局部放电超声定位系统的研究与实现
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摘要
电力变压器是组成电力系统的关键设备,其安全运行是保证供电可靠性的基础。局部放电是造成变压器设备绝缘劣化的主要原因之一,也是其绝缘劣化的重要征兆和表现形式。所以准确有效的监测变压器内部局部放电的放电量和放电位置是保证变压器正常运行的关键。本文在分析国内外局部放电定位技术发展现状的基础上,重点研究了局部放电超声信号去干扰与局部放电定位两个方面的问题,提出了相应的算法并设计开发了局部放电超声定位系统,具体内容如下：
针对现场强烈的噪声干扰,提出一种新的方案：将多分辨经验模式分解应用于局部放电超声信号去干扰中。多分辨经验模式分解将小波变换的多分辨分析思想引入传统的经验模式分解,很好的克服了传统经验模式分解的模态混叠现象和计算量大的缺点。该算法能够有效地从包含噪声的现场信号中提取超声波信号,为局部放电定位奠定良好的基础,实验结果表明了算法的有效性。
针对变压器局部放电定位问题,提出使用新的优化方法——梯度收缩法来计算局部放电点的坐标,并对该方法进行了严格的论证和实验。梯度收缩法是一种新型高效的计算机寻优算法,该算法能使目标函数梯度的模逐渐收缩到零,以达到目标函数极小化,其综合了牛顿法和共轭梯度法的优点。现场定位结果表明,梯度收缩法能够有效地求解局部放电定位问题,并优于传统的局部放电定位算法。
根据上述算法,本文设计并开发了基于虚拟仪器的局部放电超声定位软件系统,本系统以图形化编程语言LabVIEW为核心,实现了读取局部放电超声信号、局部放电信号去干扰、局部放电定位、用户管理等功能,为变压器局部放电点的准确定位提供了一个有效的平台。
Power transformer is one of the most important instruments in power system, by whose safety running the credibility of power supply directly will be influenced. Partial discharge is the main reason for insulation ageing of high-voltage electrical equipment, and also an important symptom and manifestation of insulation ageing. So, for keeping power transformer normal, it is crucial to monitor the discharge location and electric capacity effectively and precisely. Based on analyzing the development of discharge location technology, this paper pays attention to the study for the discharge resource location and de-noise for partial discharge signal, and proposes approach to solve the two problems and development of the software platform. The main contributions can be summarized as follows:
Firstly, partial discharge signal de-noise methods are studied, and multi-resolution EMD is employed to remove the noise of the signal collected in the field. Introducing multi-resolution to traditional EMD, multi-resolution EMD successfully solves two problems of traditional EMD:modal mixture and computation quantity. This method can effectively extract partial discharge signal from the site signal and provide a basis for the discharge resource location. The result shows that the proposed approach is effective.
To calculate discharge source, a new optimization algorithm, gradient shrink algorithm is applied and derived. Gradient shrink algorithm is a new optimization algorithm for unconstrained functions, and it is expected to cause the Euclidean norm of the gradient of objective function gradually shrinks to zero in order to minimize the object. Field result shows that gradient shrink algorithm can effectively locate discharge source and is greatly superior to traditional methods.
Using the two methods above, this paper developes a soft system of ultrasonic locating based on virtual instrument. The system based on the graphical programming language LabVIEW can implement several functions such as filtering the on-line detection data, locating discharge source and so on, and provide an effective platform for the location of discharge source in power transformer.
