SF_6断路器状态监测与故障诊断的研究
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摘要
高压断路器是电力系统中重要的一次设备,其中的SF_6断路器因其具有优良的绝缘性能和灭弧性能,获得了日益广泛的应用。即便如此,SF_6断路器的故障也时有发生,直接影响了电力系统的安全性和可靠性。在所有的SF_6断路器故障中,气体状态故障和机械状态故障占有相当大的比重,本文针对目前SF_6断路器状态监测和故障诊断系统在气体状态和机械状态两个方面存在的缺点和不足,从湿度监测、振动信号特征提取、故障诊断方法以及实际应用等几个方面进行了深入的研究。
SF_6断路器对SF_6气体中水蒸气的吸附效应是影响湿度监测精确性的主要原因,目前尚没有理想的定量描述吸附效应的方法。本文在Polanyi吸附势理论的基础上,提出一种采用“吸附势-吸附空间”特性曲线定量描述吸附效应的方法,推导了计及吸附效应的不同温度下的湿度之间相互换算的公式,最后通过实验手段证明了该方法的有效性。
时频联合分析是断路器振动信号特征提取的有效方法,但是在信号处理手段的选择上目前还不统一。本文对短时傅立叶变换和小波变换这两种时频联合分析方法各自的优缺点进行了研究,进而提出一种基于希尔伯特-黄变换的断路器机械振动信号特征提取新方法。应用上述三种方法分别从断路器振动信号中提取时频熵向量,通过比较各种时频熵向量的聚类性能得出结论,希尔伯特-黄变换是目前最理想的断路器振动信号特征提取方法。
现有的故障诊断方法在自动跟踪设备行为变化的能力上均存在不足,本文以人工免疫网络记忆分类器为基础,提出了一种具有自学习能力的断路器机械故障诊断方法,应用实测断路器振动特征数据对自学习方法和非自学习方法进行了比较,结果表明,文中方法能够跟踪设备状态的真实分类边界,取得更为准确的诊断结果。
最后,文章提出一种SF_6断路器气体状态和机械状态综合在线监测与故障诊断的设计方案,对系统的监测原理、硬件结构、软件流程等进行了详细的介绍,并与工程项目相结合开发了实际的应用系统。该系统已经在赤峰电业局部分投入使用,其中的气体状态监测单元获得了国家专利。
High voltage circuit breaker (HVCB) is one of the most important primaryequipment in power system, of which SF_6 HVCB is widely used because of itsgood quality of isolation and arcing, and so the malfunction of SF_6 HVCBdecreases the safety and reliability of power system directly. Gas failures andmechanical failuresconstitutequite a portion of all the failureswithin SF_6HVCB,the monitoring systems of which still have shortcomings. This paper makes greatefforts on moisture monitoring, vibration signal feature extraction, methods ofdiagnosis and their applications to improve the performance of monitoring andfaultdiagnosissystemforSF_6 HVCB.
The precision of moisture monitoring is affected by the adsorption effectbetween circuit breaker and water, and no suitable method of quantify this effectcan be used. This paper describes adsorption effect by“adsorptionpotential-adsorption space”curve based on Polanyi adsorption potential theory.Formulas for conversion of moisture value between different temperatures havebeen developed. The validity of this method has been proved by experimentalapproaches.
Time-frequency analysis is the important approach for extracting featuresfrom vibration signal, but no agreement has been made in this field. This papercompares the strength and shortcomings between short-time Fouriertransformation (STFT) and wavelet transformation (WT), and a new featureextraction method based on Hilbert-Huang transform (HHT) is presented. Thethree methods are employed to vibration signals of HVCB, and the clusteringquality of the features extracted is compared. Result shows that HHT is the mostidealmethodforanalyzingvibrationsignalofHVCB.
Theexistingdiagnosis methods are lackofthe abilitytopursuethechange ofcertain mechanical state in state space. This paper presents a self-learningdiagnosis method for mechanical failures of HVCB on the basis of artificialimmune network memory classifier (AINMC). Comparison has been madebetween self-learning method and non self-learning method, and result shows thatself-learningmethodcanachievemoreprecisejudgmentofthemechanicalstateof HVCB.
