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Reconfiguration of Distribution Networks with Distributed Generation Using a Dual Hybrid Particle Swarm Optimization Algorithm

机译:使用双重混合粒子群算法对分布式发电的配电网进行重构

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摘要

This paper proposes a reconfiguration strategy of distribution network with distribution generation (DG) based on dual hybrid particle swarm optimization algorithm. By the network structure simplification and branches grouping, network loss was selected as objective function, an improved binary particle swarm optimization algorithm (IBPSO) was used in branch group search, and the proposed group binary particle swarm optimization search algorithm was used in searching within the group to improve search efficiency and avoid early maturing. The proposed algorithm was tested on the IEEE 33-bus distribution power system and compared with other existing literature methods. The influence on the power flow of distribution network by DG position and capacity was studied. Simulation results illustrate that the proposed algorithm can get the optimal configuration results and significantly reduce system energy losses with fast convergence rate. In order to control the smart grid, using a dual hybrid particle swarm optimization algorithm to reconstruct a model, the result of simulation verifies the validity of the model. At the same time, the distributed power grid after reconstruction after optimization can effectively reduce the network loss and improve power supply quality.
机译:提出了一种基于双重混合粒子群优化算法的配电网配电网重构策略。通过简化网络结构和分支分组,选择网络损耗作为目标函数,在分支群搜索中使用了改进的二进制粒子群优化算法(IBPSO),在树内搜索中使用了提出的群二进制粒子群优化搜索算法。小组以提高搜索效率并避免过早成熟。该算法在IEEE 33总线配电系统上进行了测试,并与其他现有文献方法进行了比较。研究了DG的位置和容量对配电网潮流的影响。仿真结果表明,该算法能获得最优的配置结果,并以较快的收敛速度显着降低系统的能量损耗。为了控制智能电网,采用双重混合粒子群优化算法重建模型,仿真结果验证了模型的有效性。同时,优化改造后的分布式电网可以有效减少网络损耗,提高供电质量。

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  • 来源
    《Mathematical Problems in Engineering》 |2017年第7期|1517435.1-1517435.10|共10页
  • 作者单位

    China Univ Min & Technol, Sch Elect & Power Engn, Xuzhou 221116, Jiangsu, Peoples R China|China Univ Min & Technol, Jiangsu Prov Lab Elect & Automat Engn Coal Min, Xuzhou 221116, Jiangsu, Peoples R China;

    China Univ Min & Technol, Sch Elect & Power Engn, Xuzhou 221116, Jiangsu, Peoples R China|China Univ Min & Technol, Jiangsu Prov Lab Elect & Automat Engn Coal Min, Xuzhou 221116, Jiangsu, Peoples R China;

    China Univ Min & Technol, Sch Elect & Power Engn, Xuzhou 221116, Jiangsu, Peoples R China|China Univ Min & Technol, Jiangsu Prov Lab Elect & Automat Engn Coal Min, Xuzhou 221116, Jiangsu, Peoples R China;

    China Univ Min & Technol, Sch Elect & Power Engn, Xuzhou 221116, Jiangsu, Peoples R China|China Univ Min & Technol, Jiangsu Prov Lab Elect & Automat Engn Coal Min, Xuzhou 221116, Jiangsu, Peoples R China;

    China Univ Min & Technol, Sch Elect & Power Engn, Xuzhou 221116, Jiangsu, Peoples R China|China Univ Min & Technol, Jiangsu Prov Lab Elect & Automat Engn Coal Min, Xuzhou 221116, Jiangsu, Peoples R China;

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