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A novel approach based on cuckoo search for DG allocation in distribution network
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. Alternate Hydro Energy Centre, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India;2. Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India;1. Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia;2. UM Power Energy Dedicated Advanced Centre (UMPEDAC) Level 4, Wisma R&D, University of Malaya, Jalan Pantai Baharu, 59990 Kuala Lumpur, Malaysia;1. Electric Power and Machine Department, Faculty of Engineering, Zagazig University, Zagazig, Egypt;2. Electric Power and Machine Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt;1. Department of Power Systems, Ho Chi Minh City University of Technology, VNU-HCM, Ho Chi Minh City, Viet Nam;2. Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Malaysia
Abstract:This paper presents a novel approach based on cuckoo search (CS) which is applied for optimal distributed generation (DG) allocation to improve voltage profile and reduce power loss of the distribution network. The voltage profile which is the main criterion for power quality improvement is indicated by two indices: voltage deviations from the target value which must be minimized and voltage variations from the initial network without DG which must be maximized. The CS was inspired by the obligate brood parasitism of some cuckoo species by putting their eggs in the nests of other species. Some host birds can engage direct contest with the infringing cuckoos. For example, if a host bird detects the eggs are not their own, it will either throw these alien eggs away. The CS has been compared with other evolutionary algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) and different cases have been investigated for indicating the applicability of the proposed algorithm. The results indicate the better performance of CS compared with other methods due to the fewer parameters which must be well-tuned in this method. In addition, in this method the convergence rate is not sensitive to the parameters used, so the fine adjustment is not needed for any given problems.
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