Online Power System Contingency Screening and Ranking Methods Using Radial Basis Neural Networks

This paper presents a supervising learning approach using Multilayer Feed Forward Neural Network(MFFN) and Radial Basis Fuction Neural Network(RBFN) to deal with fast and accurate static security assessment (SSA) and contingency analysis of a large electric power systems. The degree of severity of contingencies is measured by two scalar performance indices (PIs): Voltage-reactive power performance index, PIVQ and line MVA performance index, PIMVA. For each (N-1) contingency, thePerformance Index (PI) is computed using the Newton Raphson (NR) method. A correlation coefficient feature selection technique has been utilized to identify the inputs for the MFFN and RBFN. The proposed method has been applied on an IEEE 39-bus New England test system at different operating conditions comparing to single line outage and the results demonstrate the suitability of the methodology for on-line power system security assessment at Energy Management Center. The performace of the proposed ANN models is compared withNewton Raphson (NR) method and the results shows that the proposed model is effective and reliable in terms of static security assessment of power systems.