电子科技2024，Vol.37Issue(5)：47-53,70,8.DOI:10.16180/j.cnki.issn1007-7820.2024.05.007
改进贝叶斯网络在变压器故障诊断中的应用
Research on Transformer Fault Diagnosis Based on Improved Bayesian Network
摘要
Abstract
In view of the low accuracy of transformer fault diagnosis,a transformer fault diagnosis method based on ISMA(Improved Slime Mold optimization Algorithm)and optimized BN(Bayesian Network)is proposed.The hill-climbing algorithm searches the oriented maximum support tree to obtain the initial structure of the Bayesian network,that is,the initial population.The reverse learning strategy and SCA(Sine Cosine Algorithm)are intro-duced into the improved slime mold optimization algorithm to increase population diversity,update population loca-tion,and avoid the population falling into local optimal.The characteristics of transformer fault state are selected by the improved code-free ratio method,and the structure of Bayesian network is optimized by the improved slime mold optimization algorithm to improve the accuracy of transformer fault diagnosis based on Bayesian network.Different kinds of test functions are used to verify that the improved slime mold optimization algorithm has the excellent per-formance of fast convergence speed and high convergence accuracy.The simulation results show that the accuracy of the training set and the test set of the ISMA-BN diagnostic model is up to 98.2%and 97.14%,respectively,which has certain research value.关键词
故障诊断/改进黏菌优化算法/贝叶斯网络/结构学习/变压器/反向学习策略/正弦-余弦算法/测试函数Key words
fault diagnosis/improved slime mold algorithm/Bayesian network/structure learning/transformer/reverse learning strategy/sinusoidal cosine algorithm/test function分类
信息技术与安全科学引用本文复制引用
仝兆景,兰孟月,荆利菲..改进贝叶斯网络在变压器故障诊断中的应用[J].电子科技,2024,37(5):47-53,70,8.基金项目
国家自然科学基金(U1504623) （U1504623）
河南理工大学研究生教育教学改革项目(2021YJ10) National Natural Science Foundation of China(U1504623) （2021YJ10）
Gradu-ate Education and Teaching Reform Project of Henan Polytechnic U-niversity(2021YJ10) （2021YJ10）
