基于电—声综合分析的变压器局部放电模式识别研究
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摘要
变压器局部放电模式识别的研究对于及时发现潜在故障,防止电力系统重大事故发生具有重要意义。目前变压器局部放电模式识别主要有电脉冲法和气相色谱法。由于现场强烈的电磁干扰和复杂的变压器内部结构,电脉冲法的识别效果并不理想。气相色谱法虽然不受电磁干扰影响,可帮助分析大致放电类型,但不能说明放电形态和位置,且存在严重的滞后现象,不利于及时发现潜在的故障。
变压器局部放电产生的超声波信号受电磁干扰小,但频带较窄、波形复杂且易受变压器内部结构影响。在深入分析电脉冲信号和超声信号各自特点基础上,本文提出基于电声综合分析的局部放电模式识别方法,并建立了软硬件实验分析系统。
首先,根据局部放电产生原理与特点,建立了变压器五种放电模型,搭建了局部放电实验系统。主要包括测量子系统、耦合子系统、传输子系统以及实验系统的原理图和接线图。
其次,针对实验系统常见空间干扰、电源干扰和地干扰以及接地电阻影响,分别给出对应的硬件抑制措施以及软件消干扰方法。软件方法采用基于小波变换的消除干扰算法,并与傅里叶消干扰方法进行了对比分析。
然后,分析了变压器局部放电电气定位法的特点与不足,研究了各种电声定位法和声声定位法;提取电脉冲信号时域特征和超声波信号统计特征,构建特征向量,实现了典型的放电模式识别。
最后,开发了变压器局部放电分析系统,包括数据采集与显示、小波变换与傅里叶变换消干扰、超声定位和放电模式识别等软件模块。
Partial Discharge Pattern Recognition for the timely detection of potential failures, to prevent the occurrence of major accidents power system is important. PD pattern recognition in the current transformers are electrical pulse method and gas chromatography. Because the field a strong electromagnetic interference and the complex internal structure of the transformer, electric pulse method of identifying the effect is not ideal. Gas chromatography from electromagnetic interference, high recognition accuracy, but there is a serious lag is not conducive to timely detection of potential failures.
Transformer partial discharge by the ultrasonic signal generated by electromagnetic interference is small, but the band is narrow, the waveform is complex and susceptible to influence the internal structure of the transformer. In-depth analysis of electrical pulse signals and ultrasound signals based on their own characteristics, this paper presents a comprehensive analysis based on electro-acoustic partial discharge pattern recognition method, and the establishment of the Experimental Analysis of hardware and software systems.
First, according to the principle and characteristics of partial discharges, the establishment of five discharge of transformer model, set up a partial discharge test system. Including measurement subsystem, coupled subsystem, transport subsystem and the experimental system schematic and wiring diagram.
Second, common space for the experimental system interference, power disturbances and interfere with and affect the grounding resistance, the corresponding hardware are given disincentives and software destructive interference method. Software method using wavelet-based algorithm to eliminate interference and destructive interference with the Fourier method were compared.
Then, the analysis of transformer partial discharge location method of electrical characteristics and disadvantages of various positioning methods and the sound of acoustic location method; extract the electrical pulse signal characteristics and the ultrasonic signal in time domain statistical features, build feature vectors, to achieve a typical Discharge pattern recognition.
Finally, the development of the transformer partial discharge analysis system, including data acquisition and display, wavelet transform and Fourier transform destructive interference, ultrasound and discharge pattern recognition and other software modules.
