4 research outputs found
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Generational and steady state genetic algorithms for generator maintenance scheduling problems
The aim of generator maintenance scheduling
(GMS) in an electric power system is to allocate a proper
maintenance timetable for generators while maintaining a high
system reliability, reducing total production cost, extending
generator life time etc. In order to solve this complex problem
a genetic algorithm technique is proposed here. The paper
discusses the implementation of GAs to GMS problems with
two approaches: generational and steady state. The results of
applying these GAs to a test GMS problem based on a
practical power system scenario are presented and analysed.
The effect of different GA parameters is also studie
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A review of generator maintenance scheduling using artificial intelligence techniques
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