Hybrid Particle Swarm Optimization — Genetic algorithm and Particle Swarm Optimization — Evolutionary programming for long-term generation maintenance scheduling

This paper discuss a Hybrid Particle Swarm Optimization — Genetic Algorithm and Particle Swarm Optimization — Evolutionary Programming to Long-term Generation Maintenance Scheduling to Enhance the Reliability of the units. Maintenance scheduling establishes the outage time scheduling of units in a particular time horizon. In a monopolistic power system, maintenance scheduling is being done upon the technical requirements of power plants and preserving the grid reliability. While in power system, technical viewpoints and system reliability are taken into consideration in maintenance scheduling with respect to the economical viewpoint. In this paper present a Hybrid Particle Swarm Optimization — Genetic Algorithm and Particle Swarm Optimization — Evolutionary Programming methodology for finding the optimum preventive maintenance scheduling of generating units in power system. The objective function is to maintain the units as earlier as possible. Varies constrains such as spinning reserve, duration of maintenance and maintenance crew are being taken into account. In case study, IEEE test system consist of 32 generating units is used.