A model based on SVM-GDPSO for the voltage stability forecasting of large power system

Abstract

The stability assessment of a large power system in real-time is very necessary after it encounters fault. The paper proposes a new model (SVM-GDPSO) for assessing the large power system. In order to enhance SVM, taking tangent vector of power flow Jacobian (PFJ) as the goal of machine learning was used for improving the precision. Besides, particle swarm optimization (PSO) with Gaussian disturbance (GD) is taken for setting the key parameters of SVM, and metalearning was utilized to decrease the search space of PSO. The experiment on the standard test system of IEEE 118-bus demonstrated that this model could reflect the status of large power system in time. Besides, the method could locate the fault area and rank the fault level by the observation of critical bus. The proposed method has the reliability rate 97.22 %, which is superior to the back propagation neural network (BPNN) and SVM-GA, as well as determines the fault area with the success rate of 96.61 %.