Application of computational intelligence techniques for load shedding in power systems: A review

作者：Laghari J A^*; Mokhlis H; Bakar A H A; Mohamad Hasmaini

来源：Energy Conversion and Management, 2013, 75: 130-140.

DOI：10.1016/j.enconman.2013.06.010

• Load shedding
• Artificial neural network
• Fuzzy logic control
• Adaptive neuro-fuzzy inference system
• Genetic algorithm
• Particle swarm optimization

Recent blackouts around the world question the reliability of conventional and adaptive load shedding techniques in avoiding such power outages. To address this issue, reliable techniques are required to provide fast and accurate load shedding to prevent collapse in the power system. Computational intelligence techniques, due to their robustness and flexibility in dealing with complex non-linear systems, could be an option in addressing this problem. Computational intelligence includes techniques like artificial neural networks, genetic algorithms, fuzzy logic control, adaptive neuro-fuzzy inference system, and particle swarm optimization. Research in these techniques is being undertaken in order to discover means for more efficient and reliable load shedding. This paper provides an overview of these techniques as applied to load shedding in a power system. This paper also compares the advantages of computational intelligence techniques over conventional load shedding techniques. Finally, this paper discusses the limitation of computational intelligence techniques, which restricts their usage in load shedding in real time.

• 出版日期2013-11