电子科技2024，Vol.37Issue(8)：34-39,6.DOI:10.16180/j.cnki.issn1007-7820.2024.08.005
一种用于变压器故障诊断的贝叶斯网络优化方法
A Bayesian Network Optimization Method for Transformer Fault Diagnosis
摘要
Abstract
In view of the low efficiency of transformer fault diagnosis,an improved grasshopper optimization al-gorithm is proposed by combining dissolved gas analysis in oil with artificial intelligence method to optimize the trans-former fault diagnosis method of Bayesian network.The differential evolution algorithm and simulated annealing algo-rithm are used to improve the locust algorithm,which improve the optimization ability of the algorithm.The improved locust algorithm is applied to the Bayesian network structure learning to construct the transformer fault diagnosis mod-el,and the method proposed in this study is used to diagnose the transformer fault.The experimental results show that the diagnosis accuracy of this method is 92.7%,which is higher than that of other algorithms.关键词
变压器/蝗虫算法/差分进化算法/模拟退火算法/油中溶解气体/贝叶斯网络/故障诊断/结构学习Key words
transformer/locust algorithm/differential evolution algorithm/simulated annealing algorithm/dis-solved gas in oil/Bayesian network/fault diagnosis/structural learning分类
信息技术与安全科学引用本文复制引用
仝兆景,荆利菲,兰孟月..一种用于变压器故障诊断的贝叶斯网络优化方法[J].电子科技,2024,37(8):34-39,6.基金项目
国家自然科学基金(U1504623) （U1504623）
河南理工大学教育教学改革基金(2021YJ10)National Natural Science Foundation of China(U1504623) （2021YJ10）
Educa-tion and Teaching Reform Foundation of Henan Polytechnic Univer-sity(2021YJ10) （2021YJ10）
