Artificial neural network based contingency ranking method for voltage collapse

The paper presents a neural network-based approach for contingency ranking of voltage collapse. By using the singular value decomposition method, a Radial Basis Function (RBF) neural network is trained to map the operating conditions of power systems to a voltage stability indicator and contingency severity indices corresponding to transmission lines. The advantages of RBF neural network are demonstrated by applying it to contingency ranking and voltage stability assessment. Tests carried out on the IEEE 57-bus system are reported.