A new optimal under-frequency load-shedding method using hybrid culture-particle swarm optimization-co-evolutionary algorithm and artificial neural networks

Abstract

Optimum under-frequency load shedding during contingency situations is one of the most important issues in power system security analysis; if carried out online fast enough, it will prevent the system from going to a complete blackout. This article presents a new fast load-shedding method in which the amounts of active and reactive power to be shed are optimized with a dynamic priority list by using a hybrid culture-particle swarm optimization-co-evolutionary algorithm and artificial neural network method. The proposed method uses a five-step load-shedding scenario and is able to determine the necessary active and reactive load-shedding amounts in all steps simultaneously on a real-time basis. An artificial neural network database is established by using offline N - K (K = 1, 2, and 3) contingency analysis of the IEEE 118-bus test system. The Levenberg-Marquardt back-propagation training algorithm is used for the artificial neural network, and the training process is optimized by using a genetic algorithm. The artificial neural network database is updated based on new contingency events that occur in the system. The simulation results show that the proposed algorithm will give optimal load shedding for different N - K contingency scenarios in comparison with other available under-frequency load-shedding methods. © 2015 Taylor & Francis Group, LLC.