基于改进麻雀搜索算法优化BN的变压器故障诊断研究

doi:10.16180/j.cnki.issn1007-7820.2023.04.007

Abstract

Abstract:

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.

Key words: improved sparrow search algorithm, Bayesian network, structural learning, mutual information, global optimization, transformer, fault diagnosis, accuracy

CLC Number:

TP183

TONG Zhaojing,QIAO Zhengrui,LI Jinxiang,LAN Mengyue,JING Lifei. Research on Transformer Fault Diagnosis Based on Improved Sparrow Search Algorithm Optimization BN[J].Electronic Science and Technology, 2023, 36(4): 52-58.

Figures/Tables 5

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Table 1.

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References 21

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故障类型	H₂ /μL·L^-1	CH₄ /μL·L^-1	C₂H₆ /μL·L^-1	C₂H₄ /μL·L^-1	C₂H₂ /μL·L^-1
低温过热	19.24	110.00	181.08	3.38	0.33
中温过热	72.00	442.00	221.00	461.00	0.70
高温过热	63.40	121.16	35.73	183.74	0.23
局部放电	2 437.00	115.50	0.20	3.50	22.60
低能放电	3.80	0.51	0.09	0.60	3.68
高能放电	41.60	25.10	206.00	15.70	124.00
正常	7.50	5.70	3.40	2.60	3.20

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

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