Neural network-based Load Balancing and Reactive Power Control by Static VAR Compensator

Transcription

1 nternational Journal of Computer and Eletrial Engineering, Vol. 1, No. 1, April 2009 Neural network-based Load Balaning and Reative Power Control by Stati VAR Compensator smail K. Said and Marouf Pirouti Abstrat unbalaned loads in three phase systems produe undesired negative and zero sequene urrents. Negative sequene urrents will ause exessive heating of eletrial mahines, saturation of transformers, ripple in retifiers or even instability problems of generators. Zero sequene urrents ause not only exessive power losses in neutral lines but also protetion and interferene problems. To ahieve a balaned operation for unbalaned AC system and improve power transfer apability, reative ompensation is used. This study use Artifiial Neural Network (ANN)deals with an ative sheme aiming to attenuate the negative sequene omponent of the line urrents assoiated with an unbalaned three phase load. The system also assures a unity power fator at the load bus. The proposed struture uses a Y-onneted apaitor bank and _-onneted thyristor reator whih is ontrolled by ANN, So that, the amount of reative power is being ontrolled. Three different networks are learned by both real and imaginary part for three phase load urrents (x, y, z). Hundreds of unbalaned ases in a 33 (KV) system are simulated for this purpose. Study show that ANN-based reative power ompensation is ompletely suitable for an on-line load balaning and reative power ompensation in the system by measuring both real and imaginary part of three phase load urrents and proessing them by ANN. ndex Terms ANN, Load balaning, Stati VAR ompensation. NTRODUCTON Reative power requirements of industrial loads suh as eletri ar furnaes, rolling mills, tration loads, ar welders, et. are often unbalaned and ould vary in a wide range within short period of time. These loads lead to unbalaned system voltages and flutuations in the supply voltage ausing many undesirable effets suh as malfuntion of protetive relays and sensitive loads, inandesent lam fliker, and television piture distortion. Generally, the heavy loads whih reate these problems are loated in industrial plants and supplied from on terminal network. Therefore they an be effetively ompensated by shunt ompensator onneted at supply point of the industrial faility [1] The reent tendeny of load ompensation theory is the transformation of an arbitrary ungrounded unbalaned linear load into a balaned three-phase load without hanging the smail K. Said is with the Eletrial Department of Salahaddin University, Erbil, raq. Marouf Pirouti is with the Eletrial Department of Salahaddin university, Erbil,Kurdistan region, raq, :[email protected], Tel: real power exhange between loads and soure, but by appropriate modifiation of reative load power in eah phase. Load ompensation involves two separable ats; load balaning and Power fator orretion. Both of them an be arried out together or separately. Modifiation of the reative power of the phases an be done by onneting variable reative soure in the shunt with load element. Despite the emergene of modern devie suh as STATCOMs, Stati VAR Compensator (SVS) are still popularly used in modern power distribution systems for load balaning and power fator orretion due its lower ost [2]. A Stati VAR Compensator generally onsists of a Thyristor Controlled Reator (TCR) and a Thyristor Swithed Capaitor (TSC) and ompensates loads through generation or absorption of reative power. The operation of thyristor-ontrolled reators at appropriate ondution angles an be advantageously used to meet the unbalaned and varying reative power demand in a system [1-5]. n this study Thyristor Controlled Reator -Fixed Capaitor (TCR-FC) is used and ANN apability in pattern reognition is applied to ontrol of reative power by ontrolling of firing angle of thyristors. Appropriate firing angles for eah pair of thyristors (αt1, αt2, αt3) are predited by three different ANNs for any unbalaned ondition in the system.. STATC VAR COMPENSATOR (TCR-FC TYPE) The term stati VAR system has been adopted to apply to a number of stati VAR ompensation devies for use in shunt reative ontrol. These devies onsist of shunt onneted, stati reative elements (linear or nonlinear reator and apaitor) onfigured into a VAR ompensating system, and their distintion is that the shunt reative power flowing in these devies is ontrollable over some rated range of VARs. The basi system onsists of parallel ombination of fixed apaitors and thyristor swith by an angle α, in eah half yle (α inreased from 90o to 180o) the tehnique of ontrolling the ondution intervals of the thyristor swith, generate harmoni urrent omponents [6]. Fast response and the apability of balane load make the fixed apaitor, thyristor ontrolled indutor partiularly advantageous for ompensating those loads whih present rapidly at various unbalaned onditions. n this study apability of ANN is used to reognize unbalane onditions and to provide proper firing angle as quikly as possible for thyristors whih provide reative power to balane the system. FC-TCR is

