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

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 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.

Key words: fault diagnosis, improved slime mold algorithm, Bayesian network, structure learning, transformer, reverse learning strategy, sinusoidal cosine algorithm, test function