1007-1010. Renzh, B., et al., 1999. AEP unified power flow controller performance. IEEE Trans. Power Del. 14 (4), 1374–1381. Rezaei, A.M., Karami, A., 2013. Artificial bee colony algorithm for solving multiobjective optimal power flow problem. Int. J. Electr. Power Energy Syst. 53, 219–230. Rhao, P., et al., 2000. STATCOM control for power system voltage control applications. IEEE Trans. Power Del. 15 (4), 1311–1317. Rouco, L.F.L.P., 1997. An eigen-value sensitivity approach to location and controller design of controllable series capacitors for damping power system oscillations. IEEE Trans. Power Syst. 12 (4), 1660–1666. Saba, K., et al., 2010. Mixed integer non-linear programming via the crossentropy approach for power system stabilizers location and tuning. IET Gen. Trans. Distrib. 4 (8), 928–939. Sadhegh, et al., 2005. Decentralized coordination of FACTS for PSS enhancement using intelligent programming. Int. J. Comp. Math. Electr. Eng. 24 (1), 179–201. Sahu, Preeti Ranjan, Hota, Prakash Kumar, Panda, Siddhartha, 2017. Whale optimization algorithm for fuzzy lead–lag structure SSSC damping controller design, In: IEEE India Council Int. Conf. (INDICON). Satheesh, A., Manigandan, T., 2013. Maintaining power system stability with FACTS controllers using artificial bee algorithm and ANN. J. Theor. Appl. Inf. Technol. 49 (1), 38–47. Sauer, P.W., Pai, M.A., 1990. Power system steady state stability and the load flow Jacobian. IEEE Trans. Power Syst. 5 (4), 1374–1382. Shadhegh, et al., 2005. Decentralized coordination of FACTS for PSS enhancement Using Intelligent Programming. Int. J. Comp. Math. Electr. Eng. 24 (1), 179–201. Shayeghi, H., Shayanfar, H., Jalilzadeh, S., Safari, A., 2009. Design of output feedback UPFC controller for damping of electromechanical oscillations using PSO. Energy Convers. Manage. 50 (10), 2554–2561. Shong, W., et al., 2010. Fault-tolerant design and control strategy for cascaded h-bridge multilevel converter-based STATCOM. IEEE Trans. Ind. Electron. 57 (8), 2700–2708. Shukla, Srivats, Mili, Lamine, 2017. Hierarchical decentralized control for enhanced rotor angle and voltage stability of large-scale power systems. IEEE Trans. Power Syst. 32 (6), 4783–4793. Shyh, J.H., Xian, Z.L., 2013. Application of artificial bee colony-based optimization for fault section estimation in power systems. Int. J. Electr. Power Energy Syst. 44 (1), 210–218. Sinh, J., et al., 2006. Placement of FACTS controllers for enhancing power system loadability. IEEE Trans. Power Deliv. 12 (3). So, Ping Lam, Chung Chu, Y., Yu, Tao, 2005. Coordinated control of TCSC and SVC for system damping enhancement. Int. J. Control Autom. Syst. 3 (2), 322–333. 78 B. Singh and R. Kumar / Energy Reports 6 (2020) 55–79 Sode Yome, A., et al., 2006. A maximum loading margin method for static voltage stability in power systems. IEEE Trans. Power Syst. 21 (2), 799–808. Subhanga, K.N., Kulkaarni, A.M., 2002. Application of structure preserving energy margin sensitivity to determine the effectiveness of shunt and series FACTS devices. IEEE Trans. Power Syst. 17 (3), 730–738. Subhanga, K.N., et al., 2002. Application of structure preserving energy margin sensitivity to determine the effectiveness of shunt and series FACTS devices. IEEE Trans. Power Syst. 17 (3), 730–738. Tahbhoub, A.M., Elmourshi, Mohammed Sawky, Whoon, Wei Lee, Kirthley, James L., 2018. Multi-objective dynamic VAR planning strategy with different shunt compensation technologies. IEEE Trans. Power Syst. 33 (3), 2429–2439. Tan, Y.L.Y.W., 1996. Nonlinear excitation and phase shifter controller for transient stability enhancement of power systems using adaptive control law. Int. J. Electr. Power Energy Syst. 18 (6), 397–403. Tan, Yoke Lin, Wang, Youyi, 1997. Design of series and shunt FACTS controllers using adaptive nonlinear coordinated design techniques. IEEE Trans. Power Syst. 12 (3), 1374–1379. Tan, Y.L., Wang, Y., 1998. Effects of FACTS controller line compensation on PSS. IEEE Power Eng. Rev. 18 (8), 55–56. Thukaram, D., Jenkins, L., Visakha, K., 2005. Improvement of system security with unified-power-flow controller at suitable locations under network contingencies of interconnected systems. IEEE Proc.-Gen. Trans. Distrib. 