Hybrid Intelligent Techniques for Generator Maintenance Scheduling
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As the world-wide consumption of electricity continually increases, more and more pressure is put on the capabilities of power generating systems to maintain their levels of power provision. The electricity utility companies operating these power systems are faced with numerous challenges with respect to ensuring reliable electricity supply at cost-effective rates. One of these challenges concerns the planned preventative maintenance of a utility's power generating units. This book shows different algorithms for solving the generator maintenance scheduling through hybrid intelligent techniques...
As the world-wide consumption of electricity continually increases, more and more pressure is put on the capabilities of power generating systems to maintain their levels of power provision. The electricity utility companies operating these power systems are faced with numerous challenges with respect to ensuring reliable electricity supply at cost-effective rates. One of these challenges concerns the planned preventative maintenance of a utility's power generating units. This book shows different algorithms for solving the generator maintenance scheduling through hybrid intelligent techniques. In the solution algorithm, Hybrid Particle Swarm Optimization based Genetic Algorithm (PSO-GA), Hybrid Particle Swarm Optimization based Evolutionary Programming (PSO-EP) and Hybrid Particle Swarm Optimization based Shuffled Frog Leaping Algorithm (PSO-SFLA) methods are developed by incorporating constraints on maintenance windows, the meeting of load demand together with a safety margin, the availability of maintenance crew and general exclusion constraints. The generator maintenance scheduling s modelled by adopting a reliability optimal criterion, the goal of which is to level the reserve
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