Krill herd algorithm for optimal location of distributed generator in radial distribution system |
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| Affiliation: | 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 Electrical Engineering, Baba Hira Singh Bhattal Institute of Engineering and Technology, Lehragaga 148031, District Sangrur, Punjab, India;2. Department of Electrical Engineering, Indian Institute of Technology, Roorkee, Roorkee 247667, India;1. Electric Power and Machine Department, Faculty of Engineering, Zagazig University, Zagazig, 44519, Egypt;2. Electrical Department, Faculty of Engineering, Jazan University, Kingdom of Saudi Arabia;3. Electric Power and Machine Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt;1. School of Electrical Engineering, VIT University, Vellore 632014, Tamil Nadu, India;2. Director Research, Wainganga College of Engineering & Management, Nagpur, India |
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| Abstract: | Distributed generator (DG) is recognized as a viable solution for controlling line losses, bus voltage, voltage stability, etc. and represents a new era for distribution systems. This paper focuses on developing an approach for placement of DG in order to minimize the active power loss and energy loss of distribution lines while maintaining bus voltage and voltage stability index within specified limits of a given power system. The optimization is carried out on the basis of optimal location and optimal size of DG. This paper developed a new, efficient and novel krill herd algorithm (KHA) method for solving the optimal DG allocation problem of distribution networks. To test the feasibility and effectiveness, the proposed KH algorithm is tested on standard 33-bus, 69-bus and 118-bus radial distribution networks. The simulation results indicate that installing DG in the optimal location can significantly reduce the power loss of distributed power system. Moreover, the numerical results, compared with other stochastic search algorithms like genetic algorithm (GA), particle swarm optimization (PSO), combined GA and PSO (GA/PSO) and loss sensitivity factor simulated annealing (LSFSA), show that KHA could find better quality solutions. |
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| Keywords: | Radial distribution system Distributed generators Loss reduction Evolutionary algorithms Krill herd algorithm Differential evolution |
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