Oppositional krill herd algorithm for optimal location of capacitor with reconfiguration in radial distribution system |
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| Affiliation: | 1. Department of Electrical Engineering, Dr. B C Roy Engineering College, Durgapur, West Bengal, India;2. Department of Electrical Engineering, Jalpaiguri Government Engineering College, Jalpaiguri, West Bengal, India;1. Department of Electronics and Communication Engineering, NIT Durgapur, West Bengal, India;2. Department of Electrical Engineering, NIT Durgapur, West Bengal, India;3. Department of Electrical Engineering, ISM, Dhanbad, India;1. EEE / SEEE, SASTRA University, Tirumalaisamudram, Thanjavur, Tamilnadu, India;2. EIE / SEEE, SASTRA University, Tirumalaisamudram, Thanjavur, Tamilnadu, India;1. College of Equipment Engineering, Engineering University of Armed Police Force, Xi’an 710086, China;2. China Satellite Maritime Tracking and Control Department, Jiangyin 214431, China;3. College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China;1. Department of Electrical Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran;2. Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran;1. Electrical Engineering Department, Golpayegan University of Technology, Golpayegan, Iran;2. Electrical Engineering Department, University of Jiroft, Jiroft, Iran;3. Electrical Engineering Department, Imam Khomeini International University, Iran |
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| Abstract: | The delivery of power from sources to the consumer points is always accompanied of power losses. Basically, active losses in distribution systems can be reduced by optimal reconfigurations of the network. Optimal capacitor allocation problem in reconfigured distribution network is a challenge of researchers for several decades. This paper presents a computationally efficient methodology namely, krill herd (KH) algorithm to find optimal location of capacitor and optimal reconfiguration in order to minimize real power loss of radial distribution systems. Moreover, the opposition based learning (OBL) concept is integrated with KH algorithm for improving the convergence speed and simulation results. In order to show the usefulness and supremacy, the conventional KH and proposed oppositional KH (OKH) algorithms are tested on 33-bus and 69-bus radial distribution networks. The simulation results of the proposed methods are compared with fuzzy multi-objective approach and non dominated sorting genetic algorithm (NSGA). The solution results show that OKH technique could generate better quality solutions and better convergence characteristics than those obtained by conventional KH algorithm and other existing optimization techniques available in the literature. Results also show the robustness of the proposed methodology to solve reconfigured distribution network (RDN) problems. |
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| Keywords: | Radial distribution system Reconfiguration Power loss reduction Krill herd algorithm Oppositional krill herd algorithm |
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