A modified particle swarm optimization for economic dispatch with non-smooth cost functions |
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| Authors: | Mehdi Neyestani Malihe M. Farsangi Hossein Nezamabadi-pour |
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| Affiliation: | 1. Department of Electrical and Computer Engineering, Safashahr Branch, Islamic Azad University, Safashahr, Iran;2. Intelligent Systems Research (CISR), Deakin University, Waurn Ponds Campus, Geelong, VIC 3217, Australia;1. Department of Electrical Engineering, Kalyani Government Engineering College, Kalyani, West Bengal, India;2. Department of Electrical Engineering, Dr. BC Roy Engineering College, Durgapur, West Bengal, India;3. Department of Electrical Engineering, Indian School of Mines, Dhanbad, Jharkhand, India;1. Department of Electrical and Electronics Engineering, Sir C. R. Reddy College of Engineering, Eluru-534007, Andhra Pradesh, India;2. Department of Electrical Engineering, AU College of Engineering, Andhra University, Visakhapatnam-530003, Andhra Pradesh, India;1. B.C. Roy Engineering College, Durgapur, West Bengal 713206, India;2. National Institute of Technology-Agartala, Tripura 799055, India;3. Department of Electrical Engineering, Jadavpur University, Kolkata, West Bengal 700 032, India |
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| Abstract: | This paper presents a new approach to economic dispatch (ED) problems with non-smooth cost functions using a particle swarm optimization (PSO) technique. The practical ED problems have non-smooth cost functions with equality and inequality constraints, which makes the problem of finding the global optimum difficult when using any mathematical approaches. Since, standard PSO may converge at the early stage, in this paper, a modified PSO (MPSO) mechanism is suggested to deal with the equality and inequality constraints in the ED problems. To validate the results obtained by MPSO, standard particle swarm optimization (PSO) and guaranteed convergence particle swarm optimization (GCPSO) are applied for comparison. Also, the results obtained by MPSO, PSO and GCPSO are compared with the previous approaches reported in the literature. The results show that the MPSO produces optimal or nearly optimal solutions for the study systems. |
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