On-line Voltage and Power Flow Contingencies Rankings Using Enhanced Radial Basis Function Neural Network and Kernel Principal Component Analysis

Timely and accurate assessment of voltage and power flow security is necessary to detect post-contingency problems in order to prevent a large-scale blackout. This article presents an enhanced radial basis function neural network based on a modified training algorithm for on-line ranking of the contingencies expected to cause steady-state bus voltage and power flow violations. Hidden layer neurons have been selected with the proposed algorithm, which has the advantage of being able to automatically choose optimal centers and radii. The proposed radial basis function neural network based security assessment algorithm has very small training time and space in comparison with multi-layer perceptron neural networks, support vector machines, and other machine learning based algorithms. A feature extraction technique based on kernel principal component analysis has been employed to identify the relevant inputs for the neural network. Also, the proposed feature extraction algorithm has been compared with Fisher-like criterion, the class separability index, and the correlation coefficient technique. The competence of the proposed approaches has been demonstrated on IEEE 14-bus and IEEE 118-bus power systems. The simulation results show the effectiveness and the stability of the proposed scheme for on-line voltage and power flow contingencies ranking procedures of large-scale power systems.