Application of Neural Networks in Power Systems; A Review
Authors: M. Tarafdar Haque, A.M. Kashtiban
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.
Keywords: Neural network, power system, security assessment, fault diagnosis, load forecasting, economic dispatch, harmonic analyzing.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1081503
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 7815References:
[1] M. T. Vakil, N. Pavesic, A Fast Simplified Fuzzy ARTMAP Network, Kluwer Academic Publisher, pp. 273-316, July 2003.
[2] K. Warwick, A. Ekwure, R. Aggarwal, Artificial Intelligence Techniques in Power Systems, IEE Power Engineering Series 22, Bookcratt Printed, pp. 17-19, 1997.
[3] G. Rolim, J.G. Zurn, Interpretation of Remote Backup Protection for Fault Section Estimation by a Fuzzy Exper System, IEEE PowerTech Conference, pp. 312-315, June 2003.
[4] R. Lukomski, K. Wilkosz, Power System Topology Verification Using Artificial Neural Network Utilization of Measurement Data, IEEE PowerTech Conference, pp. 180-186, July 2003.
[5] T.T. Nguyen, Neural Network Load Flow, IEEE Trans. of Distribution, Generation and Transmission Conference, pp. 51-58, January 1995.
[6] M. Vasilic, M. Kezunoric, Fuzzy ART Neural Network Algorithm for Classifying the Power System Faults, IEEE Trans. Power Delivery, pp. 1-9, July 2004.
[7] J.P. Park, K. Ganesh, Comparison of MLP and RBF Neural Networks Using Deviation Signals for Indirect Adaptive Control of a Synchronous Generator, IEEE Trans. of Power Delivery, pp. 919- 925, March 2004.
[8] K.W. Chan, A.R. Edward, A.R. Danish, On-Line Dynamic Security Contingency Screening Using Artificial Neural Network, IEEE Trans. Power Distribution System, pp. 367-372, November 2000.
[9] G. Chicco, R. Napoli, Neural Network for Fast Voltage Prediction in Power System, IEEE Power Tech Conference, pp. 312-316, September 2001.
[10] M.T. Vakil, N. Pavesic, Training RBF Network with Selective Backpropagation, Neurocomputing Elsevier Journal, pp. 39-64, July 2004.
[11] H.S. Hippert, C.E. Pedreira, R.C. Souza, Neural Networks for Short- Term Load Forecasting: A Review and Evaluation, IEEE Trans. on Power System, VOL. 16, NO. 1, pp. 44-53, Februrary2001.
[12] W. Charytoniuk, M.S. Chen, Neural Network Design for Short-Term Load Forecasting, IEEE International Conference of Deregulation of Power System Technologies, pp. 554-551, April 2000.
[13] A.K. Sinha, Short Term Load Forecasting Using Artificial Neural Networks, IEEE Trans. On Power System Distribution, pp. 548-553, 2000.
[14] G. Chicco, R. Napoli, F. Piglone, Load pattern clustering for Short- Term Load Forecasting of anomalous days, IEEE Trans. on Power Tech, pp. 550-556, September 2001.
[15] M. Gavrilas, I. Ciutera, C. Tanasa, Medium-Term Load Forecasting With Artificial Neural Network Models, IEE CIRED Conference, pp. 482-486, June 2001.
[16] M.S. Kandil, S.M. El-Debeiky, N.E. Hasanien, Long-Term Load Forecasting for Fast Developing Utility Using a Knowledge-Based Expert System, IEEE Trans. on Power Systems, Vol. 17, No. 2, pp. 491-496, May 2002.
[17] T. Senjyu, P. Mandal, K. Uezato, Next day load curve forecasting using recurrent neural network structure, IEEE Trans. on Power Distribution System, pp. 388-394, March 2003.
[18] T. Saksornchai, W.J. Lee, M. Methaprayoon, J. Liao, Improve the Unit Commitment Scheduling by Using the Neural Network Based Short Term Load Forecasting, IEEE Trans. Power Delivery, pp. 33- 39, June 2004.
[19] H. S. Hippert, C.E. Pedreira, Estimating temperature profiles for short-term load forecasting: neural networks compared to linear models, IEE Trans. on distribution and Generation Conference, pp. 543-547, January 2004.
[20] N. Kumarappan, M.R. Mohan, S. Murugappa, ANN Approach to Combined Economic and Emission Dispatch for Large-Scale System, IEEE Power Distribution system, pp. 323-327, March 2002.
[21] K. P. Wong, Computational Intelligence Application in Unit Commitment, Economic Dispatch and Power Flow, IEEE Conference in Advance in Power System Control, Operation and Management, pp. 54-59, November 97.
[22] J. Moreno, A. Esquivel, Neural Network Based Approach for the Computation of Harmonic Power in Real Time Microprocessor-Based Vector for an Induction Motor Drive, IEEE Trans. on Industry Application, pp. 277-282, January 2000.
[23] J. R. Vazquez, P. R. Salmeron, Three Phase Active Power Filter Control Using Neural Networks, 10th Mediterranean Electrotechnical Conference, Vol 3, pp. 924-927, 2000.
[24] A. G. Bahbah, A. A. Girgis, New Method for Generator's Angles and Angular Velocities Prediction for Transient Stability Assessment of Multi Machine Power Systems Using Recurrent Neural Network, IEEE Trans. of Power System, Vol 19, pp 1015-1022, May 2004.
[25] L. L. Lai, E. Vaselcar, H. Subasinghe, Fault Location of a Teed- Network With Wavelet Transform Neural Networks, IEEE International Conference of Deregulation of Power System Technologies, pp 505-509, April 2000.
[26] H. K. Siu, H. W. Ngan, Automatic Power Quality Recognition System Using Wavelet Analysis, IEEE International Conference of Deregulation of Power System Technologies, pp 311-316, April 2004.
[27] M. Fanabashi, A. Maeda, Y. Morooka, K. Mori, Fuzzy and Neural Hybrid Systems: Synergetic AI, IEEE Expert, pp. 32-40, 1995.
[28] J. A. Momoh, X. Ma, K. Tomsovic, Overview and Literature Survey of Fuzzy Set Theory in Power System, IEEE Trans. Power Systems, pp. 1676-1690, 1995.
[29] K. Tomosovic, A Fuzzy Linear Peogramming Approach to the Reactive Power/Voltage Control Problem, IEEE Trans. Power Systems,(1992), pp. 87-293.
[30] A. A. El Desouky, M. M. El Kateb, Hybrid Adaptive Technique for Electric Load Forecast Using ANN and ARIMA, IEE Proceeding in Distribution Conference, pp 213-217, July 2000.
[31] T. Saksomchai, W. J. Lee, K. Methaprayoon, J. Liao, Improve the Unit Commitment Scheduling by Using the Neural Network Based Short Term Load Forecasting, IEEE, pp. 33-39, July 2004.
[32] P. Ansarimehr, S. Barghinia, N. Vafadar, Short Term Load Forecasting for Iran National Power System Using Neural Network and Fuzzy Expert System, IEEE, pp. 1082-1085, July 2002.