A review of generator maintenance scheduling using artificial intelligence techniques

Abstract

New Artificial Intelligence (AI) approaches such as simulated annealing, genetic algorithms, simulated evolution, neural networks, tabu search, fuzzy logic and their hybrid techniques have been applied in recent years to solving Generator Maintenance Scheduling (GMS) problems. This paper presents a review of these AI approaches for the GMS problem. The formulation of problems and the methodologies of solution are discussed and analysed. A case study is also included which presents the application of a genetic algorithm to a test system based on a practical power system scenario.