变压器局部放电产生的超声波信号受电磁干扰小,但频带较窄、波形复杂且易受变压器内部结构影响。在深入分析电脉冲信号和超声信号各自特点基础上,本文提出基于电声综合分析的局部放电模式识别方法,并建立了软硬件实验分析系统。
首先,根据局部放电产生原理与特点,建立了变压器五种放电模型,搭建了局部放电实验系统。主要包括测量子系统、耦合子系统、传输子系统以及实验系统的原理图和接线图。
其次,针对实验系统常见空间干扰、电源干扰和地干扰以及接地电阻影响,分别给出对应的硬件抑制措施以及软件消干扰方法。软件方法采用基于小波变换的消除干扰算法,并与傅里叶消干扰方法进行了对比分析。
然后,分析了变压器局部放电电气定位法的特点与不足,研究了各种电声定位法和声声定位法;提取电脉冲信号时域特征和超声波信号统计特征,构建特征向量,实现了典型的放电模式识别。
最后,开发了变压器局部放电分析系统,包括数据采集与显示、小波变换与傅里叶变换消干扰、超声定位和放电模式识别等软件模块。
Partial Discharge Pattern Recognition for the timely detection of potential failures, to prevent the occurrence of major accidents power system is important. PD pattern recognition in the current transformers are electrical pulse method and gas chromatography. Because the field a strong electromagnetic interference and the complex internal structure of the transformer, electric pulse method of identifying the effect is not ideal. Gas chromatography from electromagnetic interference, high recognition accuracy, but there is a serious lag is not conducive to timely detection of potential failures.
Transformer partial discharge by the ultrasonic signal generated by electromagnetic interference is small, but the band is narrow, the waveform is complex and susceptible to influence the internal structure of the transformer. In-depth analysis of electrical pulse signals and ultrasound signals based on their own characteristics, this paper presents a comprehensive analysis based on electro-acoustic partial discharge pattern recognition method, and the establishment of the Experimental Analysis of hardware and software systems.
First, according to the principle and characteristics of partial discharges, the establishment of five discharge of transformer model, set up a partial discharge test system. Including measurement subsystem, coupled subsystem, transport subsystem and the experimental system schematic and wiring diagram.
Second, common space for the experimental system interference, power disturbances and interfere with and affect the grounding resistance, the corresponding hardware are given disincentives and software destructive interference method. Software method using wavelet-based algorithm to eliminate interference and destructive interference with the Fourier method were compared.
Then, the analysis of transformer partial discharge location method of electrical characteristics and disadvantages of various positioning methods and the sound of acoustic location method; extract the electrical pulse signal characteristics and the ultrasonic signal in time domain statistical features, build feature vectors, to achieve a typical Discharge pattern recognition.
Finally, the development of the transformer partial discharge analysis system, including data acquisition and display, wavelet transform and Fourier transform destructive interference, ultrasound and discharge pattern recognition and other software modules.
引文
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[37]张蕾,高胜友,谈克雄.油中局部放电超声信号模式识别的研究[J],电工电能新技术,2002, 21(3): 32-35
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[2]许中荣.大型电力变压器中局部放电信号传播特性的仿真研究[D].重庆大学,2004
[3]全玉生.电力变压器局部放电抗干扰与模式识别[J].电力自动化设备, 2008,28(1):57-60
[4]尹荣庆.局部放电检测技术及抗干扰技术进展[J].电气开关,2010,5:75-78
[5]张艳阳.变压器局部放电在线检测中的噪声抑制方法研究[D].湖南大学,2006
[6]刘云鹏.电力变压器局部放电的电气定位及诊断[D].华北电力大学,2004
[7]李燕青.超声波法检测电力变压器局部放电的研究[D].华北电力大学,2003:17-18
[8]吴治国.变压器局部放电超声波定位系统的研究[D].华中科技大学,2007
[9] Sander Meijer, Pantelis D.Agoris, Johan J.Smit. Application of UHF diagnostics to detect PD during power transformer acceptance tests[C]. Conference Record of the 2006 IEEE International Symposium on Electrical Insulation,2006.234-237
