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

作　　者：仝兆景[1] 兰孟月 荆利菲 TONG Zhaojing;LAN Mengyue;JING Lifei(School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo 454003,China)

机构地区：[1]河南理工大学电气工程与自动化学院,河南焦作454003

出　　处：《电子科技》2024年第5期47-53,70,共8页Electronic Science and Technology

基　　金：国家自然科学基金(U1504623);河南理工大学研究生教育教学改革项目(2021YJ10)。

摘　　要：针对变压器故障诊断精度低的问题,文中提出一种基于改进黏菌优化算法(Improved Slime Mould Algorithm,ISMA)优化贝叶斯网络(Bayesian Network,BN)的变压器故障诊断方法。通过爬山算法对定向最大支撑树搜索得到贝叶斯网络初始结构即初始种群,在改进黏菌优化算法中引入反向学习策略,增加种群多样性。添加正弦-余弦算法(Sine Cosine Algorithm,SCA),更新解的位置以避免种群陷入局部最优。根据改良的无编码比值法选取变压器故障状态的特征,利用改进黏菌优化算法优化贝叶斯网络结构,提高基于贝叶斯网络的变压器故障诊断的准确率,并利用不同种类的测试函数验证了改进黏菌优化算法具有收敛速度快、收敛精度高的优良性能。仿真结果表明,ISMA-BN诊断模型的训练集和测试集准确率分别为98.2%和97.14%,具有一定的研究价值。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 introduced into the improved slime mold optimization algorithm to increase population diversity,update population location,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 performance 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 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TM41[自动化与计算机技术—控制科学与工程]