Post-fault transient stability status prediction using Grey Wolf and Particle Swarm Optimization

Two methods are introduced for predicting the voltage and rotor angle stability status of a power system instantly after a large disturbance. Generator voltages and angles gathered instantly after clearing a fault are used as inputs of Feedforward Neural Networks (FNNs) to classify stability status of a system. Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) are applied as training methods for Feedforward Neural Networks. The performance of two methods are determined by applying the two methods on the IEEE 39 bus test system. The resulting accuracy of GWO and PSO are compared. Examination showed that the applied methods can predict the stability status 30 cycles after fault clearance. It is also shown that the FNN trained with the GWO has better classification accuracy than FNN trained with PSO. Additionally, the robustness of the two methods was inspected by determining their performance in predicting stability when the network topology is changed.