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<p>Distributed generation (DG) sources are being installed in distribution networks worldwide due to their numerous advantages over the conventional sources which include operational and economical benefits. Random placement of DG sources in a distribution network will result in adverse effects such as increased power loss, loss of voltage stability and reliability, increase in operational costs, power quality issues etc. This paper presents a methodology to obtain the optimal location for the placement of multiple DG sources in a distribution network from a technical perspective. Optimal location is obtained by evaluating a global multi-objective technical index (MOTI) using a weighted sum method. Clonal selection based artificial immune system (AIS) is used along with optimal power flow (OPF) technique to obtain the solution. The proposed method is executed on a standard IEEE-33 bus radial distribution system. The results justify the choice of AIS and the use of MOTI in optimal siting of DG sources which improves the distribution system efficiency to a great extent in terms of reduced real and reactive power losses, improved voltage profile and voltage stability. Solutions obtained using AIS are compared with Genetic algorithm (GA) and Particle Swarm optimization (PSO) solutions for the same objective function.</p>
Optimization, Artificial intelligence, Distribution Systems, Distributed Power Generation, Distributed Generation, Quantum mechanics, Reliability engineering, Electric power system, Engineering, Machine learning, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Demand Response in Smart Grids, Electrical and Electronic Engineering, Optimal siting, Stability (learning theory), Optimal Power Flow, Multi-objective, Artificial immune system, Particle swarm optimization, Physics, Mathematical optimization, Voltage, Power (physics), Computer science, Integration of Distributed Generation in Power Systems, Algorithm, Reliability (semiconductor), Genetic algorithm, Control and Systems Engineering, Electrical engineering, Physical Sciences, Control and Synchronization in Microgrid Systems, Distributed generation, Optimal power flow, Mathematics
Optimization, Artificial intelligence, Distribution Systems, Distributed Power Generation, Distributed Generation, Quantum mechanics, Reliability engineering, Electric power system, Engineering, Machine learning, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Demand Response in Smart Grids, Electrical and Electronic Engineering, Optimal siting, Stability (learning theory), Optimal Power Flow, Multi-objective, Artificial immune system, Particle swarm optimization, Physics, Mathematical optimization, Voltage, Power (physics), Computer science, Integration of Distributed Generation in Power Systems, Algorithm, Reliability (semiconductor), Genetic algorithm, Control and Systems Engineering, Electrical engineering, Physical Sciences, Control and Synchronization in Microgrid Systems, Distributed generation, Optimal power flow, Mathematics
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