Large scale economic dispatch of power systems using oppositional invasive weed optimization |
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| Affiliation: | 1. Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, P.O. Box 50, Al-Huson, Irbid, Jordan;2. Department of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza, Palestine;3. School of Computer Sciences, Universiti Sains Malaysia (USM), Pulau Pinang, Malaysia;4. Department of Computer Science, Faculty of Pure and Applied Sciences, Federal University Wukari, P. M. B. 1029, Wukari, Taraba State, Nigeria;1. Department of Electronics and Electrical Engineering, Shiraz University of Technology, Shiraz, Iran;2. Department of Materials and Polymers Engineering, Hakim Sabzevari University, Sabzevar, Iran;1. Department of Electrical and Electronics Engineering, Technology Faculty, Duzce University, Duzce, Turkey;2. Department of Electrical Engineering, Engineering Faculty, Kocaeli University, Kocaeli, Turkey;3. Department of Electrical and Electronics Engineering, Engineering Faculty, Karadeniz Technical University, 61080 Trabzon, Turkey;1. School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, Jiangsu 221116, PR China;2. Graduate School of Business and Law, RMIT University, 379-405 Russell St, Melbourne, VIC 3000, Australia;3. School of Computer Science & Technology, Jiangsu Normal University, Xuzhou, Jiangsu 221116, PR China;4. School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China;5. School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou, Guangdong 510006, PR China |
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| Abstract: | This paper presents an evolutionary hybrid algorithm of invasive weed optimization (IWO) merged with oppositional based learning to solve the large scale economic load dispatch (ELD) problems. The oppositional invasive weed optimization (OIWO) is based on the colonizing behavior of weed plants and empowered by quasi opposite numbers. The proposed OIWO methodology has been developed to minimize the total generation cost by satisfying several constraints such as generation limits, load demand, valve point loading effect, multi-fuel options and transmission losses. The proposed algorithm is tested and validated using five different test systems. The most important merit of the proposed methodology is high accuracy and good convergence characteristics and robustness to solve ELD problems. The simulation results of the proposed OIWO algorithm show its applicability and superiority when compared with the results of other tested algorithms such as oppositional real coded chemical reaction, shuffled differential evolution, biogeography based optimization, improved coordinated aggregation based PSO, quantum-inspired particle swarm optimization, hybrid quantum mechanics inspired particle swarm optimization, modified shuffled frog leaping algorithm with genetic algorithm, simulated annealing based optimization and estimation of distribution and differential evolution algorithm. |
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| Keywords: | Economic load dispatch Invasive weeds optimization Opposition based learning Seeds Fitness Valve point loading |
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