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基于特征判定系数的电力变压器振动信号故障诊断

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变压器带电故障诊断对于保证电力变压器安全平稳运行具有重要的意义.针对变压器工作环境复杂且单一参数表征变压器故障类型不全面的问题,文中提出一种基于自适应噪声完备集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和特征熵权法(entropy weight method,EWM)进行故障诊断的方法.通过相关系数与峭度加权(correlation coefficient and weighted kurtosis,CCWK)原则筛选CEEMDAN分量并重构信号,在实现剔除冗余分量的同时,提升变压器振动信号特征的表征能力;利用EWM构建特征判定系数实现单一数据诊断变压器故障类型;通过主成分分析法减小混合域特征尺度,采用鸡群优化算法优化支持向量机(support vector machine,SVM)模型进行故障诊断.对某变电站110 kV三相油浸式变压器进行分析,结果表明与概率神经网络和SVM等变压器故障诊断方法相比,文中方法能在提前定性故障类型的同时,进一步提高变压器故障诊断的准确率与效率.
Power transformer vibration signal fault diagnosis based on feature determination coefficient
Transformer live fault diagnosis is of great significance to ensure the safe and stable operation of power transformers.In response to the problem of complex working environment and limited fault types characterized by a single parameter,a method based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and feature entropy weights method(EWM)is proposed for fault diagnosis.The correlation coefficient weighted kurtosis(CCWK)principle is used to filter the CEEMDAN components and reconstruct the signal to achieve an improved characterisation of transformer vibration signal features while eliminating redundant components.The EWM is used to construct feature determination coefficients(FDC)to achieve a single data diagnosis of transformer fault types.The principal component analysis(PCA)is used to reduce the scale of mixed domain features and the chicken swarm optimization(CSO)algorithm is used to optimize support vector machine(SVM)model for fault diagnosis.The analysis is performed on a 110 kV three-phase oil-immersed transformer in a certain substation,and the results show that compared with other transformer fault diagnosis methods such as probabilistic neural network(PNN)and SVM,the proposed method not only provides early qualitative fault type identification but also improves the accuracy and efficiency of transformer fault diagnosis.

fault diagnosistransformer vibration signalcomplete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)signal-to-noise ratioentropy weight method(EWM)support vector machine(SVM)chicken swarm optimization algorithm

谢丽蓉、严侣、吐松江·卡日、张馨月

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新疆大学电气工程学院,新疆乌鲁木齐 830047

故障诊断 变压器振动信号 自适应噪声完备集合经验模态分解(CEEMDAN) 信噪比 熵权法(EWM) 支持向量机(SVM) 鸡群优化算法

国家自然科学基金

5206702162163034

2024

10.12158/j.2096-3203.2024.03.023

电力工程技术
江苏省电力公司 江苏省电机工程学会

电力工程技术

CSTPCD北大核心
影响因子：0.969
ISSN：2096-3203
年,卷(期)：2024.43(3)
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