Application of improved adaptive bee colony optimization algorithm in transformer fault diagnosis

被引：0
|
作者：
Wu J. [1 ]
Ding H. [2 ]
Ma X. [2 ]
Yan B. [3 ]
Wang X. [2 ]
机构：
[1] School of Computer Science & Technology, Henan Polytechnic University, Jiaozuo
[2] School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo
[3] Jiaozuo Power Supply Company, State Grid Henan Power Company, Jiaozuo
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2020年 / 48卷 / 09期
关键词：
Adaptive search strategy; Bee colony algorithm; Levy factor; Neural network; Transformer;
D O I：
10.19783/j.cnki.pspc.190694
中图分类号：
学科分类号：
摘要：
In order to enhance the ability of neural network to diagnose transformer faults and avoid local optimal and premature generation of bee colony algorithm, an improved adaptive search strategy bee colony optimization algorithm is proposed. The method adjusts the global and local search ability of the bee colony algorithm by adaptively adjusting the population update step size to avoid the local optimal condition, and introduces the Levy mutation factor to improve the performance of the local search. The improved bee colony algorithm is used to optimize the BP neural network weights and thresholds, and the iterative algorithm is repeated until the target accuracy requirement is reached. The method is tested based on transformer data. Simulation results show that the improved method has faster convergence speed and higher accuracy of discriminating faults. © 2020, Power System Protection and Control Press. All right reserved.
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页码：174 / 180
页数：6
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