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DG placement in radial distribution network by symbiotic organisms search algorithm for real power loss minimization
Affiliation:1. Department of Electrical Engineering, Govt. College of Engineering and Textile Technology, Berhampore, West Bengal, India;2. Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, India;3. Department of Electrical Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India;1. Department of Electrical Power Engineering, Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, Malaysia;2. NED University of Engineering and Technology, Karachi, Pakistan;1. Department of EEE, National College of Engineering, Tirunelveli 627151, India;2. Department of EEE, Thiagarajar College of Engineering, Madurai 625015, India
Abstract:Incorporation of distributed generation (DG) in distribution network may reduce the network loss if DG of appropriate size is placed at proper strategic location. The current article presents determination of optimal size and location of DG in radial distribution network (RDN) for the reduction of network loss considering deterministic load demand and DG generation using symbiotic organisms search (SOS) algorithm. SOS algorithm is a meta-heuristic technique, inspired by the symbiotic relationship between different biological species. In this paper, optimal size and location of DG are obtained for two different RDNs (such as, 33-bus and 69-bus distribution networks). The obtained results, using the proposed SOS, are compared to the results offered by some other optimization algorithms like particle swarm optimization, teaching-learning based optimization, cuckoo search, artificial bee colony, gravitational search algorithm and stochastic fractal search. The comparison is done based on minimum loss of the distribution network as well as based on the convergence mobility of the fitness function offered by each of the comparative algorithms for both the networks under consideration. It is established that the proposed SOS algorithm offers better result as compared to other optimization algorithms under consideration. The results are also compared to the existing solution available in the literature.
Keywords:Artificial bee colony (ABC)  Cuckoo search (CS)  Distributed generation (DG)  Particle swarm optimization (PSO)  Symbiotic organisms search (SOS)
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