| 引用本文: | 吴君,丁欢欢,马星河,闫炳耀,王新宇.改进自适应蜂群优化算法在变压器故障诊断中的应用[J].电力系统保护与控制,2020,48(9):174-180.[点击复制] |
| WU Jun,DING Huanhuan,MA Xinghe,YAN Bingyao,WANG Xinyu.Application of improved adaptive bee colony optimization algorithm in transformer fault diagnosis[J].Power System Protection and Control,2020,48(9):174-180[点击复制] |
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| 摘要: |
| 为增强神经网络对变压器故障诊断的能力，同时避免蜂群算法出现局部最优和易早熟，提出一种改进自适应搜索策略蜂群优化算法。该方法通过自适应调整种群更新步长来协调蜂群算法的全局和局部搜索能力，避免出现局部最优状况，同时引入Levy变异因子提高局部搜索的性能。利用改进的蜂群算法优化BP神经网络权值和阈值，通过反复迭代算法，直到达到目标精度要求。该方法以变压器数据为依据进行测试。仿真结果表明，改进后的方法收敛速度更快、判别故障准确率更高。 |
| 关键词: 蜂群算法 自适应搜索策略 神经网络 Levy因子 变压器 |
| DOI：10.19783/j.cnki.pspc.190694 |
| 投稿时间：2019-06-17修订日期：2019-08-03 |
| 基金项目:河南省科技攻关计划资助(172102210274);河南省高等学校重点科研项目资助(18A470013) |
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| Application of improved adaptive bee colony optimization algorithm in transformer fault diagnosis |
| WU Jun,DING Huanhuan,MA Xinghe,YAN Bingyao,WANG Xinyu |
| (School of Computer Science & Technology, Henan Polytechnic University, Jiaozuo 454000, China;School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China;Jiaozuo Power Supply Company, State Grid Henan Power Company, Jiaozuo 454000, China) |
| Abstract: |
| 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. This work is supported byScience and Technology Research Project of Henan Province (No. 172102210274) and Key Scientific Research Project of Colleges and Universities in Henan Province (No. 18A470013). |
| Key words: bee colony algorithm adaptive search strategy neural network Levy factor transformer |
