Counterpropagation Neural Network for Solving Power Flow Problem
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.
[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.
[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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:56594", author = "Jayendra Krishna and Laxmi Srivastava", title = "Counterpropagation Neural Network for Solving Power Flow Problem", 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", volume = "2", number = "3", pages = "431-6", }
