Artificial neural network model for voltage security based contingency ranking

The continual increase in demand for electrical energy and the tendency towards maximizing economic benefits in power transmission system has made real-time voltage security analysis an important issue in the operation of power system. The most important task in real time security analysis is the problem of identifying the critical contingencies from a large list of credible contingencies and rank them according to their severity. This paper presents an artificial neural network (ANN)-based approach for contingency ranking. A set of feed forward neural networks are developed to estimate the voltage stability level at different load conditions for the selected contingencies. Maximum L-index of the load buses in the system is taken as the indicator of voltage instability. A mutual information-based method is proposed to select the input features of the neural network. The effectiveness of the proposed method has been demonstrated through contingency ranking in IEEE 30-bus system. The performance of the developed model is compared with the unified neural network trained with the full feature set. Simulation results show that the proposed method takes less time for training and has good generalization abilities.