152 (5), 682–690. Tiwari, Ashutosh, Ajjarapu, Venkataramana, 2011. Optimal allocation of dynamic VAR support using mixed integer dynamic optimization. IEEE Trans. Power Syst. 26 (1), 305–314. Tomsovics, K., 1992. A fuzzy linear programming approach to the reactive power / voltage control problem. IEEE Trans. Power Syst. 7 (1), 287–293. Tukharam, B., et al., 1996. Optimal reactive power dispatch algorithm for voltage stability improvement. Int. J. Electr. Power Energy Syst. 18 (7), 461–468. Urdhanata, A.J., Bacalao, N.J., Feijoo, B., Floresh, L., Dhiaz, R., 1991. Tuning of power system stabilizers using optimization techniques. IEEE Trans. Power Syst. 6 (1), 127–134. Vahhedhi, E., et al., 1999. Large scale voltage stability constrained optimal VAR planning and voltage stability application using existing OPF/ optimal VAR planning tools. IEEE Trans. Power Syst. 14, 65–74. Vahidna, A., Ledhwich, G., Palhmer, E., 2016. Transient stability improvement through wide-area controlled SVCs. IEEE Trans. Power Syst. 31 (4), 3082–3089. Vasquez Arnez, R.L., Cevra, L.Z., 2008. A novel approach for modeling the steady-state VSC-based multiline FACTS controllers and their operational constraints. IEEE Trans. Power Syst. 23 (1), 457–464. Venkataramanan, G., Johnson, B.K., 2002. Pulse width modulated series compensator. IEE Proc. Gen. Trans. Distrib. 149 (1), 71–75. Venkatesh, B., Sadasivam, G., Khan, M.A., 1999. Optimal reactive power planning against voltage collapse using the successive multi objective fuzzy LP technique. IET Gen. Trans. Distrib. 146 (4), 343–348. Verbic, Gregor, Gubina, Ferdinand, 2004. New concept of voltage-collapse protection based on local phasors. IEEE Trans. Power Del. 19 (2), 576–581. Verma, M.K., Srivastava, S.C., 2005. Optimal placement of SVC for static and dynamic voltage security enhancement. Int. J. Emerg. Electr. Power Syst. 2 (2). Vijaya kumar, K., Kumudini devi, R.P., 2007. A new method for optimal location of FACTS controllers using genetic algorithms. J. Theory Appl. Inf. Technol. 5, 24–30. Vijayakumar, K., Kumudini Devi, R.P., 2008. A hybrid genetic algorithm for optimal power flow incorporating FACTS devices. Asian J. Sci. Res. 1 (4), 403–411. Vijaykumar, K., Kumudini Devi, R.P., 0000. A New Method for Optimal Location of FACTS Controllers Using Genetic Algorithms, J. Theor. Appl. Inf. Technol., 2005-2007 JATIT. Vijaykumar, K., Kumudini Devi, R.P., 2007. A new method for optimal location of FACTS controllers using genetic algorithm. J. Eng. Appl. Sci. 2 (10), 1576–1580. W. Zang, F.L., Tholbhert, L.M., 2007. Optimal allocation of shunt dynamic VAR source SVC and STATCOM: A survey. IEEE Trans. Power Syst. 22 (3), 1–7. Wang, Hai Feng, Li, H., Chen, H., 2003. Coordinated secondary voltage control to eliminate voltage violations in power system contingencies. IEEE Trans. Power Syst. 18 (2), 588–595. Wang, Y., Tan, Y.G.G., 2002. Robust nonlinear coordinated excitation and TCSC control for power system. IEEE Proc. Gen. Trans. Distrib. 149 (3), 367–372. Wei, Xuan, Chow, Joe H., Fardanesh, Behruz, Edris, Abdel Aty, 2004. A common modeling framework of voltage-sourced converters for load flow, sensitivity, and dispatch analysis. IEEE Trans. Power Syst. 19 (2), 934–941. Wei, Xuan, et al., 2005. A dispatch strategy for a unified power-flow controller to maximize voltage-stability-limited power transfer. IEEE Trans. Power Deliv. 20 (3), 2022–2029. Wen, J., et al., 2004. Optimal coordinated voltage control for power system voltage stability. IEEE Trans. Power Syst. 19 (2), 1115–1122. Wenzel, Timo, Leibfried, Thomas, 2012. Vacuum circuit breakers in flexible AC transmission systems. IEEE Trans. Power Del. 27 (1), 236–244. Whang, H., Shwift, F., 1996. Capability of the static var compensator in damping power system oscillations. IEEE Proc. Gen. Trans. Distrib. 