Generating Unit Maintenance Scheduling using Hybrid PSO Algorithm

This paper addresses a hybrid particle swarm optimization-based approach for solving a generating unit maintenance scheduling problem (GMS). We focus on the power system reliability such as reserve ratio better than cost function as the objective function of GMS problem. It is shown that particle swarm optimization-based method is more effective in obtaining feasible schedules such as GMS problem related to power system planning and operation. In our paper, we find the optimal solution of the GMS problem within a specific time horizon using hybrid PSO algorithm, simple modification to the standard particle swarm optimization adding mutation operator of GA to solve the stagnation problem. Actual data obtained from the IEEE reliability test system (1996) including 32-generators are applicable to the GMS problem. From the result, we can conclude that the hybrid PSO is enough to look for the optimal solution effectively in the generating unit maintenance scheduling problem. It is also envisaged that hybrid PSO (HPSO) algorithm can be easily implemented for similar optimizations and scheduling problems in power system problems to obtain improved solutions.