Counterpropagation Neural Network for Solving Power Flow Problem

Authors: Jayendra Krishna, Laxmi Srivastava

Abstract:

Power flow (PF) study, which is performed to determine the power system static states (voltage magnitudes and voltage angles) at each bus to find the steady state operating condition of a system, is very important and is the most frequently carried out study by power utilities for power system planning, operation and control. In this paper, a counterpropagation neural network (CPNN) is proposed to solve power flow problem under different loading/contingency conditions for computing bus voltage magnitudes and angles of the power system. The counterpropagation network uses a different mapping strategy namely counterpropagation and provides a practical approach for implementing a pattern mapping task, since learning is fast in this network. The composition of the input variables for the proposed neural network has been selected to emulate the solution process of a conventional power flow program. The effectiveness of the proposed CPNN based approach for solving power flow is demonstrated by computation of bus voltage magnitudes and voltage angles for different loading conditions and single line-outage contingencies in IEEE 14-bus system.

Keywords: Admittance matrix, counterpropagation neural network, line outage contingency, power flow

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1333941

References:

[1] S. Weerasooriya, M. El-Sharkawi, M. Damborg,, R.J. Marks,II, "Towards static security assessment of a large scale power system using neural networks," IEE Proc.-C, vol. 139, 1992, pp. 64-70.
[2] B. Stott, "Review of load flow calculation methods," IEEE Proc.,Vol. 62, 1974, pp. 916 - 929.
[3] R. Van Amerongen, "A general-purpose version of the fast decoupled load flow, " IEEE Trans. on Power Systems, Vol. 4, No. 2, 1990, pp. 760-770.
[4] T.S. Dillon, "Artificial Neural Network applications in power systems and their relationship to symbolic methods," Electric Power &Energy Systems, vol.13, no.2, 1991, pp. 66-72.
[5] P.K. Kalra, Alok Srivastava and D.K. Chaturvedi, "Possible applications of neural nets to power system operation and control," Electric Power System Research, Vol. 25, 1992, pp. 83-90.
[6] S. Vidya Sagar, Vankayala and D. Rao Natakki, "Artificial neural network and their application to power system - a bibliographical survey," Electric Power System Research, Vol. 28, 1993, pp. 67-69.
[7] L. Srivastava, S.N. Singh and J. Sharma, "ANN applications in power systems: An overview and key issues," Int. Conf. on computer applications in Elect. Engg., Recent Advances (CERA97), U.O.R., Roorkee (India), 1997, pp. 397-403.
[8] Nima Amjady, "Dynamic voltage security assessment by a neural network based method," Electric Power Systems Research, Vol. 66, no. 3, 2003, pp. 215-226.
[9] M. Pandit, L. Srivastava and J. Sharma, "Cascade Fuzzy Neural Network Based Voltage Contingency Screening and Ranking," Electric Power Systems Research, Vol. 67, 2003, pp. 143-152.
[10] T. Jain, L. Srivastava, and S.N. Singh, "Fast voltage contingency screening using Radial Basis Function neural network," IEEE Transactions on Power Systems, Vol 18, No. 4, 2003, pp. 1359-1366.
[11] D. Thukaram, H.P Khincha, and H.P Vijaynarasimha, "Artificial neural network and support vector machine approach for locating faults in radial distribution systems," IEEE Transactions on Power Delivery, Vol 20, No. 2, 2005, pp. 710-721.
[12] Won Kim Gwang, and K.Y. Lee, "Coordination control of ULTC transformer and STATCOM based on an artificial neural network," IEEE Transactions on Power Systems, Vol 20, No. 2, 2005, pp. 580- 586.
[13] Niu Dong-Xiao, Wanq Qiang, and Li Jin-Chao, "Short term load forecasting model using support vector machine based on artificial neural network," Proceedings of 2005 Int. Conference on Machine Learning and Cybernetics, 2005, Vol. 7,18-21 Aug. 2005, pp. 4260- 4265.
[14] N. Kumar, R. Wangneo, P.K. Kalra, S.C. Srivastava, "Application of Artificial Neural Network to load flow," Proc. TENCON-91, IEE Region 10 Int. Conf. On EC3-Energy, Computer, Communication and Control System, Vol.1, 28-30 Aug. 1991, pp 199-203.
[15] T.T. Nguyen, "Neural Network load-flow," IEE Proceedings - Gener. Transm. Distrib., Vol. 142, No. 1, 1995, pp. 51 - 58.
[16] Leonardo Paucar, and Marcos J. Rider, "Artificial Neural Networks for solving power flow problem in electric power systems," Electric power system Research, Vol. 62, 2002, pp.139-144.
[17] K.L. Lo, L.J. Peng, J.F. Macqueen, A.O. Ekwue and D.T.Y. Cheng, "Fast real power contingency ranking using counterpropagation neural network," IEEE Trans. on Power Systems, Vol. 13, No. 4, 1998, pp. 1259-1264.
[18] J. Krishna, L. Srivastava, M. Pandit and S.N. Singh, "On-line Contingency Analysis Using Counterpropagation Neural Network," Proceedings National Power System conference (NPSC 2004) held at I.I.T. Chennai in December 2004, pp. 333-337.
[19] Li Min Fu, Neural Networks in Computer Intelligence McGraw-Hill International Editions, 1994.
[20] L.L. Freris, and A.M. Sasson, "Investigation of the load-flow problem," Proc. IEE, Vol. 115, No.10, October 1968, pp. 1459 - 1470.