Dynamic reconfiguration of a distribution network based on an improved grey wolf optimization algorithm

被引：0
|
作者：
Tian S. [1 ]
Liu L. [1 ]
Wei S. [1 ]
Fu Y. [1 ]
Mi Y. [1 ]
Liu S. [2 ]
机构：
[1] College of Electrical Engineering, Shanghai University of Electric Power, Shanghai
[2] State Grid Shanghai Electric Power Research Institute, Shanghai
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2021年 / 49卷 / 16期
关键词：
Distributed generation; Distribution network dynamic reconfiguration; Improved grey wolf optimization algorithm; K-means++clustering;
D O I：
10.19783/j.cnki.pspc.201356
中图分类号：
学科分类号：
摘要：
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. © 2021 Power System Protection and Control Press.
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页码：1 / 11
页数：10
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