{"title":"Application of Neural Networks in Power Systems; A Review","authors":"M. Tarafdar Haque, A.M. Kashtiban","volume":6,"journal":"International Journal of Energy and Power Engineering","pagesStart":897,"pagesEnd":902,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/13640","abstract":"
The electric power industry is currently undergoing an unprecedented reform. One of the most exciting and potentially profitable recent developments is increasing usage of artificial intelligence techniques. The intention of this paper is to give an overview of using neural network (NN) techniques in power systems. According to the growth rate of NNs application in some power system subjects, this paper introduce a brief overview in fault diagnosis, security assessment, load forecasting, economic dispatch and harmonic analyzing. Advantages and disadvantages of using NNs in above mentioned subjects and the main challenges in these fields have been explained, too.<\/p>\r\n","references":"[1] M. T. Vakil, N. Pavesic, A Fast Simplified Fuzzy ARTMAP Network,\r\nKluwer Academic Publisher, pp. 273-316, July 2003.\r\n[2] K. Warwick, A. Ekwure, R. Aggarwal, Artificial Intelligence\r\nTechniques in Power Systems, IEE Power Engineering Series 22,\r\nBookcratt Printed, pp. 17-19, 1997.\r\n[3] G. Rolim, J.G. Zurn, Interpretation of Remote Backup Protection for\r\nFault Section Estimation by a Fuzzy Exper System, IEEE PowerTech\r\nConference, pp. 312-315, June 2003.\r\n[4] R. Lukomski, K. Wilkosz, Power System Topology Verification Using\r\nArtificial Neural Network Utilization of Measurement Data, IEEE\r\nPowerTech Conference, pp. 180-186, July 2003.\r\n[5] T.T. Nguyen, Neural Network Load Flow, IEEE Trans. of\r\nDistribution, Generation and Transmission Conference, pp. 51-58,\r\nJanuary 1995.\r\n[6] M. Vasilic, M. Kezunoric, Fuzzy ART Neural Network Algorithm for\r\nClassifying the Power System Faults, IEEE Trans. Power Delivery,\r\npp. 1-9, July 2004.\r\n[7] J.P. Park, K. Ganesh, Comparison of MLP and RBF Neural Networks\r\nUsing Deviation Signals for Indirect Adaptive Control of a\r\nSynchronous Generator, IEEE Trans. of Power Delivery, pp. 919-\r\n925, March 2004.\r\n[8] K.W. Chan, A.R. Edward, A.R. Danish, On-Line Dynamic Security\r\nContingency Screening Using Artificial Neural Network, IEEE Trans.\r\nPower Distribution System, pp. 367-372, November 2000.\r\n[9] G. Chicco, R. Napoli, Neural Network for Fast Voltage Prediction in\r\nPower System, IEEE Power Tech Conference, pp. 312-316,\r\nSeptember 2001.\r\n[10] M.T. Vakil, N. Pavesic, Training RBF Network with Selective\r\nBackpropagation, Neurocomputing Elsevier Journal, pp. 39-64, July\r\n2004.\r\n[11] H.S. Hippert, C.E. Pedreira, R.C. Souza, Neural Networks for Short-\r\nTerm Load Forecasting: A Review and Evaluation, IEEE Trans. on\r\nPower System, VOL. 16, NO. 1, pp. 44-53, Februrary2001.\r\n[12] W. Charytoniuk, M.S. Chen, Neural Network Design for Short-Term\r\nLoad Forecasting, IEEE International Conference of Deregulation of\r\nPower System Technologies, pp. 554-551, April 2000.\r\n[13] A.K. Sinha, Short Term Load Forecasting Using Artificial Neural\r\nNetworks, IEEE Trans. On Power System Distribution, pp. 548-553,\r\n2000.\r\n[14] G. Chicco, R. Napoli, F. Piglone, Load pattern clustering for Short-\r\nTerm Load Forecasting of anomalous days, IEEE Trans. on Power\r\nTech, pp. 550-556, September 2001.\r\n[15] M. Gavrilas, I. Ciutera, C. Tanasa, Medium-Term Load Forecasting\r\nWith Artificial Neural Network Models, IEE CIRED Conference, pp.