2 nternational Journal of Computer and Eletrial Engineering, Vol. 1, No. 1, April 2009 shown in Fig.1. network is a network in whih signal propagate from the output of any neuron to the input of any neuron. What is fed bak is usually the error in the output modified appropriately aording to the requirement needed. Both these models will have the three or subgroups of proessing elements via, input layer, hidden layer, and output layer. n this study multi layer feed forward network with ML bak propagation training algorithm is used [8]. Fig.1 Fixed apaitor, thyristor-ontrolled indutor type. ARTFCLAL NEURAL NETWORKS Artifiial neural networks have been widely used in the power industry in fault lassifiation, protetion, fault diagnosis, relaying shemes, load foreasting and power generation. At present most ANNs are built upon the environment of real numbers. However it is well known that in omputations related to eletri power systems, suh as load flow analysis and fault level estimation, omplex numbers are extensively involved. The reative power drawn from a substation, the impedane, bus bar voltages and urrents are all expressed in omplex numbers. But ANNs are able to deal with omplex numbers by treating the real parts and imaginary parts independently. ANNs have been proved to be apable of learning from raw data. They an be used to identify relations within raw data not expliitly given or even known by human experts and there is no need to assume any linear relationship between data. ANNs represent the promising new generation of information proessing networks [7]. ANNs an supplement the enormous proessing power of the digital omputer with the ability to make sensible deision and to learn by ordinary experiene. ANNs have widely been used in eletri power engineering. For energy management, load flow and optimum power flow problems are solved by ANNs to estimate bus bar voltages. ANNs an perform the task of assoiative memory. Sine information is stored in the onnetions and it is distributed throughout, the network an funtions as a memory. This memory an work even the presene of ertain level of internal noise. ANNs are somewhat fault tolerant in the sense that information is not lost even if some onnetion are snapped or some units are not funtioning. t an deal with data that are not only noisy, but also fuzzy, inonsistent due to assoiative and distributed nature. They have ability to approximate funtions and automati similarity based generalization. ANNs are distinguished based on the signal flow diretion and is lassified into, feed forward network and feed bak network. A feed forward network is a network in whih signal propagate in only one diretion from an input stage through intermediate neurons to an output stage. t has no memory sine output solely depends on the input. A feed bak V. MATHEMATCS MODELNG AND SMULATON n this study a three-phase 33 KV system as shown in Fig.2, is onsidered. The substation is assumed to be a onstant balaned voltage soure and the three distribution line impedanes are equal. The unbalane of the single phase loads in the system will ause an asymmetry of the line urrents, and onsequently unequal voltage drops on the line distribution lines. n these onditions the load bus voltages will beome unbalaned. n order to improve the load power fator and to balane the line urrents a fast ation system is onneted to the load bus. t onsists of a star onneted apaitor bank (Y-CCB) and a delta onneted TCR bank (Δ-TCR). The thyristors in the TCR are driven to provide the neessary amount of reative power to eah phase. From Fig.2, we have,, the omplex values of the fundamental urrents in the TCR branhes. as, bs, s the omplex values of the line urrents (supply urrents) 1, 2, 3 the omplex values of the urrents in the apaitors. x, y, z the omplex values of the unbalaned load urrents. The load urrents are: x = xl 2 y = h yl ---- (1) z = h * zl The urrents through the Y-CCB branhes are balaned, whih onsists only reative omponent and for phase (a) an be expressed as = V / X ---- (2) a The values of the urrents that must flow in the -TCR branhes, in order to obtain the desired effets are: 1 = [ + imag( xl yl ) imag( zl )] (3) = [ imag( xl yl zl )] 3 1 = [ + imag( xl ) imag( yl zl )] 3 The instantaneous value of the urrent through one leg of the -TCR bank is the sum of fored and natural responses [9]: V π π i = 2 sin( wt ) sin( α ) --- (4) ω * L