[10] R.E.James,B.T.Phung,Q.Su,Application of digital filtering techniques to the determination of partial discharge location in transformers[J],IEEE transactions on Electrical Insulation Magazine,1989,24(2):657-668
[11] Duan Dapeng,etc. Experimental study and feature extraction on UHF partial discharge detection for typical model[C]. International conference on condition monitoring and diagnosis ,Beijing,China,April,2008,21-24
[12]王猛,谈克雄,高文胜.局部放电脉冲波形的时频联合分析特征提取方法[J].电工技术学报,2002,17(2):76-79
[13]刘玲,廖瑞金,周湶,等.基于放电时差的局部放电模式识别的研究[J].高电压技术,2007,33(8):35-39
[14]全玉生,马彦伟,郑彬,等.基于时域脉冲特征量的神经网络方法在变压器局放模式识别中的应用[J].现代电力,2006,23(6):35-39
[15]郭俊,吴广宁,张血琴,等.局部放电检测技术的现状和发展[J].电工技术学报,2005,20(2):29-35
[16] Sander Meijer, Pantelis D.Agoris, Johan J.Smit. Application of UHF diagnostics to detect PD during power transformer acceptance tests[C]. Conference Record of the 2006 IEEE International Symposium on Electrical Insulation,2006,58-72
[17]王海跃,王国利.自适应遗传算法在变压器超高频局放模式识别中的应用[J].湖南电力,2004,24(5):4-8
[18]洪川,舒鸿科.色谱分析结合电气试验法在变压器故障定位中的应用[J].变压器,2008,45(11):38-40
[19]郑殿春.基于BP网络的局部放电模式识别[D].哈尔滨理工大学,2005
[20]殷录民,张慧芬.神经网络在变压器超高频局部放电模式识别中的应用[J].山东大学学报(工学版),2004,34(3):51-54
[21] D.Wenzel, H.Borsi, E.Gockenbach. A new approach for partial discharge recognition on-site by means of genetic algorithms. Conference record of the 1996 IEEE international symposium on electrical insulation. Montreal, quebec, Canada, june16-19, 1996
[22] Wen-Yeau Chang, Hong-Tzer Yang. Application of tuzzy c-means clustering approach to partial discharge pattern recognition of cast-resin current transformers[C]. IEEE, 2006
[23] T.Boczar, S.Borucki, A.Cichon and D.Zmarzy. Application possibilities of artificial neural networks for recognizing partial discharges measured by the acoustic emissionmethod.IEEE,2009
[24] B. Karthikeyan, S. Gopal, S. Venkatesh. ART 2—an unsupervised neural network for PD pattern recognition and classification[C]. Expert Systems with Applications 31 (2006) 345–350
[25] B. Karthikeyan, S. Gopal, M. Vimala. Conception of complex probabilistic neural network system for classification of partial discharge patterns using multifarious inputs[J]. Expert Systems with Applications 29 (2005) 953–963
[26]刘秀兰,刘念.基于灰度图像分形特征的局部放电模式识别[J].变压器,2009,46(1)
[27]付立华,张晓玟.基于支持向量机的局部放电模式识别[J].河南工程学院学报,2008,20(4):5-8
[28]毕为民.变压器局部放电监测中以小波包去噪和统计量识别放电模式的研究[D].重庆大学,2003
[29]李新,曾敏,田甜,等.基于小波多尺度变换局部放电图像模式识别的研究[J].变压器,2008,45(11): 34-37,68
[30]唐炬,等.以复小波变换系数为特征量的局部放电模式识别[J].重庆大学学报(自然科学版),2007,30(4)
[31] Y Tian,P L Lewin,A E Davies,etc,PD pattern identiflcation using acoustic emission measurement and neural network[C],proceeding of 11th Interntional SymPosium on High-Voltage Engineering,London,1999
[32]全玉生,马彦伟,何秋宇,等.基于模糊概率论的变压器局放信号模式识别法[J].电力系统自动化,2006,30(4):71-74
[33]姜磊,朱德恒,李福琪,等.基于人工神经网络的变压器绝缘模型放电模式识别的研究[J].中国电机工程学报,2001,21(1):21-24
[34] R. Badent, 0. Rudolph, W. Zierhut, A. Breuer, A.J. Schwab. PD-pattern recognition of on-site-measurement data.IEEE,1998
[35] R.E.James,B.T.Phung,Development of computer-based measurements and their application to PD pattern analysis,IEEE Transactions on Dielectrics and Electrical Insulation,1995,2(5):838-856
[36] Feliciano Massingue, Sander Meijer, Pantelis D. Agoris, Johan J. Smit. Partial discharge pattern analysis of modeled insulation defects in transformer insulation[C]. Conference Record of the 2006 IEEE International Symposium on Electrical Insulation
[37]张蕾,高胜友,谈克雄.油中局部放电超声信号模式识别的研究[J],电工电能新技术,2002, 21(3): 32-35
[38]马卫平,董旭柱.大型变压器局部放电在线监测系统研究[J].中国电力,2000,33(4):35-37,66
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