Open AccessFault Location Analysis in Active Distribution Network Based on Multi-population Genetic Algorithm
Author(s) -
Shifeng Ou,
Feng Bao,
Liwen Qin,
Xin Yu,
Yuteng Luo
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2237/1/012020
Subject(s) - fault (geology) , convergence (economics) , genetic algorithm , computer science , algorithm , range (aeronautics) , generator (circuit theory) , mathematical optimization , population , power (physics) , engineering , mathematics , physics , demography , quantum mechanics , aerospace engineering , seismology , geology , sociology , economics , economic growth
Because the fault location of active distribution network is a complex optimization model, the traditional genetic algorithm has the problems of premature and slow convergence when solving complex optimization problems. The switch function that can adapt to the switching of multiple distributed power sources in real time is proposed. Here, we proposed a novel method to locate the fault of distributed power distribution network based on a multi-group genetic algorithm. When locating the fault zone, The algorithm stipulates that the direction of the generator flowing to the electrical equipment is the forward direction of the transmission line. In this paper, multiple populations are used to search the solution range at the same time to ensure that the final solution is the global optimal solution, and the optimal individual retention algebra is used as the convergence condition to fully improve the solution speed, which is suitable for complex distribution networks with distributed power generation. The fault location model proposed in this paper is simulated by a numerical example, and the results show that the model can accurately find the fault point, and has validity and accuracy.