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首页> 外文期刊>Electric Power Components and Systems >Optimal Allocation of DGs and Reconfiguration of Radial Distribution Systems Using an Intelligent Search-based TLBO
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Optimal Allocation of DGs and Reconfiguration of Radial Distribution Systems Using an Intelligent Search-based TLBO

机译：DG的最佳分配和使用基于智能搜索的TLBO重新配置径向分配系统

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This paper addresses an application of Teaching-Learning-Based Optimization method for the optimal allocation of Distributed Generations (DGs) in radial distribution systems. The problem is formulated to maximize annual energy loss reduction while maintaining better node voltage profiles using penalty function approach. A piecewise linear multi-level load pattern is considered, and the distribution network is reconfigured after optimal placement of DGs in the distribution network. A probability-based heuristic intelligent search (IS) is suggested to enhance the accuracy and convergence of the optimization techniques. IS directs optimization techniques to efficiently scan the problem search space in such a way that a fair candidature is available to all decision variables of the problem. It virtually squeezes the search space while maintaining adequate diversity in population. The proposed method is investigated on the benchmark IEEE 33-bus, 69-bus test distribution systems, and 83-bus real distribution system. The application results show that the proposed optimization methodology provides substantial improvement in convergence characteristics and quality of solutions.
机译：本文讨论了基于教学-学习的优化方法在径向分配系统中分布式发电（DG）最优分配的应用。使用惩罚函数方法制定了该问题，以最大程度地减少年度能量损失，同时保持更好的节点电压分布。考虑分段线性多级负载模式，在将DG最佳分配到配电网之后，重新配置配电网。建议基于概率的启发式智能搜索（IS）以提高优化技术的准确性和收敛性。 IS指导优化技术，以使问题的所有决策变量都能获得公平的候选人资格，从而有效地扫描问题搜索空间。它实际上压缩了搜索空间，同时保持了足够的人口多样性。在基准IEEE 33总线，69总线测试配电系统和83总线实际配电系统上研究了该方法。应用结果表明，所提出的优化方法在收敛特性和解质量方面提供了实质性的改进。

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