Multi-Leader Particle Swarm Optimization for Optimal Planning of Distributed Generation
In today's world, Distributed Generation (DG) has become an outstanding solution to cater to power system challenges caused due to the exponential growth of load demand. Many researchers have used various optimization techniques for the optimal planning of location and the size of the DGs. Howe...
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| フォーマット: | Conference Paper |
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Institute of Electrical and Electronics Engineers Inc.
2023
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| 要約: | In today's world, Distributed Generation (DG) has become an outstanding solution to cater to power system challenges caused due to the exponential growth of load demand. Many researchers have used various optimization techniques for the optimal planning of location and the size of the DGs. However, premature convergence, precision of the output and complexity are few major drawbacks of these optimization techniques. In this paper, Multi-Leader Particle Swarm Optimization (MLPSO) is utilized to determine the optimal locations and sizes of DGs with the intention of active power loss minimization. Thus, the primary drawback of premature convergence in existing optimization techniques is suppressed. A comprehensive performance analysis is carried out on IEEE 33 bus system. The findings reveal a 67.40% reduction of loss by integrating three DGs with unity power factor. The comparison of the results with other optimization techniques has demonstrated the effectiveness of MLPSO Algorithm. � 2020 IEEE. |
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