A System for the Generation and Detection of Electrical Disturbances

A SYSTEM FOR THE GENERATION AND DETECTION OF ELECTRICAL DISTURBANCES Iñigo Monede o1, Ca los León1, Jo ge Rope o1, José Luis de la Vega2,, Juan C. Mon año2, José Manuel Elena1 1School o Compu e Science and Enginee ing 2IRNAS Elec onic Technology Depa men Campus Reina Me cedes A da, Reina Me cedes s/n P.O. Box 1052 41012 Se ille (Spain) 41080 Se ille (Spain) con ac email: imonede [email protected] phone: 34-954624711 Abs ac . Powe Quali y is de ined as he s udy o he quali y o elec ic powe lines. The de ec ion and classi ica ion o he di e en dis u bances which cause powe quali y p oblems is a di icul ask which equi es a high le el o enginee ing expe ise. Thus, neu al ne wo ks a e usually a good choice o he de ec ion and classi ica ion o hese dis u bances. This pape desc ibes a powe ul sys em, de eloped by he Ins i u e o Na u al Resou ces and Ag obiology a he Scien i ic Resea ch Council (CSIC) and he Elec onic Technology Depa men a he Uni e si y o Se ille, o he gene a ion and de ec ion (by means o neu al ne wo ks) o elec ical dis u bances. Key Wo ds Powe quali y, elec ical dis u bance, Wa ele ans o m, neu al ne wo k. 1.- In oduc ion Powe Quali y Powe Quali y (PQ) is de ined as he s udy o he quali y o elec ic powe lines. PQ has been a opic o conside a ion o he las wo decades, and has ecen ly acqui ed in ensi ied in e es due o he wide sp ead use o elec onical de ices in complica ed indus ial p ocesses and he gene alised powe quali y o comme cial elec ic powe [1]. Thus nowadays, cus ome s demand highe le els o PQ o ensu e he p ope and con inued ope a ion o such sensi i e equipmen . The poo quali y o elec ical powe is no mally a ibu ed o powe line dis u bances such as wa eshape aul s, o e ol ages, capaci o swi ching ansien s, ha monic dis o ion and impulse ansien s. Thus, elec omagne ic ansien s, which a e momen a y ol age su ges powe ul enough o sha e a gene a o sha , can cause ca as ophic damage suddenly. Ha monics, some imes e e ed o as elec ical pollu ion, a e dis o ions o he no mal ol age wa e o ms ound in ac ansmission, which can a ise a i ually any poin in a powe sys em. While ha monics can be as des uc i e as ansien s, o en he g ea es damage om hese dis o ions lies in he loss o c edibili y o he powe u ili ies is-a- is hei cus ome s. The classi ica ion and iden i ica ion o each one o he dis u bances is no mally ca ied ou om s anda ds and ecommenda ions depending on whe e he u ili ies ope a e (IEEE in he Uni ed S a es, UNE in Spain, e c). Figu e 1.- S anda d IEEE 1159 The de ec ion and classi ica ion o he di e en dis u bances which cause powe quali y p oblems is a di icul ask which equi es a high le el o enginee ing expe ise [2]. Due o he abo e men ioned di icul ies, a i icial in elligence ools [3] eme ge as an in e es ing al e na i e in he de ec ion o elec ical dis u bances. The main in elligen ools o in e es include expe sys ems, uzzy logic and a i icial neu al ne wo ks (ANNs) [4]. 442-286 180 Neu al Ne wo ks on Powe Quali y Fo he de ec ion and classi ica ion o dis u bances, ANNs can be combined wi h ma hema ical analysis such as Fou ie and Wa ele ans o ms o he gene a ion o signal ea u es which se e as inpu s in he ne wo k [5]. Thus, ea u e ex ac ion by wa ele ans o ms p o ides an unique cha ac e is ic which can ep esen e e y single PQ dis u bance a di e en esolu ions using he echnique called mul i- esolu ion signal decomposi ion o mul i esolu ion analysis. In his way, while he de ec ion o he powe quali y signals has ended o be easy, hei classi ica ion is s ill a di icul ask in which ANNs play an impo an ole [6][7][8]. Pa e n ecogni ion in ANNs gene ally equi es p ep ocessing o da a, ea u e ex ac ion and inal classi ica ion. One o he mos