电力变压器故障诊断方法研究
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摘要
电力变压器是电力系统中重要的电气设备。研究电力变压器故障诊断方法对及早发现变压器潜在故障、提高电力系统的安全性具有重要意义。鉴于变压器故障机理复杂,本文重点借助变压器振动信号、局部放电信号和油中溶解气体含量数据,提取状态特征,并结合机器学习的理论,对变压器故障诊断方法进行了研究。
论文提出了采用局域波进行变压器器身振动信号分析的方法。鉴于变压器器身振动信号能有效反映变压器内部绕组与铁芯的状况,应用局域波方法,通过识别信号中的振动模式,可更好地理解发生故障时振动信号包含的特征,进而可判断变压器是否出现故障。
论文提出了因子分析方法与基因表达式编程算法相结合的变压器故障诊断新方法。首先用因子分析方法对原始DGA数据提取主成分,然后用基因表达式编程算法对变压器油色谱样本进行智能训练,建立变压器故障诊断模型。该方法可有效降低DGA属性维数,克服属性间的相关性,提高诊断正确率。
论文提出了物元理论和云模型相结合的变压器故障诊断方法。该方法采用云模型改造物元的结构,解决了变压器故障诊断中信息的不确性问题,且模型可在无数据样本的情况下建立,可有效解决变压器数据样本、特别是故障样本少的情况下的故障诊断问题。
论文提出了首先利用多种故障诊断模型进行初步诊断,再利用支持向量机进行二次组合诊断的新的变压器组合诊断方法。该方法采用智能互补融合的思想,克服了单一诊断模型在变压器故障诊断中的不足,提高了模型的诊断正确率和适用范围。
论文提出了基于EEMD的对局部放电信号加窗分段去噪的新算法,以有效减少信号数据的EEMD分解所需要的计算量；计算并选取了放电二维谱图的8种特征量,并利用Fisher方法对实验样本集进行了分类。结果表明,使用这些特征量和所提方法能够很好地甄别局部放电类型。
Power transformer is a key equipment in the power system, and research on its fault diagnosis methods is of great significance for the early detection of transformer potential failure and the improvement of the power system security. In view of the complex fault mechanism of transformer, the paper selects vibration signal, partial discharge signal and dissolved gases in the oil as research object, and focus on transformer fault diagnosis methods combining with status feature extraction and machine learning theory.
Local wave method for analyzing transformer body vibration signal is proposed. In view of transformer body vibration signal can effectively reflect the status of winding and iron core of transformer, local wave method is used for vibration signal pattern recognition of transformer body vibration. It can better understand the characteristics included in the transformer fault vibration signal, and thereby can discriminate whether the transformer is fault.
A novel transformer fault diagnosis method based on factor analysis (FA) and gene expression programming algorithm (GEP) is proposed. First, the principal components are extracted by FA from original DGA data, and then GEP based transformer fault diagnosis model is builded by intelligent training. It can effectively reduce DGA attribute dimension, overcome correlation between attributes and improve the diagnostic accuracy.
A transformer fault diagnosis method based on matter-element theory combining with cloud model is proposed. Cloud model is used to reform the structure of matter-element, which can resolve the information's uncertainties of transformer fault diagnosis. And the model can be builded of no data sample, so it is suitable to slove the transformer fault diagnosis problem of fewer samples or fewer fault samples.
A novel combination model for transformer fault diagnosis is proposed, which uses multiple fault diagnosis models to initial diagnosis and then use support vector machine to secondary combination diagnosis. Based on intelligent complementary idea, the combination model overcomes weakness of single diagnosis model and improves the diagnostic accuracy and the application scope.
Windowed segments denosing algorithm based on EEMD for partial discharge signal is proposed, which can effectively reduce EEMD computation. And also, by selecting and computing the eight kinds of feature of two-dimensional spectra of discharge signal and adopting Fisher method, the experimental samples are classified. The results show that the selected characteristic quantities and the proposed method can well discriminate the partial discharge type.
论文提出了采用局域波进行变压器器身振动信号分析的方法。鉴于变压器器身振动信号能有效反映变压器内部绕组与铁芯的状况,应用局域波方法,通过识别信号中的振动模式,可更好地理解发生故障时振动信号包含的特征,进而可判断变压器是否出现故障。
论文提出了因子分析方法与基因表达式编程算法相结合的变压器故障诊断新方法。首先用因子分析方法对原始DGA数据提取主成分,然后用基因表达式编程算法对变压器油色谱样本进行智能训练,建立变压器故障诊断模型。该方法可有效降低DGA属性维数,克服属性间的相关性,提高诊断正确率。
论文提出了物元理论和云模型相结合的变压器故障诊断方法。该方法采用云模型改造物元的结构,解决了变压器故障诊断中信息的不确性问题,且模型可在无数据样本的情况下建立,可有效解决变压器数据样本、特别是故障样本少的情况下的故障诊断问题。
论文提出了首先利用多种故障诊断模型进行初步诊断,再利用支持向量机进行二次组合诊断的新的变压器组合诊断方法。该方法采用智能互补融合的思想,克服了单一诊断模型在变压器故障诊断中的不足,提高了模型的诊断正确率和适用范围。
论文提出了基于EEMD的对局部放电信号加窗分段去噪的新算法,以有效减少信号数据的EEMD分解所需要的计算量；计算并选取了放电二维谱图的8种特征量,并利用Fisher方法对实验样本集进行了分类。结果表明,使用这些特征量和所提方法能够很好地甄别局部放电类型。
Power transformer is a key equipment in the power system, and research on its fault diagnosis methods is of great significance for the early detection of transformer potential failure and the improvement of the power system security. In view of the complex fault mechanism of transformer, the paper selects vibration signal, partial discharge signal and dissolved gases in the oil as research object, and focus on transformer fault diagnosis methods combining with status feature extraction and machine learning theory.
Local wave method for analyzing transformer body vibration signal is proposed. In view of transformer body vibration signal can effectively reflect the status of winding and iron core of transformer, local wave method is used for vibration signal pattern recognition of transformer body vibration. It can better understand the characteristics included in the transformer fault vibration signal, and thereby can discriminate whether the transformer is fault.
A novel transformer fault diagnosis method based on factor analysis (FA) and gene expression programming algorithm (GEP) is proposed. First, the principal components are extracted by FA from original DGA data, and then GEP based transformer fault diagnosis model is builded by intelligent training. It can effectively reduce DGA attribute dimension, overcome correlation between attributes and improve the diagnostic accuracy.
A transformer fault diagnosis method based on matter-element theory combining with cloud model is proposed. Cloud model is used to reform the structure of matter-element, which can resolve the information's uncertainties of transformer fault diagnosis. And the model can be builded of no data sample, so it is suitable to slove the transformer fault diagnosis problem of fewer samples or fewer fault samples.
A novel combination model for transformer fault diagnosis is proposed, which uses multiple fault diagnosis models to initial diagnosis and then use support vector machine to secondary combination diagnosis. Based on intelligent complementary idea, the combination model overcomes weakness of single diagnosis model and improves the diagnostic accuracy and the application scope.
Windowed segments denosing algorithm based on EEMD for partial discharge signal is proposed, which can effectively reduce EEMD computation. And also, by selecting and computing the eight kinds of feature of two-dimensional spectra of discharge signal and adopting Fisher method, the experimental samples are classified. The results show that the selected characteristic quantities and the proposed method can well discriminate the partial discharge type.
引文
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