针对现场强烈的噪声干扰,提出一种新的方案：将多分辨经验模式分解应用于局部放电超声信号去干扰中。多分辨经验模式分解将小波变换的多分辨分析思想引入传统的经验模式分解,很好的克服了传统经验模式分解的模态混叠现象和计算量大的缺点。该算法能够有效地从包含噪声的现场信号中提取超声波信号,为局部放电定位奠定良好的基础,实验结果表明了算法的有效性。
针对变压器局部放电定位问题,提出使用新的优化方法——梯度收缩法来计算局部放电点的坐标,并对该方法进行了严格的论证和实验。梯度收缩法是一种新型高效的计算机寻优算法,该算法能使目标函数梯度的模逐渐收缩到零,以达到目标函数极小化,其综合了牛顿法和共轭梯度法的优点。现场定位结果表明,梯度收缩法能够有效地求解局部放电定位问题,并优于传统的局部放电定位算法。
根据上述算法,本文设计并开发了基于虚拟仪器的局部放电超声定位软件系统,本系统以图形化编程语言LabVIEW为核心,实现了读取局部放电超声信号、局部放电信号去干扰、局部放电定位、用户管理等功能,为变压器局部放电点的准确定位提供了一个有效的平台。
Power transformer is one of the most important instruments in power system, by whose safety running the credibility of power supply directly will be influenced. Partial discharge is the main reason for insulation ageing of high-voltage electrical equipment, and also an important symptom and manifestation of insulation ageing. So, for keeping power transformer normal, it is crucial to monitor the discharge location and electric capacity effectively and precisely. Based on analyzing the development of discharge location technology, this paper pays attention to the study for the discharge resource location and de-noise for partial discharge signal, and proposes approach to solve the two problems and development of the software platform. The main contributions can be summarized as follows:
Firstly, partial discharge signal de-noise methods are studied, and multi-resolution EMD is employed to remove the noise of the signal collected in the field. Introducing multi-resolution to traditional EMD, multi-resolution EMD successfully solves two problems of traditional EMD:modal mixture and computation quantity. This method can effectively extract partial discharge signal from the site signal and provide a basis for the discharge resource location. The result shows that the proposed approach is effective.
To calculate discharge source, a new optimization algorithm, gradient shrink algorithm is applied and derived. Gradient shrink algorithm is a new optimization algorithm for unconstrained functions, and it is expected to cause the Euclidean norm of the gradient of objective function gradually shrinks to zero in order to minimize the object. Field result shows that gradient shrink algorithm can effectively locate discharge source and is greatly superior to traditional methods.
Using the two methods above, this paper developes a soft system of ultrasonic locating based on virtual instrument. The system based on the graphical programming language LabVIEW can implement several functions such as filtering the on-line detection data, locating discharge source and so on, and provide an effective platform for the location of discharge source in power transformer.
引文
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[8]韩朝晖.利用小波包分析抑制窄带干扰[J].信息与电子工程,2006,4(06)：449-453.
[9]李新,张明霞,张从力,胡伟宣.基于综合门限值小波包去除变压器局部放电白噪声[J].变压器,2006,43(12)：19-23.
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[5]Werle P., Akbari A.,Borsi H.Gockenbach E. Enhanced online PD evaluation on power transformers using wavelet techniques and frequency rejection filter for noise suppression[C]. Electrical Insulation Conference Record of the 2002 IEEE International Symposium on,7-10 April 2002:195-198.
[6]Driesen J, Belmans R. Time-frequency analysis in power measurement using complex wavelets Circuits and Systems [C].ISCAS 2002.IEEE International Symposium on,2002.5, Vol.5:681-684.
[7]刘燕川,韩朝晖.小波包技术在抑制窄带干扰中的应用[J].现代电子技术,2008(17)：36-38.
[8]韩朝晖.利用小波包分析抑制窄带干扰[J].信息与电子工程,2006,4(06)：449-453.
[9]李新,张明霞,张从力,胡伟宣.基于综合门限值小波包去除变压器局部放电白噪声[J].变压器,2006,43(12)：19-23.
[10]谭善文,秦树人.多分辨希尔伯特-黄(Hilbert-Huang)变换方法的研究[D].重庆大学博士学位论文,2001.
[1]]李岳生,黄友谦.数值逼近[M].人民教育出版社,1979.
[12]刘云鹏,陈志业,李成榕,律成方.电力变压器局部放电的电气定位及诊断[D].华北电力大学博士论文,2005.