impo an asks in he design and de elopmen p ocess o an ANN is o gene a e an adequa e numbe o aining pa e ns in o de o app oxima e u u e inpu s. Some imes an op imal design o he ANN is ound bu he limi ed numbe o ain pa e ns does no gi e good esul s. In pa icula , in PQ a g ea numbe s o elec ical pa e ns a e necessa y due o he mul iple combina ions o di e en dis u bances which can coincide in one o a ious samples. Ano he addi ional p oblem wi h ANNs applied o PQ is he impossibili y o ge ing eal pa e ns di ec ly om he powe line due o he i egula i y in he appa i ion o dis u bances. 2.- Elec ical Pa e n Gene a o Fo he ask o aining neu al ne wo ks o he de ec ion and classi ica ion o elec ical dis u bances we a e de eloping an elec ical pa e n gene a o . The objec i e o his gene a o is o c ea e an unlimi ed numbe o pa e ns o be used by a classi ica ion sys em. The Elec ical Pa e n Gene a o make i possible o con igu e pa ame e s such as he du a ion o he sample, he equency o he signal and he numbe o samples in an ideal cycle (50Hz o 60Hz) and o add one o mo e dis u bances. F om he selec ed pa ame e s, he gene a o c ea es a ex ile wi h he ol age alues o he sample. The s uc u e o he ile consis s o a heade wi h he ile in o ma ion (name, numbe o sample cycles and sampling pe iod) and a da a column co esponding o he ol ages o each o he samples. One ile example om he Elec ical Pa e n Gene a o can be seen in Figu e 2. HEADER Name: Impulse-01. x Ideal cycles: 5 Sampling pe iod (ms): 0.156250 END HEADER -28.171582 -12.934081 2.334579 17.597614 32.818256 47.959835 62.985875 77.860176 92.546906 107.010682 121.216659 ... Figu e 2.- File om he Elec ical Pa e n Gene a o The ype o dis u bances includes: impulse, oscilla ion, sag, swell, in e up ion, unde ol age, o e ol age, ha monics and equency a ia ions. In ampli ude dis u bances (impulses, sags, swells, in e up ions, unde ol ages and o e ol ages), he ool allows us ha pa ame e s such as ampli ude, s a ime, inal ime, ising and alling slope, be con igu ed. The edi ion o ha monics allows he con igu a ion o ampli ude and phases as a as o y ha monic le els including he possibili y o adding hem an o se . Figu e 3.- Ideal signal (one-phase ep esen a ion) Models o dis u bances 1.- Ha monic dis o ion Ha monic dis o ion is de ined as he phenomenon in which di e se sinusoidal signals wi h di e se equencies which a e mul iples o he undamen al equency a e supe posed on he ideal signal (Figu e 4). 181 Figu e 4.- Ha monic dis o ion The ollowing ma hema ical model was implemen ed in he gene a o : 1 () sin(2 ) N iii i C A A π ϕ = =+ + ∑ (1) • A: DC e m (V). • Ai: Ampli ude o he i h ha monic o signal (V). • i: F equency o he i h ha monic o signal (Hz) • φi: Phase o he i h ha monic (Rad) • i: Ha monic o de (i= 1,.., N). In ou ha monic model C( ) is conside ed as consis ing o a undamen al and 39 ha monic componen s. 2.- F equency de ia ion F equency de ia ion is a signal dis u bance added o he ha monic dis o ion. The model consis s o he equency modula ion o he signal C( ) by means o he ca ie signal M( ), which is named modula ing signal. Figu e 5.- F equency de ia ion The ma hema ical exp ession o his signal is: ) j sin(BB) (M jm j j ϕπ ++= ∑ = 2 40 1 (2) • B: DC e m (V). • Bj: Ampli ude o he j h ha monic o signal (V) • m: Fundamen al equency (Hz) • φi: Phase o he i h ha monic (Rad) Conside ing (1) and (2) he esul an signal would end up as: ∑∑ ==      +++++= 40 1 40 1 22 ij jmjici ) j sin(BB i sinAA) (X ϕπϕπ (3) which shows he ha monic con en and equency modula ion. The addi ion o ano he kind o dis u bance was ca ied ou om he p e ious exp ession X( ). The e o e, he p e ious exp ession would be he esul o an ideal elec ical signal o a equency o /and ha monic dis u bed signal. 3.