Application of Artificial Neural Networks to solve the problem of Power Flow in Electrical Energy Systems
DOI:
https://doi.org/10.21754/tecnia.v10i2.464Abstract
This article proposes the use of artificial neural networks (ANN) to solve the power flow problem in electrical energy systems. Power flow calculates the steady state of an electrical power system (SEP) and is a fundamental tool for the planning, operation and control of modern SEPs. The mathematical model of the power flow corresponds to a set of nonlinear algebraic equations that can be solved conventionally with the iterative Newton-Raphson (NR) method or with its decoupled versions. Currently, there are various commercial computer programs that use such methods. Among the objectives of the solution of the ANN-based power flow problem proposed here, its potential application stands out to solve problems that require a large computational effort such as online static security analysis and contingency analysis. The proposed methodology was applied to the 6-bar Ward-Hale and 14-bar IEEE (IEEE-14) test systems, observing
successful results in terms of arithmetic precision and processing time, compared to other conventional methods.
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[1] . Liacco T.E., "Enhancing power system security control", IEEE Computer Applications in Power, Vol. 10, No.3, pp.38-41, July 1997.
[2] . Arrillaga J. and Arnold C.P., "Computer Analysis of Power Systems", John Wiley & Sons Ltd., 1990.
[3] . Stott B., "Review of load-flow calculation methods", IEEE Proceedings, Vol. 62, pp.916-929, 1974.
[4] . Van Amerongen R., "A general-purpose version of the fast decoupled load flow", IEEE Transactions on Power Systems, Vol.4, No.2, pp.760-770, May 1990.
[5] . Sobajic D.J. and Pao Y.H., "Artificial Neural-Net Based Dynamic Security Assessment for Electric Power Systems", IEEE Transactions on Power Systems, Vol.4, No.1, pp.220-226, Feb.1989.
[6] . El-Sharkawi M. and Niebur D., "Artificial Neural Networks with Applications to Power Systems", IEEE PES special publication 96 TP 112-0, 1996.
[7] . Haykin S., «Neural Networks: a comprehensive foundation», 2nd.ed., Prentice Hall, 1998.
[8] . Lo K.L., Peng L.J., Macqueen J.F, Ekwue A.O. and Cheng D.T.Y., "Fast Real Power Contingency Ranking Using a Counterpropagation Network", IEEE Transactions on Power Systems, Vol. 13, No.4, pp. 1259-1264, Nov. 1998.
[9] . Refaee J.A., Mohandes M. and Maghrabi H., “Radial Basis Function Networks for Contingency Analysis of Bulk Power Systems", IEEE Transactions on Power Systems, Vol.14, No.2, pp.772-778, May 1999.
[10] . Nguyen T.T., "Neural network load-flow", IEE Proceedings- Generation, Transmission and Distribution, Vol. 142, No. 1, pp.51-58, Jan. 1995.
[11] . Paucar V.L., Morelato A.L. and Vuono E., "Training an ANN with the BFGS method for real-time identification of sinusoidal waveforms", Proceedings of the International Conference on Intelligent Systems Applications to Power Systems, ISAP, pp. 193-196, Brazil, Apr. 1999.
[12] . Mansour Y., Chang A., Tamby J., Vaahedi E., Corns B. and El-Sharkawi M., "Large Scale Dynamic Security Screening and Ranking Using Neural Networks", IEEE Transactions on Power Systems, Vol. 12, No.2, pp.954-960, May 1997
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