loading石家庄铁道大学四方学院毕业设计
摘 要
由于绝缘内部的局部放电是导致电气设备绝缘劣化的重要因素，而且放电模式与内部缺陷类型具有紧密的联系，因此，局部放电在线监测与模式识别技术是及时发现放电性故障并诊断出绝缘内部局部缺陷的重要手段，对于防止高压电气设备事故发生，提高高压电气设备运行的安全性和可靠性具有重要的意义。
小波分析理论是一种新兴的信号处理理论，它在时间上和频率上都有很好的局部性，这使得小波分析非常适合于时-频分析，借助时- 频局部分析特性，小波分析理论已经成为信号去噪中的一种重要的工具。利用小波方法去噪，是小波分析应用于实际的重要方面。小波去噪的关键是如何选择阈值和如何利用阈值来处理小波系数，通过对小波阈值化去噪的原理介绍,运用MATLAB 中的小波工具箱，对一个含噪信号进行阈值去噪，实例验证理论的实际效果，证实了理论的可靠性。本文简述了几种小波去噪方法，其中的阈值去噪的方法是一种实现简单、效果较好的小波去噪方法。
关键词：局部放电，去噪，阈值，小波系数
Partial discharge(PD)inside insulation iS considered as one main reason that lead to electrical equipments damage，and discharge patterns are closed related to the types of internal defects．As one of the major methods to find out discharge faults and diagnose internal defcets，the technologies of PD on-line monitoring and pattern recognition are of importance for the diagnosis of the quality of HV insulation system，and of importance to the safety and reliability of electrical equipment in service．
The wavelet analysis theory is a new signal processing theory. It has a very good
topicality in time and frequency, which makes the wavelet analysis very suitable for the time - frequency analysis. With the time - frequency’s local analysis characteristics, the wavelet analysis theory has become an important tool in the signal de-noising. Using wavelet methods in de-noising, is an important aspect in the application of wavelet analysis. The key of wavelet de-noising is how to choose a threshold and how to use thresholds to deal with wavelet coefficients. It confirms the reliability of the theory through the wavelet threshold de-noising principle, the use of the wavelet toolbox in MATLAB, carrying on threshold de-noising for a signal with noise and actual results of the example confirmation theory. This paper has summarized several methods about the wavelet de-noising, in which the threshold de-noising is a simple, effective method of wavelet de-noise.
Key words：Partial Discharge，Denoising , Threshold ，
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石家庄铁道大学四方学院毕业设计
Wavelet coefficients
目 录
第1章 绪 论 ································· 8 1.1 课题研究背景 ························································································································ 8 1.2 课题研究的现状 ····················································································································· 9 1.2.1局部放电在线监测与模式识别技术研究现状 ················ 9 1.3小波分析原理和应用 ···························· 11 1.3.1 小波变换在去除局部放电信号中的干扰的应用 ···························································· 11 1.3.2 小波分析在局部放电模式识别中的应用 ········································································ 11 1.4本文研究目标及主要工作 ·························· 11 第2章 小波多尺度变换的原理及应用分析 ·················································································· 12 2.1 引言 ······································································································································ 12 2.2 小波变换 ······························································································································ 12 2.2.1 连续小波变换 ··········································································································· 12 2.2.2 离散小波变换 ················································································ 错误！未定义书签。 2.2.3 多分辨分析 ··············································································································· 15 2.3 MALLAT算法 ··························································································································· 17 2.3.1 一维MALLAT算法 ·········································································································· 17 2.4 小波多尺度变换及分析的应用研究 ······················································································ 18 2.4.1 小波基的数学特性 ··································································································· 18 2.4.2 常用的小波函数及主要性质 ······················································································· 21 2.4.3 一维的小波信号多尺度变换及分析 ········································································ 25 2.5 小结 ········································································································ 错误！未定义书签。 