Transient stability preventive control of power systems using chaotic particle swarm optimization combined with two-stage support vector machine

This paper presents a chaotic particle swarm optimization (CPSO) algorithm combined with data mining method for transient stability preventive control. The data mining method is utilized to approximate the security region considering transient stability. Therefore, the application effects of different input features and data-mining classifiers are compared first. Then, a two-stage support vector machine (SVM) approach is proposed to generate two models, including a linear SVM model with controllable features provides preventive adjustment rules, and a more accurate SVM model to approximate the actual security region. Finally, the CPSO in combination with the two-stage SVM is proposed to calculate the optimal preventive control strategies. Comprehensive studies are conducted on a 16-machine 68-bus system and 48-machine 140-bus system to verify the effectiveness.