GIS局部放电超高频包络检测研究
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摘要
封闭式气体绝缘组合电器（Gas Insulated Switchgear，GIS）广泛应用于城市供电等高压输变电系统中。局部放电既是GIS绝缘劣化的征兆和表现形式，又是绝缘进一步劣化的原因。为了保证GIS的安全可靠运行，国内外对GIS局部放电检测技术开展了大量的研究，UHF检测法在GIS局部放电研究中处于重要地位。但到目前为止，对检测中的UHF信号的模式识别还是一个尚待深入的前沿课题，其难点包括：局部放电UHF信号的传输机理和建模、稳定可靠的检测系统研制、UHF信号特征参数的选择和特征值的获取、合理的模式识别分类器设计和基于模式特征数据库的长期数据积累等。
本文运用电磁波理论，对局部放电产生的UHF信号在GIS腔体内的传播特性进行了深入地研究，分析了UHF信号传播的原理和特点，为GIS局部放电检测分析提供理论依据。
本文介绍了在实验室建立的GIS局部放电实验系统，设计制作了5种典型的GIS缺陷，针对5种缺陷模型分别进行了局部放电试验，记录了不同故障模型下局部放电信号的时域波形和包络信号波形。大量的试验结果表明，同一类缺陷模型所产生的局部放电信号特征具有很好的一致性，其包络也大致相同；不同的缺陷模型所产生的局部放电时域信号波形特征不同，其包络形状也不同；论文在此基础上提出了基于包络检波的GIS局部放电检测和缺陷类型模式识别的方法。相比于PRPD（Phase Resolved PartialDischarge）分类方法，该方法可以根据包络检波信号的特征参数更好地分辨干扰脉冲信号，减小因干扰脉冲产生的误警；该方法不依赖于工频相位，因此还可以应用于直流GIS的局放检测；采用包络检波避免了直接对UHF时域信号进行实时采样所需要的超高速采样系统和对庞大数据量的处理，具有更高的实用价值。
如何抑制干扰信号是影响局部放电准确测量的关键因素之一。论文分析了UHF法测量GIS局部放电中的各种干扰信号的来源及其特征，提出分别采用硬件带通滤波器、带阻滤波器和基于小波的信号处理算法来抑制干扰的方案。硬件滤波器能有效去除窄带干扰；对于白噪声干扰，论文从数学的角度运用希尔伯特变换的方法研究了包络检波前后信号中白噪声特性的变化，研究结果表明白噪声经过包络检波电路后其特征仍然是白噪声。白噪声的存在影响了包络信号时域特征参数提取的准确度，论文对小波滤波方法进行了比较研究，提出了针对包络信号的改进小波阈值去噪方法，取得了很好的去噪效果。
对局部放电信号提取和分析特征参数的最终目的是判断GIS中是否产生了缺陷，以及判断是哪种缺陷和确定缺陷的位置，以便于维修和维护。论文对包络信号模式识别的数据预处理、特征提取、分类器设计和样本训练的原理和算法做了深入的研究，针对包络信号的特点确定了以时域参数为特征参数和以BP神经网络为分类器进行缺陷类型的模式识别；时域参数能很好的描述包络信号的特征，BP神经网络结构简单、工作状态稳定、容错性好、抗干扰能力强、且具有自组织能力，其作为分类器的模式识别取得了很好的缺陷分类效果；处理结果表明对试验中缺陷类型的识别率达到96％以上。
论文还完整地介绍了研制的基于包络检波的GIS局部放电检测系统，包括UHF传感器、UHF放大器和数据采集电路等的检测系统硬件，实验室和GIS生产工厂测试验证了系统的正确性和可靠性。
Enclosed gas-insulated switchgear (GIS) is widely used in cities such as high voltageelectricity power transmission system. Partial Discharge serves as both a warning of and arepresentation of the degraded insulation of the GIS, and partial discharge may cause furtherdegradation of the insulation. In order to improve the safety and reliability of GIS, A largeamount of research has been carried out both at home and abroad. UHF signals analysis isone of the main methods of the study in GIS. So far, however, pattern recognition for UHFsignals is still a pending issue which is the forefront. The difficulties of the researchincluding: Transmission mechanism and modeling of UHF signals of Partial discharge,stability and reliability of detection system, selection and acquisition of characteristicparameters of UHF signal, Classifier design for pattern recognition, pattern-based databasesand long-term data accumulation.