[13]李军浩,司文荣,王颂,袁鹏,李彦明.电力变压器局部放电定位方法的现状及发展[J].变压器,2007,44(6)：40-44.
[14]Wang Z D, Crossley P A, Comick K J. Partial discharge location in power transformer using the spectra of the terminal current signals [J]. High Voltage Engineering,1999. Eleventh International Symposium on.1999,8, Vol.5:58-61.
[15]桂峻峰,谈克雄,高文胜.变压器局部放电电气定位的分析[J].电工电能新技术,2003,22(1)：32-34.
[16]王国利,郝艳捧,李彦明.电力变压器局部放电定位方法的现状和前景[J].变压 器,2001,38(11)：22-27.
[17]王昌友,李福祺,高胜友.电力设备的在线监测与故障诊断[M].北京：清华大学出版社,2006.
[18]严璋.电气绝缘在线检测技术[M].北京：中国电力出版社,1995.
[19]徐永禧,胡维新译.高压电器设备局部放电[M].北京：水利水电出版社.
[20]孙才新,罗兵,顾乐观等.变压器局部放电源的电—声和声—声定位法及其评判的研究[J].电工技术学报,1997,12(5)：49-60.
[21]唐良,李焕章,伍志容.变压器局部放电超声波定位原理[J].高电压技术1991,13(1)：39-42.
[22]董旭柱,王昌长,朱德恒.电力变压器局部放电在线监测研究的现状和趋势[J].变压器,1996,Vo1.33：2-7.
[23]卢毅,楼樟达,王大忠.用模式识别法进行油中放电超声定位的研究[J].电工技术学报,1999,14(3)：51-53.
[24]刘本永.非平稳信号分析导论[M].北京：国防工业出版社,2006.
[25]Zhidong Zhao, Min Pan, Yuquan Chen. Instantaneous frequency estimate for non-stationary signal [C], Proceedings of the fifth WCICA,2004(6):3641-3643.
[26]陈娟,邱天爽.Hilbert-Huang变换及其在信号处理中的应用[D].大连理工大学硕士学位论文,2005.
[27]杜寿昌,梁虹.基于Hilbert-Huang变换的心音信号时频分析研究[D].云南大学硕士学位论文,2003.
[28]杨志华,齐东旭.Hilbert-Huang变换的若干应用研究[D].中山大学博士学位论文,2005.
[29]彭博,金小刚.基于Hilbert-Huang变换和支持向量机的生物电信号的分析研究.浙江大学硕士论文[D],2006.
[30]Hilbert-Huang transforms analysis for neuron signal of microelectrode-guided stereotactic neurosurgery for Parkinson's disease [C]. Proceeding of ICBBE,2007(7)：1273-1276.
[31]张郁山,胡聿贤,梁建文.希尔伯特-黄变换(HHT)与地震动时程的希尔伯特谱——方法与应用研究[D].中国地震局地球物理研究所博士学位论文,2003.
[32]毋琦,柳亦兵.改进HHT方法及其在故障信号分析中的应用[J].华北电力大学(北京)硕士学位论文,2006.
[33]Gabor. Theory of communication [J].IEE,1946, Vol.93:429-457.
[34]Bedrosian E. A product theorem for Hilbert transforms[C]. Proceedings of the IEEE, 1963,5, Vol.51:868-869.
[35]Boashash B. Estimating and interpreting the instantaneous frequency of a signal. Ⅰ. [C] fundamentals. Proceedings of the IEEE,1992,5, Vol.80:520-538.
[36]EC. Titchmarsh. Introduction to the Theory of Fourier Integrals [M].Oxford, Oxford University Press,1948.
[37]N. E. Huang, Z. Shen, S. R. Long et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear non-stationary time series analysis [C]. Proc. Roy. Soc, London,1998:903-995.
[38]于德介,程军圣,杨宇.机械故障诊断的Hilbert-Huang变换方法[M].科学出版社,2006.
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