Finally, this paper presents a scheme of developing on-line monitoring anddiagnosis system for SF_6 HVCB. The monitoring principles, hardware structureand software flow are fullydiscussed. The system developed has been used in ChiFengbureauofelectricpowerandthemoisturemonitoringunitisgrantedapatent.
SF_6断路器对SF_6气体中水蒸气的吸附效应是影响湿度监测精确性的主要原因,目前尚没有理想的定量描述吸附效应的方法。本文在Polanyi吸附势理论的基础上,提出一种采用“吸附势-吸附空间”特性曲线定量描述吸附效应的方法,推导了计及吸附效应的不同温度下的湿度之间相互换算的公式,最后通过实验手段证明了该方法的有效性。
时频联合分析是断路器振动信号特征提取的有效方法,但是在信号处理手段的选择上目前还不统一。本文对短时傅立叶变换和小波变换这两种时频联合分析方法各自的优缺点进行了研究,进而提出一种基于希尔伯特-黄变换的断路器机械振动信号特征提取新方法。应用上述三种方法分别从断路器振动信号中提取时频熵向量,通过比较各种时频熵向量的聚类性能得出结论,希尔伯特-黄变换是目前最理想的断路器振动信号特征提取方法。
现有的故障诊断方法在自动跟踪设备行为变化的能力上均存在不足,本文以人工免疫网络记忆分类器为基础,提出了一种具有自学习能力的断路器机械故障诊断方法,应用实测断路器振动特征数据对自学习方法和非自学习方法进行了比较,结果表明,文中方法能够跟踪设备状态的真实分类边界,取得更为准确的诊断结果。
最后,文章提出一种SF_6断路器气体状态和机械状态综合在线监测与故障诊断的设计方案,对系统的监测原理、硬件结构、软件流程等进行了详细的介绍,并与工程项目相结合开发了实际的应用系统。该系统已经在赤峰电业局部分投入使用,其中的气体状态监测单元获得了国家专利。
High voltage circuit breaker (HVCB) is one of the most important primaryequipment in power system, of which SF_6 HVCB is widely used because of itsgood quality of isolation and arcing, and so the malfunction of SF_6 HVCBdecreases the safety and reliability of power system directly. Gas failures andmechanical failuresconstitutequite a portion of all the failureswithin SF_6HVCB,the monitoring systems of which still have shortcomings. This paper makes greatefforts on moisture monitoring, vibration signal feature extraction, methods ofdiagnosis and their applications to improve the performance of monitoring andfaultdiagnosissystemforSF_6 HVCB.
The precision of moisture monitoring is affected by the adsorption effectbetween circuit breaker and water, and no suitable method of quantify this effectcan be used. This paper describes adsorption effect by“adsorptionpotential-adsorption space”curve based on Polanyi adsorption potential theory.Formulas for conversion of moisture value between different temperatures havebeen developed. The validity of this method has been proved by experimentalapproaches.
Time-frequency analysis is the important approach for extracting featuresfrom vibration signal, but no agreement has been made in this field. This papercompares the strength and shortcomings between short-time Fouriertransformation (STFT) and wavelet transformation (WT), and a new featureextraction method based on Hilbert-Huang transform (HHT) is presented. Thethree methods are employed to vibration signals of HVCB, and the clusteringquality of the features extracted is compared. Result shows that HHT is the mostidealmethodforanalyzingvibrationsignalofHVCB.
Theexistingdiagnosis methods are lackofthe abilitytopursuethechange ofcertain mechanical state in state space. This paper presents a self-learningdiagnosis method for mechanical failures of HVCB on the basis of artificialimmune network memory classifier (AINMC). Comparison has been madebetween self-learning method and non self-learning method, and result shows thatself-learningmethodcanachievemoreprecisejudgmentofthemechanicalstateof HVCB.
Finally, this paper presents a scheme of developing on-line monitoring anddiagnosis system for SF_6 HVCB. The monitoring principles, hardware structureand software flow are fullydiscussed. The system developed has been used in ChiFengbureauofelectricpowerandthemoisturemonitoringunitisgrantedapatent.
引文
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