Elman Neural Network Backpropagation Based Evaluation of Critical Busbars in Power Systems with Renewable Sources

Year 2015, Volume: 5 Issue: 2, 532 - 541, 01.06.2015

Prasanta Kumar Satpathy Pradyumna Kumar Sahoo Mihir Narayan Mohanty

Abstract

We present an efficient analysis for evaluation of critical busbars in electric power systems by judging the performance of various backpropagation schemes of Historical Elman Neural Network. The objective of this study is to find the most efficient scheme that yields fastest convergence under supervised learning. The study is conducted on the standard IEEE 30-bus test system supplemented by renewable source of generation. Out of six backpropagation schemes tried in this work, it is observed that gradient descent backpropagation with momentum and adaptive learning rate performs exceedingly well in terms of fast convergence irrespective of number of hidden layers and neuron assignment. It is claimed that this study would be very helpful to the power system utilities and researchers in reducing the burden on the utility of conducting routine power flow simulations.

Keywords

Voltage stability; critical busbars; neural network; backpropagation

References

• C. Concordia, “Voltage Instability”, Electrical Power and Energy Systems. Vol. 13, pp. 14-20, 1991.
• N. Yorino, H. Sasaki, Y. Masuda, Y. Tamura, M. Kitagawa, and A. Oshimo, “An investigation of Voltage Instability Problems”, IEEE Trans on Power Systems. Vol. 7, pp. 600-611, 1992.
• V. Ajjarapu and B. Lee, “Bibliography on Voltage Stability”, IEEE Trans on Power Systems. Vol. 13, pp. 115-125, 1998.
• P. Kessel and H. Glavitsch, “Estimating the Voltage Stability of a Power System”, IEEE Trans on Power Delivery. Vol. 1, pp. 346-354, 1986.
• M.E. Hawary, Electric Power Applications of Fuzzy Systems, IEEE Press, 1998.
• L. Zadeh, “Fuzzy Sets as a basis for theory of Possibility”. Fuzzy Sets and Systems. Vol. 1, pp. 3-28, 1978.
• H.J. Zimmerman, Fuzzy Set Theory and its Application, Kluwer Academic Press, 1994.
• A.A.E. Keib and X. Ma, “Application of Artificial Neural Networks in Voltage Stability Assessment”, IEEE Trans on Power Systems. Vol. 10, pp. 1890-1896, 1995.
• T.S. Dillon, “Artificial neural nets applications to power systems and their relationships to symbolic methods”, Electrical Power and Energy Systems. Vol. 13, pp. 66-72, 1991.
• S.N. Pandey, S. Tapaswi, and L. Srivastava “Price Prediction based Congestion Management using growing RBF Neural Network”, Proc. IEEE India Conference (INDICON), pp. 482-487, 2008.
• B. Alberto, D. Maurizio, M. Marco, S.P. Marco, and M. Politecnico, “Congestion Management in a Zonal Market by a Neural Network Approach”, European Transactions on Electric Power. Vol. 19, pp. 569-584, 2009.
• S.S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, India, 1999.
• M. Hagan, H. Demuth, and M. Beale, Neural Network Design, PWS Publishing, Boston, 1996.
• Z. Zhigang and W. Jun, Advances in Neural Network Research and application, Springer-Verlag, Berlin, 2010.
• P.K. Kalra, A. Srivastava, and D.K. Chaturvedi, “Artificial neural nets applications to power systems operation and control”, Electric Power Systems Research. Vol. 25, pp. 83-90, 1992.
• S. Iman, K. Abbas, and F. Rene, “Radial Basis Function Neural Network Application to Power System Restoration Studies”, Computational Intelligence and Neuroscience. Vol. 1, pp. 1-10, 2012.
• A.R. Bahamanyar and A. Karami, “Power System Voltage Stability Monitoring using Artificial Neural Networks with a Reduced set of Inputs”, Electrical Power and Energy Systems. Vol. 58, pp. 246-256, 2014.
• D.Q. Zhou, U.D. Annakkage, and A.D. Rajapakse, “Online Monitoring of Voltage Stability Margin Using an Artificial Neural Network”, IEEE Trans on Power Systems. Vol. 25, pp. 1566-1574, 2010.
• M. Mirzaei, M.Z.A. Kadir, H. Hizam, and E. Moazami, “Comparative Analysis of Probabilistic Neural Network, Radial Basis Function, and Feed-forward Neural Network for Fault Classification in Power Distribution Systems”, Electric Power Components and Systems. Vol. 39, pp. 1858-1871, 2011.
• L. Xue, C. Jia, and D. Dajun, “Comparison of Levenberg- Marquardt method and Path Following interior point method for the solution of optimal power flow problem”, Emerging Electric Power Systems. Vol. 13, pp. 15-35, 2012.
• B. Ilamathi, V.G. Selladurai, and K. Balamurugan, “ANN-SQP Approach for NOx emission reduction in Coal Fired Boilers”, Emerging Electric Power Systems. Vol. 13, pp. 1-14, 2012.
• D.K. Ranaweera, “Comparison of neural network models for fault diagnosis of power systems”, Electric Power Systems Research. Vol. 29, pp. 99-104, 1994.
• El. Sharkawi, M.A. Marks, and R.J. Weerasoritya, “Neural Networks and their Application to Power Engineering”, Control Dynamic System, Advances in theory and Applications. Vol. 41, 1991.
• M.F. Moller, “A scaled conjugate gradient algorithm for fast supervised learning”, IEEE Trans. on Neural Networks. Vol. 6, pp. 525-533, 1993.