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.

Keywords:

• under-frequency load shedding
• hybrid culture–particle swarm optimization–co-evolutionary algorithm
• artificial neural network
• genetic algorithm optimization

Additional information

Notes on contributors

Majid Moazzami

Majid Moazzami was born in Golpayegan, Iran. He received his B.Sc. and M.Sc. in electrical engineering from Islamic Azad University of Najafabad (IAUN), Iran, in 2004 and 2007, respectively, and his Ph.D. from Electrical Engineering Department, University of Isfahan, Iran, in 2013. He is currently an assistant professor and the head of the power group in Electrical Engineering Department of IAUN. His main areas of research interests are power system dynamics, restructured power systems and electricity markets, renewable energy, and microgrids.

Amin Khodabakhshian

Amin Khodabakhshian was born in Isfahan, Iran. He received his B.Sc. and M.Sc. in electrical engineering from University of Tehran, Tehran, Iran, in 1986 and 1988, respectively, and his Ph.D. in 1995 from University of Wollongong, Australia. He is currently an associate professor in the Electrical Engineering Department, University of Isfahan, Isfahan, Iran. His research interests include power system dynamic stability, control applications in power systems, and dispersed generations.

Rahmat-Allah Hooshmand

Rahmat-Allah Hooshmand was born in Isfahan, Iran. He received his B.Sc. from University of Mashhad in 1989; his M.Sc. from University of Tehran, Iran, in 1990; and his Ph.D. from Tarbiat Modarres University, Iran, in 1995, all in electrical engineering. He is currently a professor in the Electrical Engineering Department, University of Isfahan, Isfahan, Iran. His main areas of research interests are modeling power systems and distribution networks.