基于贝叶斯网络的变压器故障诊断研究

doi:10.16180/j.cnki.issn1007-7820.2021.03.008

Abstract

Abstract:

In the traditional transformer fault diagnosis ratio method, there are problems that the ratio code is too absolute and early latency failures cannot be found in time. In view of these problems, the improved non-coding ratio method and the type of transformer fault are used as the Bayesian Network nodes, and the improved search strategy and scoring function are employed to establish a transformer fault diagnosis model based on Bayesian network. The on-line fault diagnosis system of transformer based on LabVIEW is established, which realizes the functions of data acquisition, fault reasoning and historical information inquiry. Finally, the experiment proves that the system can not only effectively improve the efficiency of transformer fault diagnosis, but also detect the fault in advance to extend the service life of the transformer, and provide strategic support for the safe and stable operation of the transformer.

Key words: transformer, Bayesian network, search strategy, scoring function, fault diagnosis, LabVIEW

CLC Number:

TP183

TONG Zhaojing,QIN Zini,ZHAO Yunxing,LU Tong,ZHENG Quan. Research on Transformer Fault Diagnosis Based on Bayesian Network[J].Electronic Science and Technology, 2021, 34(3): 43-47.

Figures/Tables 7

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References 17

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编号	属性	编号	属性
A₁	H₂	A₇	C₂H₂/(C₁+C₂)
A₂	C₂H₂	A₈	H₂/(H₂+ C₁+C₂)
A₃	C₁+C₂	A₉	C₂H₄/(C₁+C₂)
A₄	CH₄/H₂	A₁₀	CH₄/(C₁+C₂)
A₅	C₂H₂/C₂H₄	A₁₁	C₂H₆/(C₁+C₂)
A₆	C₂H₄/C₂H₆	A₁₂	(CH₄+C₂H₄)/(C₁+C₂)

编号	故障类型
S₀	正常
S₁	中低温过热(<700 ℃)
S₂	高温过热(>700 ℃)
S₃	低能放电
S₄	高能放电
S₅	放电兼过热
S₆	局部放电

诊断方法及参数		故障类型
诊断方法及参数		S₀	S₁	S₂	S₃	S₄	S₅	S₆
	测试集	7	21	86	15	15	3	9
三比	正判数	6	13	64	14	13	2	8
值法	正判率	77.0%
K2	正判数	7	19	76	14	13	2	9
模型	正判率	89.7%
DSC	正判数	7	20	79	15	14	3	9
模型	正判率	94.2%

Research on Transformer Fault Diagnosis Based on Bayesian Network

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