Estimation of the critical clearing time using MLP and RBF neural networks

This paper presents multi-layer perceptron (MLP) and radial basis function (RBF) neural networks (NNs) based methods for the estimation of the critical clearing time (tcr) as an index for power systems transient stability analysis (TSA). The tcr evaluation involves elaborate computations that often include time-consuming solutions of nonlinear on-fault and post-fault systems equations. Knowing that for a particular fault scenario (contingency), the tcr is a function of the pre-fault system operating point, the objective of this paper is to show how one may develop the MLP and the RBF NNs based methods for estimating the tcr by using only the pre-fault operating conditions as the inputs of the NNs. The paper uses the proposed MLP and RBF NNs based methods to estimate the tcr under different topological as well as operating conditions of the 10-machane 39-bus New England test power system, and results are given. The simulation results show that both NNs are able to retain past learned information almost instantaneously. However, compared to the RBF NN, the MLP NN makes us have a more accurate estimation for the tcr. Copyright © 2008 John Wiley & Sons, Ltd.

[1]  Mania Ribbens-Pavella,et al.  A simple direct method for fast transient stability assessment of large power systems , 1988 .

[2]  A.A. Girgis,et al.  New method for generators' angles and angular velocities prediction for transient stability assessment of multimachine power systems using recurrent artificial neural network , 2004, IEEE Transactions on Power Systems.

[3]  Kurt Hornik,et al.  Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks , 1990, Neural Networks.

[4]  I. Erlich,et al.  New parallel radial basis function neural network for voltage security analysis , 2005, Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems.

[5]  Dejan J. Sobajic,et al.  Artificial Neural-Net Based Dynamic Security Assessment for Electric Power Systems , 1989, IEEE Power Engineering Review.

[6]  M. Ribbens-Pavella,et al.  Extended Equal Area Criterion Justifications, Generalizations, Applications , 1989, IEEE Power Engineering Review.

[7]  Laxmi Srivastava,et al.  Fast voltage contingency screening using radial basis function neural network , 2003 .

[8]  E. Hobson,et al.  Effectiveness of artificial neural networks for first swing stability determination of practical systems , 1994 .

[9]  Hong Chen,et al.  Approximation capability to functions of several variables, nonlinear functionals, and operators by radial basis function neural networks , 1993, IEEE Trans. Neural Networks.

[10]  F. Aboytes,et al.  TRANSIENT STABILITY ASSESSMENT IN LONGITIJDINAL POWER SYSTEMS USING ARTIFICIAL NEURAL NETWORKS , 1996 .

[11]  Chong-Ho Choi,et al.  Input feature selection for classification problems , 2002, IEEE Trans. Neural Networks.

[12]  Jaydev Sharma,et al.  Comparison of feature selection techniques for ANN-based voltage estimation , 2000 .

[13]  A. A. Fouad,et al.  Application of artificial neural networks in power system security and vulnerability assessment , 1994 .

[14]  Khaleequr Rehman Niazi,et al.  Power system security evaluation using ANN: feature selection using divergence , 2004 .

[15]  Nicolaos B. Karayiannis,et al.  Growing radial basis neural networks: merging supervised and unsupervised learning with network growth techniques , 1997, IEEE Trans. Neural Networks.

[16]  Mohamed Mohandes,et al.  Radial basis function networks for contingency analysis of bulk power systems , 1999 .

[17]  C. Jensen,et al.  Power System Security Assessment Using Neural Networks: Feature Selection Using Fisher Discrimination , 2001, IEEE Power Engineering Review.

[18]  Dejan J. Sobajic,et al.  Combined use of unsupervised and supervised learning for dynamic security assessment , 1991 .

[19]  Dejan J. Sobajic,et al.  Neural-net based unstable machine identification using individual energy functions , 1991 .

[20]  C. N. Lu,et al.  Reliability Worth Assessment of High-Tech Industry , 2002, IEEE Power Engineering Review.