\r\n482-486, June 2001.\r\n[16] M.S. Kandil, S.M. El-Debeiky, N.E. Hasanien, Long-Term Load\r\nForecasting for Fast Developing Utility Using a Knowledge-Based\r\nExpert System, IEEE Trans. on Power Systems, Vol. 17, No. 2, pp.\r\n491-496, May 2002.\r\n[17] T. Senjyu, P. Mandal, K. Uezato, Next day load curve forecasting\r\nusing recurrent neural network structure, IEEE Trans. on Power\r\nDistribution System, pp. 388-394, March 2003.\r\n[18] T. Saksornchai, W.J. Lee, M. Methaprayoon, J. Liao, Improve the\r\nUnit Commitment Scheduling by Using the Neural Network Based\r\nShort Term Load Forecasting, IEEE Trans. Power Delivery, pp. 33-\r\n39, June 2004.\r\n[19] H. S. Hippert, C.E. Pedreira, Estimating temperature profiles for\r\nshort-term load forecasting: neural networks compared to linear\r\nmodels, IEE Trans. on distribution and Generation Conference, pp.\r\n543-547, January 2004.\r\n[20] N. Kumarappan, M.R. Mohan, S. Murugappa, ANN Approach to\r\nCombined Economic and Emission Dispatch for Large-Scale System,\r\nIEEE Power Distribution system, pp. 323-327, March 2002.\r\n[21] K. P. Wong, Computational Intelligence Application in Unit\r\nCommitment, Economic Dispatch and Power Flow, IEEE Conference\r\nin Advance in Power System Control, Operation and Management, pp.\r\n54-59, November 97.\r\n[22] J. Moreno, A. Esquivel, Neural Network Based Approach for the\r\nComputation of Harmonic Power in Real Time Microprocessor-Based\r\nVector for an Induction Motor Drive, IEEE Trans. on Industry\r\nApplication, pp. 277-282, January 2000.\r\n[23] J. R. Vazquez, P. R. Salmeron, Three Phase Active Power Filter\r\nControl Using Neural Networks, 10th Mediterranean Electrotechnical\r\nConference, Vol 3, pp. 924-927, 2000.\r\n[24] A. G. Bahbah, A. A. Girgis, New Method for Generator's Angles and\r\nAngular Velocities Prediction for Transient Stability Assessment of\r\nMulti Machine Power Systems Using Recurrent Neural Network,\r\nIEEE Trans. of Power System, Vol 19, pp 1015-1022, May 2004.\r\n[25] L. L. Lai, E. Vaselcar, H. Subasinghe, Fault Location of a Teed-\r\nNetwork With Wavelet Transform Neural Networks, IEEE\r\nInternational Conference of Deregulation of Power System\r\nTechnologies, pp 505-509, April 2000.\r\n[26] H. K. Siu, H. W. Ngan, Automatic Power Quality Recognition System\r\nUsing Wavelet Analysis, IEEE International Conference of\r\nDeregulation of Power System Technologies, pp 311-316, April 2004.\r\n[27] M. Fanabashi, A. Maeda, Y. Morooka, K. Mori, Fuzzy and Neural\r\nHybrid Systems: Synergetic AI, IEEE Expert, pp. 32-40, 1995.\r\n[28] J. A. Momoh, X. Ma, K. Tomsovic, Overview and Literature Survey\r\nof Fuzzy Set Theory in Power System, IEEE Trans. Power Systems,\r\npp. 1676-1690, 1995.\r\n[29] K. Tomosovic, A Fuzzy Linear Peogramming Approach to the\r\nReactive Power\/Voltage Control Problem, IEEE Trans. Power\r\nSystems,(1992), pp. 87-293.\r\n[30] A. A. El Desouky, M. M. El Kateb, Hybrid Adaptive Technique for\r\nElectric Load Forecast Using ANN and ARIMA, IEE Proceeding in\r\nDistribution Conference, pp 213-217, July 2000.\r\n[31] T. Saksomchai, W. J. Lee, K. Methaprayoon, J. Liao, Improve the\r\nUnit Commitment Scheduling by Using the Neural Network Based\r\nShort Term Load Forecasting, IEEE, pp. 33-39, July 2004.\r\n[32] P. Ansarimehr, S. Barghinia, N. Vafadar, Short Term Load\r\nForecasting for Iran National Power System Using Neural Network\r\nand Fuzzy Expert System, IEEE, pp. 1082-1085, July 2002.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 6, 2007"}