论文信息 - Power System Security Margin Prediction Using Radial Basis Function Networks

Power System Security Margin Prediction Using Radial Basis Function Networks

This paper presents a method to predict the postcontingency security margin using radial basis function networks with a fast training method. A genetic-based feature selection tool is developed to obtain the most predictive attributes for use in RBF networks. The proposed method is applied to a thermal overload problem for demonstration. The simulation results show that the proposed method gives satisfactory results and the running time decreases by a factor of 10 compared with using multilayer perceptrons.

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