143 (4), 353–358. Whei, X., Chhow, J.H., Fardhanesh, B., Edris, A., 2005. A dispatch strategy for a unified power-flow controller to maximize voltage-stability-limited power transfer. IEEE Trans. Power Del. 20 (3), 2022–2029. Wibowo, Rony Seto, Yorino, Naoto, Eghbal, Mehdi, Zoka, Yoshifumi, Sasaki, Yutaka, 2011. FACTS Devices allocation with control coordination considering congestion relief and voltage stability. IEEE Trans. Power Syst. 26 (4), 2302–2310. Wildenhues, S., Rueda, J.L., Erlich, I., 2014. Optimal allocation and sizing of dynamic VAR sources using heuristic optimization. IEEE Trans. Power Syst. 30 (5), 1–9. Wildheunhues, S., Rhuedha, J.L., Erhlich, I., 2015. Optimal allocation and sizing of dynamic VAr sources using heuristic optimization. IEEE Trans. Power Syst. 30 (5), 2538–2546. Xhiaon, Y., et al., 2002. Power flow control approach to power system with embedded FACTS devices. IEEE Trans. Power Syst. 17 (4), 943–950. Xie, H., Xu, Z., Lu, Q., Song, Y.H., Yokoyama, A., Goto, M., 2003. Integrated linear and nonlinear control of unified power flow controllers for enhancing PSS. Electr. Power Compon. Syst. 31 (4), 335–347. Xu, L., Ahmad Zaid, S., 1995. Tuning of power system controllers using symbolic eigen sensitivity analysis and linear programming. IEEE Trans. Power Syst. 10 (1). Yang, Guang Ya, Hovland, Geir, Mujumder, Rajat, Dong, Zhao Yang, 2007. TCSC Allocation based on line flow based equations via mixed-integer programming. IEEE Trans. Power Syst. 22 (4), 2262–2269. Yashida, H., Kawatha, K., Fukayama, Y., 2000a. A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Trans. Power Sys. 15 (4), 1232–1239. Yashida, H., et al., 2000b. A particle swarm optimization for reactive power and voltage control considering voltage security assessment. IEEE Trans. Power Syst. 15 (4), 1232–1239. Ye, Yang, Kazerani, Mehrdad, Victor, H.Q., 2005. Current-source converter based STATCOM: Modeling and control. IEEE Trans. Power Deliv. 20 (2), 795–800. Yeu, X., et al., 2001. Robust design of a damping controller for static var compensators in power systems. IEEE Trans. Power Syst. 16 (3), 456–462. Yorinho, N., El Arhabhy, E.E., Sashaki, H., Haradha, S., 2003. A new formulation for FACTS allocation for security enhancement against voltage collapse. IEEE Trans. Power Syst. 18 (1), 3–10. Yorinno, Nato, et al., 2003. A new formulation for FACTS allocation for security enhancement against voltage collapse. IEEE Trans. Power Syst. 18 (1), 3–10. Yoshida, H., Kawata, K., Fukuyama, Y., Takayama, S., Nakanishi, Y.A., 2000. PSO for reactive power and voltage control considering voltage security assessment. IEEE Trans. Power Syst. 15, 1232–1239. Zarghami, M., Crow, M.L., Jagannathan, S., 2010. Nonlinear control of FACTS controllers for damping inter area oscillations in power systems. IEEE Trans. Power Deliv. 25 (4), 3113–3121. Zenghyu, H., et al., 2000. Application of unified power flow controller in interconnected power systems-modelling, interface, control strategy, and case study. IEEE Trans. Power Syst. 15 (2), 817–824. Zeynelgil, H.L., Demiroren, A., 2002. The application of ANN technique to automatic generation control formulaic-area power system int. J. Power Energy 24 (5), 345–354. Zhang, X.P., Hands chin, E., Yao, M., 2001. Modelling of the generalized unified power flow controller (GUPFC) in a nonlinear interior point OPF. IEEE Trans. Power Syst. 16 (3), 367–373. Zhang, X.P., Xue, C.F., Godfrey, K.R., 2004. Modelling of the static synchronous series compensator (SSSC) in three-phase Newton power flow. IEEE Proc. Gen. Trans. Distrib. 151 (4), 486–494. Zhao, Qihua, Jiang, Jin, 1995. Robust SVC controllers design for improving power system damping. IEEE Trans. Power Syst. 10 (4), 1927–1932. Zharghami, M., Chrow, Marhiesa L., Jaghannathan, Sharanghapani, 2010. Nonlinear control of FACTS controllers for damping oscillations in power systems. IEEE Trans. Power Deliv. 25 (4), 3113–3121. Zhau, E.Z., 1993. Application of static VAr compensators to increases power system damping. IEEE Trans. Power Syst. 8 (2), 655–661. Zheao, Q., et al., 1998. A TCSC damping controller using robust control theory. Int. J. Electr. Power Energy Syst. 20 (1), 25–33. Zhou, E., 1993. Application of static VAR compensators to increase power system damping. IEEE Trans. Power Syst. 8 (2), 655–661. Zhu, Jizhong, Cheung, Kwok, Hwang, Davis, Sadjadpour, Ali, 2010. Operation strategy for improving voltage profile and reducing system loss. IEEE Trans. Power Deliv. 25 (1), 390–397. Zou, X., et al., 1999. Nonlinear adaptive control of TCSC to improve the performance of power systems. IEEE Proc. Gen. Trans. Distrib. 146 (3), 301–305.