Stochastic model based reliability centered preventive generator maintenance planning using Ant Lion Optimizer

Abstract

Regular maintenance of equipments improves their lifespan as well as performance. In order to enhance the overall performance of the power systems, preventive maintenance scheduling of generating units are essential. The stochastic model which includes the forced outage rates of units and this reliability model reflects the risk associated with the maintenance schedules. Thus, the probabilistic approach for reliability evaluation is more consistent than deterministic ones. The main purpose of this paper is to propose a promising tool to address the preventive generator maintenance scheduling problems considering reliability issues. Inspiring foraging behavior of ant lions in nature, Ant Lion Optimizer (ALO) is developed and is chosen as the prime optimization tool for solving the stochastic model based generator maintenance scheduling problem. The intended ALO algorithm is implemented on the standard test systems including IEEE-30 bus, RTS-9 unit, 21-unit and IEEE-32 unit. Numerical results comparisons validate that the ALO is a promising alternative for solving stochastic preventive GMS problems.