一种用于变压器故障诊断的贝叶斯网络优化方法

doi:10.16180/j.cnki.issn1007-7820.2024.08.005

Abstract

Abstract:

In view of the low efficiency of transformer fault diagnosis, an improved grasshopper optimization algorithm is proposed by combining dissolved gas analysis in oil with artificial intelligence method to optimize the transformer fault diagnosis method of Bayesian network. The differential evolution algorithm and simulated annealing algorithm 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 model, 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.

Key words: transformer, locust algorithm, differential evolution algorithm, simulated annealing algorithm, dissolved gas in oil, Bayesian network, fault diagnosis, structural learning

CLC Number:

TP18

TONG Zhaojing, JING Lifei, LAN Mengyue. A Bayesian Network Optimization Method for Transformer Fault Diagnosis[J].Electronic Science and Technology, 2024, 37(8): 34-39.

Figures/Tables 9

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References 19

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状态编码	运行状态
F₀	正常
F₁	低温过热
F₂	中温过热
F₃	高温过热
F₄	局部放电
F₅	低能放电
F₆	高能放电

故障类型	样本数据	训练样本	测试样本
正常	48	38	10
低温过热	48	38	10
中温过热	52	41	11
局部放电	55	44	11
低能放电	96	77	19
高能放电	88	70	18
高温过热	58	46	12

故障类型	DESAGOA- BNs/%	GOA- BNs/%	GWO- BNs/%	PSO- BNs/%
正常	100.0	90.0	80.0	70.0
低温过热	100.0	80.0	70.0	70.0
中温过热	90.9	72.7	90.9	81.8
局部放电	90.9	90.9	72.7	72.7
低能放电	89.4	84.3	84.2	78.9
高能放电	94.4	88.9	83.3	77.8
高温过热	83.3	75.0	83.3	66.7

A Bayesian Network Optimization Method for Transformer Fault Diagnosis

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