引用本文: | 田书欣,刘 浪,魏书荣,等.基于改进灰狼优化算法的配电网动态重构[J].电力系统保护与控制,2021,49(16):1-11.[点击复制] |
TIAN Shuxin,LIU Lang,WEI Shurong,et al.Dynamic reconfiguration of a distribution network based on an improved grey wolf optimization algorithm[J].Power System Protection and Control,2021,49(16):1-11[点击复制] |
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摘要: |
为了更好地解决含分布式电源(Distributed Generation, DG)的配电网重构问题,建立了考虑负荷需求与DG出力时变特性的配电网动态重构模型。首先采用K-means++聚类算法对日负荷进行时段划分。然后以系统损耗、电压偏离量为目标函数,并利用改进灰狼优化算法进行寻优计算。针对传统灰狼优化算法中存在的初始种群分布不均、缺少全局交流、容易陷入局部最优等问题,在生成初始种群时引入tent映射,增强初始种群的均匀性。引入合作竞争机制,提高个体间有效信息的利用率。在灰狼种群位置更新时引入自适应惯性权值,以满足不同时期的寻优要求。最后通过算例分析,验证了该算法的可行性与优越性。 |
关键词: 分布式电源 K-means++聚类 配电网动态重构 改进灰狼优化算法 |
DOI:DOI: 10.19783/j.cnki.pspc.201356 |
投稿时间:2020-11-05修订日期:2020-11-05 |
基金项目:国家重点研发计划项目资助(2017YFB0902800);国家电网公司科技项目(52094017003D)资助 |
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Dynamic reconfiguration of a distribution network based on an improved grey wolf optimization algorithm |
TIAN Shuxin,LIU Lang,WEI Shurong,FU Yang,MI Yang,LIU Shu |
(1. College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China;
2. State Grid Shanghai Electric Power Research Institute, Shanghai 200437, China) |
Abstract: |
To improve distribution network reconfiguration with Distributed Generation (DG), a dynamic distribution network reconfiguration model considering time-varying characteristics of DG output and load demand is established. First, the K-means++ clustering algorithm is used to divide the daily load period. Then, the system loss and voltage deviation are taken as the objective functions, and the improved grey wolf optimization algorithm is used to optimize the calculation. To tackle uneven initial population distribution, lack of global communication and ‘easy to fall into local optima’ in traditional grey wolf optimization algorithms, when generating the initial population, it introduces tent mapping to enhance the uniformity of the initial population. A cooperative competition mechanism is introduced to improve the utilization rate of effective information between individuals. An adaptive inertia weight is introduced when the grey wolf population position is updated to meet the optimization requirements of different periods. Finally, the feasibility and superiority of the proposed algorithm are verified by a numerical example.
This work is supported by the National Key Research and Development Program of China (No. 2017YFB0902800) and the Science and Technology Project of State Grid Corporation of China (No. 52094017003D). |
Key words: distributed generation K-means++clustering distribution network dynamic reconfiguration improved grey wolf optimization algorithm |