Generators Maintenance Schedule for WAPDA System using Meta-Heuristic Paradigms

This research work applies four different meta-heuristic techniques: genetic algorithm (GA), particle swarm optimization (PSO), Virtual bee algorithm (VBA) and harmony search algorithm (HS) to solve the complex power systems maintenance scheduling problem for water and power development authority (WAPDA), Pakistan. These algorithms are utilized to compute the optimal generators maintenance schedule (GMS) for economical and reliable operation of WAPDA power system, while satisfying maintenance window constraint, load demand and crew constraints. Also, the GMS objective function is evaluated by maximizing the minimum reserve margin during each generation. Meta-heuristic techniques are proposed in the first instance to solve the GMS problem to evolve optimal and feasible solutions. The effectiveness and comparisons of the performances of GA, PSO, VBA and HS algorithms for the GMS problem on the WAPDA system are presented