第3章 小波阈值去噪的原理及方法 ······························································· 错误！未定义书签。 3.1 引言 ········································································································ 错误！未定义书签。 3.2 小波去噪原理分析 ················································································· 错误！未定义书签。 3.2.1 小波去噪原理 ················································································· 错误！未定义书签。 3.2.2 小波去噪步骤 ················································································· 错误！未定义书签。 3.3 阈值的选取与量化 ··············································································· 错误！未定义书签。 3.3.1 软阈值和硬阈值 ············································································· 错误！未定义书签。 3.3.2 阈值的几种形式 ············································································· 错误！未定义书签。 3.3.3 阈值的选取 ····················································································· 错误！未定义书签。 3.4 小结 ········································································································ 错误！未定义书签。 第4章 FFT频域阈值法去噪分析 ··································································· 错误！未定义书签。 4.1 引言 ········································································································ 错误！未定义书签。 4.2 FFT频域阈值法原理 ············································································· 错误！未定义书签。 4.3 小结 ········································································································ 错误！未定义书签。 第5章 基于MATLAB仿真结果分析 ····························································· 错误！未定义书签。
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石家庄铁道大学四方学院毕业设计
第6章 结论与展望 ·························································································· 错误！未定义书签。 参考文献 ··········································································································································· 32 致 谢 ·············································································································································· 33 附 录 ·············································································································································· 34
第一章 绪 论
1.1 课题研究的背景
电气设备绝缘材料多为有机材料，如矿物油、绝缘纸或各种有机合成材料。
绝缘体各区域承受的电场一般是不均匀的。而电介质本身通常也是不均匀的，有的是由不同材料组成的复合绝缘体，如气体．固体复合绝缘，液体-固体复合绝缘，以及固体．固体复合绝缘等。有的虽是单一的材料，但在制造或使用过程中会残留一些气泡或其它杂质；于是在绝缘体内部或表面就会出现某些区域的电场强度高于平均电场强度，某些区域的击穿场强低于平均击穿场强，因此在某些区域就会首先发生放电，而其他区域仍然保持绝缘的特性，这就形成了局部放电在电场作用下，导体间绝缘仅部分区域被击穿的电气放电现象称为局部放电。对于被气体包围的导体附近发生的局部放电，可称之为电晕。局部放电可能发生在导体边上，也可能发生在绝缘体的表面或内部，发生在表面的称为表面局部放电，发生在内部的称为内部局部放电。
局部放电对电气设备绝缘会产生严重的危害，主要表现在由于放电产生的局部发热、带电粒子的撞击、化学活性生成物以及射线等因素对绝缘材料的损害。这种对绝缘的破坏作用是一个缓慢发展的过程，而且从局部开始，受多种因素影响，对运行中的高压电气设备是一种隐患。由于电力系统中保护措施的日趋完善，各种过电压对设备绝缘的破坏作用相对减小，而运行中的工作电压对绝缘的劣化起着主导作用，局部放电对工作电压下的电气设备的影响越来越被人们重视。随着对高压电网运行可靠性要求的不断提高，对大型高压电气设备的运行安全性要求的也越来越高。据统计，在2000年至2003年间，高压电气设备故障占全国电网事故的45％；而且，绝缘故障是影响高压电气设备正常运行的主要原因。因此加强对高压电气设备绝状况的监督是保障电力系统安全稳定运行的重要手段。
由于局部放电与电气设备绝缘缺陷具有紧密联系，为了提高对高压电气设备局部放电的监视能力，局部放电在线检测与自动识别技术从20世纪80年代开始逐步发展起来并受到广大研究人员的重视。电气设备绝缘局部放电在线检测和自动识别是通过监测获得设备内部局部放电信息，利用计算机及信号处理技术对放电信号进行处理和提取，并代替人对局部放电进行描述和分类，以便进一步判
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石家庄铁道大学四方学院毕业设计
断电气设备绝缘可靠性。然而，由于在局部放电测量方法和测量仪器等方面还存在不少问题，如抗干扰、放电定位、放电识别等，因此在高压电气设各局部放电在线监测与自动识别技术研究上需要继续深入。特别对于大型电气设备局部放电的测量，被测信号通常会被强大的电磁干扰所淹没，而且内部局部放电的产生和发展过程更为复杂，从而需要具有更强能的抗干扰、放电定位以及模式识别技术来解决这些更为复杂的难题。
1.2 课题研究的现状
1.2.1局部放电在线监测与模式识别技术研究现状
现代传感器技术、信号处理技术、电子技术、计算机技术以及人工智能
技术和现代数学方法的发展，为局部放电在线监测技术的发展起到了推动作用。目前国内外对局部放电在线监测技术的研究可分为局部放电测量、干扰的分析和抑制、放电信号的特征提取和模式识别、局部放电的定位等四部分。其中局部放电的测量是基础，有效地采集放电信号的特征是后面进一步分析和判断的前提；而由于放电信号很微弱，测量现场特别是在线监测时，干扰往往还很强，干扰的抑制成为必不可少的环节；而局部放电的识别和定位则是研究的目的，它又是对设备进行故障诊断和维修的重要依据之一。以下是本文对局部放电在线监测技术中的抗干扰技术和模式识别技术研究现状的分析。
高压电气设备局部放电在线监测中，与局部放电信号一起通过传感器进入监测系统的干扰信号，按照波形特征可分为：周期性干扰信号、随机脉冲干扰信号和白噪干扰。周期性干扰信号又分为连续性和脉冲性两种，如广播、通信、谐波等信号的干扰一般呈正弦波，为连续性周期干扰，也称窄带周期干扰；而可控硅开、闭时产生的脉冲干扰信号则周期地出现在工频的某相位上，为周期性脉冲干扰。白噪干扰则多是由电气设备及监测系统等运行过程中发热而引起的针对干扰信号特征和性质的不同，需采用不同的措旋抑制不同的干扰。在已有的各种系统中，采用的抗干扰措施主要可以分为专用抗干扰电路和抗干扰软件两大类。在局部放电检测系统中，专用抗干扰电路不仅仅只是特殊的抗干扰电子电路本身，而且还包括检测阻抗或传感器取信号的特殊方法，主要包括脉冲极性鉴别法、差动平衡法、定向耦合差动平衡法、多端调节法等。随着现代化数字处理技术的发展，局放在线监测中抑制干扰的措施正趋向软件化方向发展，即对采集信号进行数字处理。同时，人们努力将硬件和软件处理结合起来，以最大限度地抑制和识别干扰，获得局部放电信息，形成一整套完整的抗干扰体系。局放在线监测中采用的抗干扰现代数字信号处理方法主要有以下几种方法。
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