基于改进麻雀搜索算法优化BN的变压器故障诊断研究

针对变压器故障诊断准确率低和稳定性差的问题，文中提出了一种改进麻雀搜索算法优化贝叶斯网络的变压器故障诊断方法。首先，通过计算互信息建立最大支撑树并进行定向处理得到贝叶斯网络初始结构即初始种群。然后，在算法中引入一种新的合作机制和正弦余弦算法，提高算法收敛速度和全局搜索能力，并利用油中溶解气体分析，创建基于改进麻雀搜索算法优化贝叶斯网络的变压器故障诊断模型。最后，为了证明所提方法的优越性，将所提的方法与现有变压器故障诊断方法进行对比。结果表明，文中所提出的方法故障诊断率最高，可以更精准地对变压器进行故障诊断。

Research on Transformer Fault Diagnosis Based on Improved Sparrow Search Algorithm Optimization BN

In view of at the problems of low accuracy and poor stability of transformer fault diagnosis, a transformer fault diagnosis method based on improved sparrow search algorithm and optimized Bayesian network is proposed. By calculating mutual information, the maximum support tree is established and directional processing is carried out to obtain the initial structure of Bayesian network, that is, the initial population, a new cooperation mechanism and sine cosine algorithm are introduced into the algorithm to improve the convergence speed and global search ability of the algorithm. Based on the analysis of dissolved gas in oil, a transformer fault diagnosis model based on improved sparrow search algorithm and optimized Bayesian network is established. In order to prove the superiority of the proposed method, the proposed method is compared with the existing transformer fault diagnosis methods. The results show that the proposed method has the highest fault diagnosis rate and can diagnose the transformer fault more accurately.