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There is a need to increase the distributed generation (DGs) penetration into the distribution network to meet the increasing energy consumption. Line/Power losses and voltage profile maintenance are the most significant restrictions of the existing power system. Random DGs penetration also causes many problems in the distribution system like system islanding, relay tripping, reverse power flow, etc. To avoid such issues, to reduce power losses and to enhance voltage profile penetration of optimal sized distributed generations at optimal locations in the distribution network is required. These…mehr

Produktbeschreibung
There is a need to increase the distributed generation (DGs) penetration into the distribution network to meet the increasing energy consumption. Line/Power losses and voltage profile maintenance are the most significant restrictions of the existing power system. Random DGs penetration also causes many problems in the distribution system like system islanding, relay tripping, reverse power flow, etc. To avoid such issues, to reduce power losses and to enhance voltage profile penetration of optimal sized distributed generations at optimal locations in the distribution network is required. These distribution networks will be the IEEE 33 bus and IEEE 69 bus radial distribution networks. Adaptive schemes based on adaptive neuro-fuzzy inference system, biogeography based optimization and particle swarm optimization techniques will do this optimal sizing and penetration of DGs for minimizing the power loss, enhancing the voltage profile and fitness rate.
Autorenporträt
Dr. Amandeep Gill is Assistant Professor in the Department of Electrical Engineering at JECRC University, Jaipur, Rajasthan, India. He is PhD, M.E, and B.E in EE. His research fields are distributed generations, smart grids, power systems, machine learning etc. He had his research publications in the Scopus/Sci-e and reputed international journals.