3 nternational Journal of Computer and Eletrial Engineering, Vol. 1, No. 1, April 2009 π Where < α < π 2 n order to alulate the values of the firing angle, we must alulate the ratio of RMS / * RMS Table.1, where RMS represents the required RMS value of the urrent through TCR and * RMS is the maximum value of the TCR s urrent (α=π/2) RMS / * RM S a (RAD) TABLE RMS / * RM S a (RAD) Sine the urrents in the bank are in quadrate lagging the line voltages, they have the expressions: = h * = --- (5) 2 = h Using first Kerhief s law, the supply urrents will be: as = + + x 2 bs = h + + y --- (6) s = h + + z n this paper, based above relations the value of firing angle of TCR Thyristors (one branh) as funtion of RMS is alulated. n order to generalize, these values are alulated as a funtion of RMS / * RMS ratio Table.1, where RMS represents the required RMS value of the urrent through TCR and * RMS is the maximum value of the TCR s RMS urrent at (α=π/2). For providing data in ANN appliation, based above mathematial bakground a program in MATLAB is written for this purpose [10]. Hundreds of unbalaned onditions by using this program are simulated and appropriate α is obtained. V. RESULT Multilayer feed forward networks were hosen to proess the prepared input data. n this study 749 unbalaned ases were simulated and appropriated firing angle for eah pair of thyristors (T1, T2, T3) was obtained. 66 perent of all data was used for training and others were applied for testing the networks. Three multilayer feed forward networks with proposed sheme as shown in Fig.2. are onsidered for this propose. Both real and mg parts of three phase load urrent x, y and z after normalization were fed to neural networks. The networks arhitetures were deided empirially by using trail and error method for hoosing best ANN struture whih is involved to training and testing different number of networks. Unfortunately, it is diffiult to know beforehand how large a network should be for a speifi appliation. f a larger network is used the more omplex funtions of the network an be reated and if small enough networks are seleted, they will not have enough power to over fit the data. Various networks with different number of neurons in their hidden layers were trained with ML algorithms. Ultimately, three networks with Logsig-Logsig-purelin ativation funtion and neuron in input-first hidden layer-seond hidden layer-output were suitable for this appliation by whih the mean square errors for all testing data for all three networks were A. Reative power ompensation Testing result for eah network for 60 unbalaned onditions is shown in Table.2. For eah unbalaned ondition both real and imag parts of three phase urrent as mentioned before were used. And at the right side of the table both desired and atual firing angle for ompensate reative power during load flutuation is ome. The amount of reative power whih is supplied by the system (Qs) with out ompensation and with ompensation (ANN-based FC-TCR) for all 60 ases in Table.2, are shown in Fig.3. B. Balaning three phase urrents The three phase urrents whih are supplied by the system (as, bs and s) also balaned by this purposed sheme. The amount of three phase urrents for all 60 ases in Table.2 after power reative ompensation and balaning system are shown in Fig.4. Also unbalaned fator for all 60 ases before and after ompensation is shown in Table.3. (a) (b) Fig. 2 (a) Qs Supplied by the system with out ompensation (b) Qs Supplied by the system with ANN based FC-TCR ompensation

4 nternational Journal of Computer and Eletrial Engineering, Vol. 1, No. 1, April 2009 Fig. 3 Load ompensator modeling using (TCR) &FC ombined with ANN Fig. 4 Three phase urrent after power reative ompensation by ANN based FC-TCR for all 60 ases in Table.2. CONCLUSON A medium voltage system 33KV is seleted for this study and ANN based FC-TCR ompensator is onsidered. ANN apabilities in pattern reognition and lassifiation are designed. Simulation studies are performed and module s performane with different system onditions is investigated. t was found that ANN appliation is very aurate and useful for ontrol of reative power and balaning system. From the study Table.2. t an be seen that ANN is very quik and reliable to find appropriate firing angle for thyristors whih are onerning to ompensate any unbalaned ondition in the system. Control of reative and minimizing the reative power drawn from the supply. Fig.3. (a) and (b), shows by seleting appropriate firing angle for Thyristors amount of reative power drawn from the supply is minimized signifiantly so that power fator also is orreted. Balaning three phase urrents and minimizing the unbalaned fator, from the Table.3. and Fig.4. t an be observed that this proposed algorithm is able to balane three phase urrent and minimize the unbalaned fator ( Negative-sequene / positive-sequene ) in any unbalaned ondition. 28

