Hybrid: Particle Swarm Optimization–Genetic Algorithm and Particle Swarm Optimization–Shuffled Frog Leaping Algorithm for long-term generator maintenance scheduling |
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| Affiliation: | 1. Department of EEE, National Institute of Technology Puducherry, Karaikal 609 602, India;2. Department of EEE, Pondicherry Engineering College, Puducherry 605 014, India;1. Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan;2. Nanya Technology Corporation, New Taipei City, Taiwan;1. Section Transport Engineering and Logistics, Department of Maritime and Transport Technology, Faculty of Mechanical, Marine and Materials Engineering, Delft University of Technology, Mekelweg 2, 2628 CD Delft, The Netherlands;2. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, No.3 ShangYuanCun, HaiDian District, Beijing 100044, China;1. Department of Electrical Engineering, Siksha ‘O’ Anusandhan University, Bhubaneswar 751030, Odisha, India;2. Department of Electrical & Electronics Engineering, VSSUT, Burla 768018, Odisha, India;1. Department of Electrical and Electronics Engineering, Anna University – University College of Engineering Dindigul, India;2. Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, India |
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| Abstract: | This paper presents a Hybrid Particle Swarm Optimization based Genetic Algorithm and Hybrid Particle Swarm Optimization based Shuffled Frog Leaping Algorithm for solving long-term generation maintenance scheduling problem. In power system, maintenance scheduling is being done upon the technical requirements of power plants and preserving the grid reliability. The objective function is to sell electricity as much as possible according to the market clearing price forecast. While in power system, technical viewpoints and system reliability are taken into consideration in maintenance scheduling with respect to the economical viewpoint. It will consider security constrained model for preventive Maintenance scheduling such as generation capacity, duration of maintenance, maintenance continuity, spinning reserve and reliability index are being taken into account. The proposed hybrid methods are applied to an IEEE test system consist of 24 buses with 32 thermal generating units. |
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| Keywords: | Generation maintenance schedule Optimization Shuffled Frog Leaping Algorithm Hybrid Particle Swarm Optimization–Genetic Algorithm and Particle Swarm Optimization–Shuffled Frog Leaping Algorithm |
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