Hybrid: Particle Swarm Optimization–Genetic Algorithm and Particle Swarm Optimization–Shuffled Frog Leaping Algorithm for long-term generator maintenance scheduling

• Published in: International journal of electrical power & energy systems Vol. 65; pp. 432 - 442
• Main Authors: Giftson Samuel, G., Christober Asir Rajan, C.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2015
• Subjects: Algorithms; Buses (vehicles); Electric power generation; Frogs; Generation maintenance schedule; Hybrid Particle Swarm Optimization–Genetic Algorithm and Particle Swarm Optimization–Shuffled Frog Leaping Algorithm; Maintenance; Maintenance management; Mathematical models; Optimization; Shuffled Frog Leaping Algorithm; Swarm intelligence; Generation maintenance schedule; Hybrid Particle Swarm Optimization–Genetic Algorithm and Particle Swarm Optimization–Shuffled Frog Leaping Algorithm; Optimization; Shuffled Frog Leaping Algorithm
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2014.10.042

•We developed new hybrid evolutionary algorithm for solving generator maintenance scheduling problem.•Hybrid optimization method balance overall reliability and economy.•A case study of 32 thermal generating units reveal the effectiveness of the hybrid method. This paper presents a Hybrid Particle Swarm Optimization based Genetic Algorithm and Hybrid Particle Swarm Optimization based Shuffled Frog Leaping Algorithm for solving long-term generation maintenance scheduling problem. In power system, maintenance scheduling is being done upon the technical requirements of power plants and preserving the grid reliability. The objective function is to sell electricity as much as possible according to the market clearing price forecast. While in power system, technical viewpoints and system reliability are taken into consideration in maintenance scheduling with respect to the economical viewpoint. It will consider security constrained model for preventive Maintenance scheduling such as generation capacity, duration of maintenance, maintenance continuity, spinning reserve and reliability index are being taken into account. The proposed hybrid methods are applied to an IEEE test system consist of 24 buses with 32 thermal generating units.