In this paper, the theory of electromagnetic is used for analysis of UHF signals generated bypartial discharge in GIS. The principle of resonant and transmission characteristics isanalyzed too.
A GIS partial discharge testing system is established in the laboratory. Five kinds of typicaldefects was made in GIS and partial discharge testing were carried out. The time-domainsignal and its envelope signal of partial discharge was recorded. A large number ofexperiments showed that the time-domain characteristics of the partial discharge signals andthe envelopes generated by the same defect model are about the same. Both the time-domaincharacteristics of the partial discharge signals and the envelope shapes generated by thedifferent defect models are different. Therefore, the partial discharge detection and defectspattern recognition method based on the envelope detection is proposed in this paper.
Compared with the PRPD methods (Phase Resolved Partial Discharge), it can remove thepulse interference by the characteristic parameters of the envelope and reduce the wrongreports by interference. This method is independent on the phase of AC, so it is suitable forGIS of DC applications. The use of envelope detector avoids ultra-wideband real-timesampling requirement and a huge amount of data on the treatment.
Noise interference is one of the key factors affecting the accurate measurement of partialdischarge. Thesis analyzes various kinds of interference signal and their characteristics which affect the measurement in GIS. Hardware band-pass filter, band-stop filters andwavelet-based signal processing algorithm are used to remove interference. Hardware filtercan effectively remove the narrowband interference, but can not eliminate the white-noise.The mathematical method of Hilbert transform was used to study the signal changes ofwhite-noise before and after envelope detector. The existence of white-noise affects theextraction accuracy of signal envelope in time domain feature. Thesis compare and studyseveral types of wavelet methods for de-noise and an improved threshold method wasproposed which obtain good de-noising effect with testing data. Whether GIS is in goodcondition or not and finding where defects are the ultimate goal of signal extraction andanalysis of characteristic parameters. This work is used to guide the repair and maintenance.Data preprocessing, feature extraction, classifier design, principle and algorithm of trainingof network were studied in the paper. According to the feature of the envelop signal, thesisused the time-domain parameters for the characteristic parameters and the BP neural networkclassifier for defect types of pattern recognition. Time-domain parameters can be a gooddescription of the characteristics of the envelope signal. BP neural network has simplestructure, high stability, fault tolerance, and anti-interference ability; and it’s self-organized.As a classifier of pattern recognition, its hit rates are as much as more than96％withmeasurement data for five different defects.
Partial discharge detection system based on envelope detector was developed and realized inthis paper, which including the UHF sensor, UHF amplifier and data acquisition circuit.
本文运用电磁波理论，对局部放电产生的UHF信号在GIS腔体内的传播特性进行了深入地研究，分析了UHF信号传播的原理和特点，为GIS局部放电检测分析提供理论依据。
本文介绍了在实验室建立的GIS局部放电实验系统，设计制作了5种典型的GIS缺陷，针对5种缺陷模型分别进行了局部放电试验，记录了不同故障模型下局部放电信号的时域波形和包络信号波形。大量的试验结果表明，同一类缺陷模型所产生的局部放电信号特征具有很好的一致性，其包络也大致相同；不同的缺陷模型所产生的局部放电时域信号波形特征不同，其包络形状也不同；论文在此基础上提出了基于包络检波的GIS局部放电检测和缺陷类型模式识别的方法。相比于PRPD（Phase Resolved PartialDischarge）分类方法，该方法可以根据包络检波信号的特征参数更好地分辨干扰脉冲信号，减小因干扰脉冲产生的误警；该方法不依赖于工频相位，因此还可以应用于直流GIS的局放检测；采用包络检波避免了直接对UHF时域信号进行实时采样所需要的超高速采样系统和对庞大数据量的处理，具有更高的实用价值。
如何抑制干扰信号是影响局部放电准确测量的关键因素之一。论文分析了UHF法测量GIS局部放电中的各种干扰信号的来源及其特征，提出分别采用硬件带通滤波器、带阻滤波器和基于小波的信号处理算法来抑制干扰的方案。硬件滤波器能有效去除窄带干扰；对于白噪声干扰，论文从数学的角度运用希尔伯特变换的方法研究了包络检波前后信号中白噪声特性的变化，研究结果表明白噪声经过包络检波电路后其特征仍然是白噪声。白噪声的存在影响了包络信号时域特征参数提取的准确度，论文对小波滤波方法进行了比较研究，提出了针对包络信号的改进小波阈值去噪方法，取得了很好的去噪效果。
对局部放电信号提取和分析特征参数的最终目的是判断GIS中是否产生了缺陷，以及判断是哪种缺陷和确定缺陷的位置，以便于维修和维护。论文对包络信号模式识别的数据预处理、特征提取、分类器设计和样本训练的原理和算法做了深入的研究，针对包络信号的特点确定了以时域参数为特征参数和以BP神经网络为分类器进行缺陷类型的模式识别；时域参数能很好的描述包络信号的特征，BP神经网络结构简单、工作状态稳定、容错性好、抗干扰能力强、且具有自组织能力，其作为分类器的模式识别取得了很好的缺陷分类效果；处理结果表明对试验中缺陷类型的识别率达到96％以上。