- O e ol ages, swells, unde ol ages and sags. In his kind o dis u bances he ampli ude o he signal ises (o e ol ages o swells) o alls (unde ol ages and sags) a ce ain alue along a ime in e al. Figu e 6.- Sag In he de elopmen o he dis u bance gene a o , a apezoidal model o he ampli ude e olu ion (lineal slope) was conside ed. The model makes i possible o app oxima e he ampli ude dis u bances mos equen ly encoun e ed in powe sys ems. Figu e 7 shows a g aphical o he model used o o e ol ages o swells (in e se apeze o unde ol ages and sags). Figu e 7.- O e ol age and swell model • p: ini ial sample o he apeze • pip: slope o he ini ial amp 182 • p p: slope o he inal amp • n1: numbe o samples o he ini ial amp • n2: numbe o samples o he inal amp • : numbe o samples when he climb is eached • selec : o al numbe o samples Figu e 8.- O e ol age 4.- T ansien s The elec ical pa e n gene a o models ansien s as a damped sine h ough a supe posed exponen ial unc ion, which is added o X( ) a a ce ain poin . Figu e 9.- T ansien The implemen ed ma hema ical model obeys o: ) sin(Ae) (T a ϕπ += −2 (4) • a: ansi o y exponen . • A : ampli ude o he ipple (V) • : equency o he ipple (Hz) • φ : ini ial phase o he ipple (Rad) 5.- Noise The gene a o makes i possible o add Addi i e Whi e Gaussian Noise (AWGN) (Figu e 10) in o de o simula e mo e ealis ic signals o he powe line. Figu e 10.- A signal wi h AWGN 3.- Elec ical dis u bances classi ie based on Neu al Ne wo ks. In addi ion o he gene a ion sys em, we ha e de eloped a i s e sion o a sys em o he de ec ion and classi ica ion o elec ical dis u bances. The sys em is a de ec o o powe line dis u bances based on a i icial in elligence echniques (in pa icula , a i s e sion based on ANNs). We ha e used he p e iously desc ibed Elec ical Pa e n Gene a o in o de o ca y ou he ANN aining. A he momen , we ha e gene a ed a ound one housand pa e ns o elec ical signals which include all he abo e-men ioned dis u bances. The de ec ion sys em uses Wa ele ans o m o he acqui ed signal o he gene a ion o signal ea u es [5][6][7][8]. The aim o ea u e ex ac ion by Wa ele ans o ms was o p o ide an unique cha ac e is ic which can ep esen e e y single PQ dis u bance. Fo p og amming asks we ha e used he MATLAB ool which has some powe ul oolboxes (specialized unc ions) o signal, Wa ele s and Neu al Ne wo ks. The inpu ec o s o he ANN a e gene a ed ca ying ou a numbe o ope a ions on he Wa ele ans o m. I is known he Wa ele ans o m de ec s be e he slow a ia ions in he las le els and as a ia ions in i s le els. So, ou solu ion is based on he concep o ha he ampli ude dis u bances would be be e de ec ed in he i s le els o Wa ele ans o m while he equency ones would be be e de ec ed in he las le els. The e o e, we ha e used wo pa allel neu al ne wo ks. One o hem is used o de ec he ampli ude dis u bances and he o he one he equency dis u bances. In pa icula , we ha e used he ollowing alues as inpu ec o o he ampli ude neu al ne wo k: he VRMS o he signal, he in eg al, he maximum and he VRMS o he de ail coe icien s o 1,2 and 3 le el. Capas Capa Capa de en ada ocul as de salida Figu e 11.- Mul ilaye pe cep on Inpu laye Hidden laye s Ou pu laye 183 The kind o ANN is a mul ilaye pe cep on wi h 3 hidden laye s wi h 20, 14 and 8 neu ons espec i ely. We ha e eached his s uc u e a e es ing a g ea numbe o di e en numbe s o neu ons and laye s. Al hough we ha e es ed o he kinds o s uc u es like LVQ and RBF a he momen he bes esul s ha e been eached wi h he pe cep on. In equency we ha e used he ollowing alues o he inpu ec o o he ne wo k: he VRMS o he signal, he in eg al, he maximum and he VRMS o he de ail coe icien s o 6,7 and 8 le el. The s uc u e o he ANN o equency is a mul ilaye pe cep on wi h 3 hidden laye wi h 4,3 and 2 neu ons espec i ely. In he aining o he ne wo ks we used 80% o he gene a ed signals as aining pa e ns and 20% as es pa e ns. In he i s ne wo k we eached he bes esul s wi h a aining o 180 epochs ge ing he igh classi ica ion o he 93.82% o he es signals. Besides, mos o he inco ec ly de ec ed dis u bances we e in he h eshold wi h o he kind o dis u bance. The kind o di e en ampli ude dis u bances as well as he pe cen o co ec ly de ec ed dis u bances in each one is shown in able 1. Table 1.