Contingency ranking for voltage collapse using parallel self-organizing hierarchical neural network

On-line monitoring of the power system voltage security has become a vital factor for electric utilities. This paper proposes a voltage contingency ranking approach based on parallel selforganizing hierarchical neural network (PSHNN). Loadability margin to voltage collapse following a contingency has been used to rank the contingencies. PSHNN is a multi-stage neural network where the stages operate in parallel rather than in series during testing. The number of ANNs required is drastically reduced by adopting a clustering technique to group contingencies of similar severity into one cluster. Entropy based feature selection has been employed to reduce the dimensionality of the ANN. Once trained, the proposed ANN model is capable of ranking the voltage contingencies under varying load conditions, on line. The effectiveness of the proposed method has been demonstrated by applying it for contingency ranking of IEEE 30-bus system and a practical 75-bus Indian system.