Application of artificial neural networks in voltage stability assessment

Voltage stability problems have been one of the major concerns for electric utilities as a result of system heavy loading. This paper reports on an investigation on the application of ANNs in voltage stability assessment. A multilayer feedforward artificial neural network (ANN) with error backpropagation learning is proposed for calculation of voltage stability margins (VSM). Based on the energy method, a direct mapping relation between power system loading conditions and the VSMs is set up via the ANN. A systematic method for selecting the ANN´s input variables was developed using sensitivity analysis. The effects of ANN´s training pattern sensitivity problems were also studied by dividing system operating conditions into several loading levels based on sensitivity analysis. Extensive testing of the proposed ANN-based approach indicate its viability for power system voltage stability assessment. Simulation results on five test systems are reported in the paper.