TY - JOUR
T1 - Distribution network planning method based on hybrid genetic algorithm
AU - Shao, Yunfeng
AU - Sun, Yuanming
AU - Wang, Yajing
AU - Ma, Zhongjing
AU - Liu, Yongqiang
AU - Zhao, Yang
N1 - Publisher Copyright:
© 2020 Institute of Physics Publishing. All rights reserved.
PY - 2020/11/23
Y1 - 2020/11/23
N2 - Facing the discrete, multi-constrained, non-linear, multi-objective combination optimization problem of distribution network grid planning, some traditional heuristic algorithms such as genetic algorithms sometimes fall into local optimum. This paper proposes a distribution network planning method based on hybrid genetic algorithm. The algorithm consists of two stages. In the first stage, the genetic algorithm is used to obtain the initial planning scheme. In the second stage, the initial planning scheme obtained in the first stage is used to form the planned route set. The improved minimum spanning tree method is used to obtain the final planning scheme. In order to make full use of the effective information obtained in the first stage, this paper proposes a transmission line classification method to assess the importance of the transmission line, provide guidance for the second stage, and improve the search efficiency and accuracy. The algorithm solves the problem that heuristic algorithms such as genetic algorithm often fall into local optimization to a certain extent, and the problem of slow convergence when the minimum spanning tree algorithm has a large number of lines to be planned.
AB - Facing the discrete, multi-constrained, non-linear, multi-objective combination optimization problem of distribution network grid planning, some traditional heuristic algorithms such as genetic algorithms sometimes fall into local optimum. This paper proposes a distribution network planning method based on hybrid genetic algorithm. The algorithm consists of two stages. In the first stage, the genetic algorithm is used to obtain the initial planning scheme. In the second stage, the initial planning scheme obtained in the first stage is used to form the planned route set. The improved minimum spanning tree method is used to obtain the final planning scheme. In order to make full use of the effective information obtained in the first stage, this paper proposes a transmission line classification method to assess the importance of the transmission line, provide guidance for the second stage, and improve the search efficiency and accuracy. The algorithm solves the problem that heuristic algorithms such as genetic algorithm often fall into local optimization to a certain extent, and the problem of slow convergence when the minimum spanning tree algorithm has a large number of lines to be planned.
UR - http://www.scopus.com/inward/record.url?scp=85097090875&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1673/1/012032
DO - 10.1088/1742-6596/1673/1/012032
M3 - Conference article
AN - SCOPUS:85097090875
SN - 1742-6588
VL - 1673
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012032
T2 - 6th Annual International Conference on Computer Science and Applications, CSA 2020
Y2 - 25 September 2020 through 27 September 2020
ER -