Title :
Comparison of DE and PSO for generator maintenance scheduling
Author :
Yare, Y. ; Venayagamoorthy, G.K.
Author_Institution :
Dept. of Electr. & Comput. Eng., Missouri Univ. of Sci. & Technol., Rolla, MO
Abstract :
This paper presents a comparison of a differential evolution (DE) algorithm and a modified discrete particle swarm optimization (MDPSO) algorithm for generating optimal preventive maintenance schedules for economical and reliable operation of a power system, while satisfying system load demand and crew constraints. The DE, an evolutionary technique and an optimization algorithm utilizes the differential information to guide its further search, and can handle mixed integer discrete continuous optimization problems. Discrete particle swarm optimization (DPSO) is known to effectively solve large scale multi-objective optimization problems and has been widely applied in power systems. Both the DE and MDPSO are applied to solve a multi-objective generator maintenance scheduling (GMS) optimization problem. The two algorithms generate feasible and optimal solutions and overcome the limitations of the conventional methods including extensive computational effort, which increases exponentially as the size of the problem increases. The proposed methods are tested, validated and compared on the Nigerian power system.
Keywords :
electric generators; evolutionary computation; integer programming; particle swarm optimisation; power system management; power system measurement; preventive maintenance; scheduling; GMS optimization problem; Nigerian power system; crew constraints; differential evolution; evolutionary technique; large scale multiobjective optimization; mixed integer discrete continuous optimization problem; modified discrete particle swarm optimization; multiobjective generator maintenance scheduling; optimal preventive maintenance schedules; optimization algorithm; system load demand; Large-scale systems; Particle swarm optimization; Power generation; Power generation economics; Power system economics; Power system reliability; Power systems; Preventive maintenance; Processor scheduling; Scheduling algorithm;
Conference_Titel :
Swarm Intelligence Symposium, 2008. SIS 2008. IEEE
Conference_Location :
St. Louis, MO
Print_ISBN :
978-1-4244-2704-8
Electronic_ISBN :
978-1-4244-2705-5
DOI :
10.1109/SIS.2008.4668285