5 nternational Journal of Computer and Eletrial Engineering, Vol. 1, No. 1, April 2009 TABLE. : DESRED AND ACTUAL FRNG ANGLE OF T1, T2 AND T3 FOR ANN-BASED FC-TCR TO COMPENSATE REACTVE POWER AND BALANCE SEVERAL UNBALANCED CONDTONS Real ( a) m ( a) Real m Real ( b) ( b) ( ) m ( ) Desired fire angle (Angle) ANN fire angle (Angle) α T1 α T2 α T3 α T1 α T2 α T TABLE : COMPARSON OF UNBALANCED FACTOR BEFORE AND AFTER COMPENSATON BY ANN-BASED FC-TCR FOR ALL 60 CASES N TABLE.2 No. ANN-based firing angle Unbalaned Fator( Negative -Sequene / Positive- sequene )

6 nternational Journal of Computer and Eletrial Engineering, Vol. 1, No. 1, April 2009 of α T1 α T2 α T3 Before ompensation by ANN After ompensation by ANN Cases i e i i i i e-005i i 1.70e e-006i i e e-005i i e e-005i i e-006i i e e-005i i e e-005i i e-005i i e-005i i e e-005i i e e-005i i e e-005i i e i i e e-006i i e e-006i i e i i e i i 4.105e e-005i i e e-005i i e e-005i i e i i e e-005i i e-005i i i i i i e e-005i i e e-005i i i i i i e-005i i i i i i i i e i i i i i i e e-005i i 8.98e e-005i i i i i i 7.148e e-005i i e i i e-006i i i i i i i i i i e e-005i i e i i i i i i e i i i i i i i i e-005i i e-005i i e-005i furnae loads, Eletri Power systems Researh, 58, (2001) pp REFERENCES [4] G.Gueth, P.Enstedt, A Ray and R.W.Menzies, ndividual phase ontrol [1] D.Thukaram, A.Lomi and S.Chirarattananon, Minimization of harmonis under three-phase unbalaned operation of stati VAR of stati ompensator for load ompensation and voltage balaning and regulation, EEE Trans, Power system, 2(4), 1987, pp ompensators, Proeedings of the th nternational Conferene on Power Quality (Power Quality 99), Chiago, USA, [5] A. Rajapakse and Anawat Puangpairoj, Harmoni reduing ANN ontroller for a SVS ompensating unbalaned flutuating loads, [2] S.Y.Lee, C.J.Wu and W.N. Chang, A ompat ontrol algorithm for reative power ompensation and load balaning with stati var nternational Journal of Emerging Eletri Power Systems, 7 (1), 2006, Artile 5. ompensator, Eletri Power Systems Researh, 58, 2001, pp [6] R.Mohan Mathur and Rajiv K.Varma, Thyristor-based FACTs [3] G.El-Saady, Adaptive stati VAR ontroller for simultaneous elimination of voltage fliker and phase urrent imbalanes due to ar ontrollers for eletrial transmission systems, John Wiley & sons publiations, 2002 pp

7 nternational Journal of Computer and Eletrial Engineering, Vol. 1, No. 1, April 2009 [7] Jayabarathi.R.a and Devarajan.N.b, ANN based DSPC ontroller for reative power ompensation, Amerian journal of applied siene, 4(7), 2007, pp [8] Hagan, M.T and M.Menhaj, Training feed forward networks with the Marquardt algorithm, EEE. Transation on Neural Networks, 5(6), 1999, pp [9] Muhammad H. Rashid, Power eletronis iruits, devies and appliation, Seond Edition, Prentie-Hall, [10] Brian D. Hahn and Daniel T. Valentine, Essential MATLAB for Engineers and Sientists, Third edition, Published by Elsevier, smail K. Said, is graduated from Salahaddin University both BS and MS and now he is assistant leturer at the Eletrial Department, his area of interest is ontrol of power system Marouf Pirouti is graduated BS from K.N.T University of Tehnology Tehran-ran, 2003 and MS from Salahaddin University, He is now assistant leturer at Eletrial Department of Salahaddin University and also he is teahing at the Dejleh College.His area of interest is ontrol of power and appliation of A in power system.