Support vector regression based adaptive power system stabilizer

The main purpose of this paper is to compare the performances of our proposed support vector machine (SVM) based power system stabilizer (PSS) with a conventional PSS, artificial neural networks (ANN) and radial basis function (RBF) networks in PSS applications. We train an application of the SVM, namely the support vector regression (SVR), to approximate functions (nonlinear regression) in real-time tuning of the parameters of PSS. In addition to being a simpler model, the experimental results suggest that the SVR can be trained in a much shorter time than ANN and RBF networks. Moreover, the SVR provides the greatest robustness among these four approaches.

[1]  P. Kundur,et al.  Power system stability and control , 1994 .

[2]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[3]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[4]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[5]  M. L. Kothari,et al.  Orthogonal least squares learning algorithm based radial basis function (RBF) network adaptive power system stabilizer , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[6]  Davide Anguita,et al.  Fast training of Support Vector Machines for regression , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[7]  Young-Moon Park,et al.  A neural network-based power system stabilizer using power flow characteristics , 1996 .

[8]  M. L. Kothari,et al.  Adaptive conventional power system stabilizer based on artificial neural network , 1996, Proceedings of International Conference on Power Electronics, Drives and Energy Systems for Industrial Growth.

[9]  M.L. Kothari,et al.  Radial basis function (RBF) network adaptive power system stabilizer , 2000, 2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.00CH37077).