- Resul s in ampli ude dis u bances Dis u bance Tes signals Numbe o e o s Co ec ly de ec ed % Sag 51 2 96.08 Ideal 56 6 89.29 Swell 25 2 92.00 Unde ol age 21 0 100 O e ol age 38 4 89.47 F equency de ia ion 52 1 98.08 To al 243 15 93.83 On he o he hand, in he equency ne wo k we e co ec ly classi ied he 97.53% wi h a aining o 261 epochs. Wi h he help o he gene a o o dis u bances, we a e cu en ly wo king in he gene a ion o new pa e ns o he aining o he ANN. E en when he cu en esul s a e e y good, we expec o ge be e hem wi h he inc ease o he numbe o pa e ns. 4.- Conclusions Today i is known ha neu al ne wo ks a e a good choice o de ec ing and classi ying elec ical powe dis u bances. In he huge li e a u e [4][5][6][7] abou de ec ion o elec ical dis u bances which we can ind, o en he p oblem lies in gene a ing a su icien numbe o aining pa e ns o ge ha neu al ne wo k ob ains good esul s in u u e inpu s. Wi h he help o he gene a o is possible o ca y ou a aining o he ANN su icien ly en i e in o de o ge eliable esul s. Thus, we ha e de eloped an elec ical pa e n gene a o which is capable o gene a ing common dis u bances which can be ound in a powe line wi h he aim o making he aining o neu al ne wo ks easie . Besides, we ha e de eloped a i s e sion o a classi ie sys em based on ANNs. Figu e 12.- E olu ion o aining o he equency ANN Ou inal objec i e is o implan his de ec o as a eal- ime sys em capable o classi ying dis u bances on line. In o de o ge a as sys em, we a e cu en ly wo king in educing and compiling he p ocessing o he signal p e ious o ANN. We a e es ing o he possibili ies o p ocessing o he signal and o he s uc u es o he ANN in o de o ge be e he esul s. Re e ences [1] M. McG anaghan, B. Roe ge , Economic E alua ion o Powe Quali y, IEEE Powe Enginee ing Re iew, Feb 2002. [2] A.Hussain, M.H. Sukai i, A. Mohamed, R. Mohamed, Au oma ic De ec ion O Powe Quali y Dis u bances and Iden i ica ion o T ansien s Signals”, In e na ional Symposium on Signal P ocessing and i s Applica ions, Kuala Lumpu , Malaysia, 13-16 Augus 2001. [3] R. Adapa, Powe Quali y Analysis So wa e, IEEE Powe Enginee ing Re iew, Feb ua y 2002, 0272- 1724/02. [4] W.R. Anis Ib ahim and M.M. Mo cos, A i icial In elligence and Ad anced Ma hema ical Tools o Powe Quali y Applica ions: A Su ey, 668 IEEE T ansac ions on Powe Deli e y, Vol. 17, Ap il 2002 [5] G. Zheng, M.X. Shi, D. Liu, J. Yao, Z.M. Mao, Powe Quali y Dis u bance Classi ica ion Based on Rule-Based and Wa ele -Mul i-Resolu ion Decomposi ion, P oceedings o he Fi s In e na ional Con e ence on Machine Lea ning and Cybe ne ics, Beijing, 4-5 No embe 2002 184 [6] R. Daniels, Powe quali y moni o ing using neu al ne wo ks, P oc.1s In . Fo um Applica ions Neu al Ne wo ks Powe Sys ., 1991, pp. 195–197. [7] C. Xiangxun, Wa ele -based Measu emen and Classi ica ion o Powe Quali y Dis u bances, Powe Enginee ing Socie y Win e Mee ing, 2002, ol. 2, 2002. [8] D. Bo ás, M. Cas illa, N. Mo eno and J.C. Mon año, Wa ele and neu al s uc u e: a new ool o diagnos ic o powe sys em dis u bances, IEEE T ans. on Indus y Applica ions, Vol. 37, No. 1, pp. 184-190, 2001. Acknowledgemen s The wo k desc ibed in his pape has been suppo ed by he Spanish Minis y o Science and Technology (MCYT: Minis e io de Ciencia y Tecnología) h ough p ojec e e ence numbe DPI2002-04420-C03-03. 185