论文还完整地介绍了研制的基于包络检波的GIS局部放电检测系统，包括UHF传感器、UHF放大器和数据采集电路等的检测系统硬件，实验室和GIS生产工厂测试验证了系统的正确性和可靠性。
Enclosed gas-insulated switchgear (GIS) is widely used in cities such as high voltageelectricity power transmission system. Partial Discharge serves as both a warning of and arepresentation of the degraded insulation of the GIS, and partial discharge may cause furtherdegradation of the insulation. In order to improve the safety and reliability of GIS, A largeamount of research has been carried out both at home and abroad. UHF signals analysis isone of the main methods of the study in GIS. So far, however, pattern recognition for UHFsignals is still a pending issue which is the forefront. The difficulties of the researchincluding: Transmission mechanism and modeling of UHF signals of Partial discharge,stability and reliability of detection system, selection and acquisition of characteristicparameters of UHF signal, Classifier design for pattern recognition, pattern-based databasesand long-term data accumulation.
In this paper, the theory of electromagnetic is used for analysis of UHF signals generated bypartial discharge in GIS. The principle of resonant and transmission characteristics isanalyzed too.
A GIS partial discharge testing system is established in the laboratory. Five kinds of typicaldefects was made in GIS and partial discharge testing were carried out. The time-domainsignal and its envelope signal of partial discharge was recorded. A large number ofexperiments showed that the time-domain characteristics of the partial discharge signals andthe envelopes generated by the same defect model are about the same. Both the time-domaincharacteristics of the partial discharge signals and the envelope shapes generated by thedifferent defect models are different. Therefore, the partial discharge detection and defectspattern recognition method based on the envelope detection is proposed in this paper.
Compared with the PRPD methods (Phase Resolved Partial Discharge), it can remove thepulse interference by the characteristic parameters of the envelope and reduce the wrongreports by interference. This method is independent on the phase of AC, so it is suitable forGIS of DC applications. The use of envelope detector avoids ultra-wideband real-timesampling requirement and a huge amount of data on the treatment.
Noise interference is one of the key factors affecting the accurate measurement of partialdischarge. Thesis analyzes various kinds of interference signal and their characteristics which affect the measurement in GIS. Hardware band-pass filter, band-stop filters andwavelet-based signal processing algorithm are used to remove interference. Hardware filtercan effectively remove the narrowband interference, but can not eliminate the white-noise.The mathematical method of Hilbert transform was used to study the signal changes ofwhite-noise before and after envelope detector. The existence of white-noise affects theextraction accuracy of signal envelope in time domain feature. Thesis compare and studyseveral types of wavelet methods for de-noise and an improved threshold method wasproposed which obtain good de-noising effect with testing data. Whether GIS is in goodcondition or not and finding where defects are the ultimate goal of signal extraction andanalysis of characteristic parameters. This work is used to guide the repair and maintenance.Data preprocessing, feature extraction, classifier design, principle and algorithm of trainingof network were studied in the paper. According to the feature of the envelop signal, thesisused the time-domain parameters for the characteristic parameters and the BP neural networkclassifier for defect types of pattern recognition. Time-domain parameters can be a gooddescription of the characteristics of the envelope signal. BP neural network has simplestructure, high stability, fault tolerance, and anti-interference ability; and it’s self-organized.As a classifier of pattern recognition, its hit rates are as much as more than96％withmeasurement data for five different defects.
Partial discharge detection system based on envelope detector was developed and realized inthis paper, which including the UHF sensor, UHF amplifier and data acquisition circuit.
引文
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