本文選題：局部放電 + 閾值去噪　； 參考：《中國礦業(yè)大學》2017年碩士論文

【摘要】：局部放電既是電力變壓器絕緣故障的重要誘發(fā)因素,又是絕緣潛伏性故障的高靈敏度表現(xiàn)形式。因此,局放信號的檢測、局放故障模式識別及故障定位的研究,一直備受重視。本文制作了代表氣隙放電、空氣中電暈放電、油中沿面放電和空氣中沿面放電四種典型局放故障的實驗?zāi)Ｐ?搭建了實驗平臺,采集了典型局放故障的原始信號,進行了局放故障的模式識別研究。首先,針對局部放電信號非平穩(wěn)、非線性強、易受干擾等特性,結(jié)合經(jīng)驗?zāi)B(tài)分解法,提出了基于完全經(jīng)驗?zāi)B(tài)分解和總體經(jīng)驗?zāi)B(tài)分解(CEEMD-EEMD)的局部放電閾值去噪新方法。利用新方法和常規(guī)的小波閾值去噪算法對仿真與實測局部放電信號進行去噪,去噪結(jié)果表明,基于CEEMD-EEMD的局部放電閾值去噪方法去噪效果更優(yōu),驗證了該方法的有效性,為局部放電故障的特征提取與模式識別奠定了基礎(chǔ)。其次,根據(jù)局部放電相位分布(PRPD)模式繪制了不同絕緣故障類型的二維圖譜、三維圖譜以及灰度圖,利用主成分因子分析法將36維統(tǒng)計特征量降到8維具有物理意義的特征量。為了最大限度提取到局放故障最本質(zhì)的信息,嘗試采用自回歸模型算法提取局部放電信號特征,通過BP神經(jīng)網(wǎng)絡(luò)和SVM對這兩類特征進行分類識別,結(jié)果表明基于自回歸模型系數(shù)特征的局部放電識別率均較高,說明局放故障的自回歸系數(shù)特征要優(yōu)于統(tǒng)計特征量。再次,利用超球面支持向量機對不同絕緣故障局部放電類型進行模式識別。局部放電信號檢測復雜,對應(yīng)故障類型多樣,局部放電樣本數(shù)目有限且特征量呈非線性,使得BP神經(jīng)網(wǎng)絡(luò)和SVM的識別率較低。本文基于自回歸系數(shù)特征,采用經(jīng)過粒子群優(yōu)化的超球面支持向量機對不同絕緣故障類型的局部放電進行模式識別,識別率高,這對提高局部放電模式識別率具有一定的指導意義。最后,應(yīng)用時域有限差分方法的全波三維電磁場仿真軟件(XFDTD),研究局部放電產(chǎn)生的電磁波在變壓器繞組中的傳播特性。利用XFDTD構(gòu)建了變壓器繞組仿真模型,利用脈沖信號源來模擬局部放電信號,設(shè)定假想監(jiān)測點來測量電磁波衰減情況,仿真結(jié)果表明:當電磁波通過繞組時,其幅值衰減率在65%左右;當繞組線餅寬度變大時,通過繞組電磁波的幅值衰減程度越嚴重,電磁波傳播的時延差也會變大。
[Abstract]:Partial discharge (PD) is not only an important inductive factor of insulation fault of power transformer, but also a high sensitive form of insulation latent fault. Therefore, the research of PD signal detection, PD fault pattern recognition and fault location has been paid more attention. In this paper, the experimental models of four typical partial discharge faults, representing air gap discharge, corona discharge in air, surface discharge in oil and surface discharge in air, are made, and the experimental platform is built, and the original signals of typical partial discharge faults are collected. The pattern recognition of PD fault is studied. Firstly, a new method of partial discharge threshold de-noising based on complete empirical mode decomposition and total empirical mode decomposition (CEEMD-EEMD) is proposed, which is based on the characteristics of non-stationary, strong nonlinear and easily disturbed partial discharge signals. The new method and the conventional wavelet threshold denoising algorithm are used to Denoise the simulated and measured PD signals. The results show that the denoising effect of the PD threshold denoising method based on CEEMD-EEMD is better, and the effectiveness of the proposed method is verified. It lays a foundation for feature extraction and pattern recognition of partial discharge faults. Secondly, according to the partial discharge phase distribution (PRPD) pattern, the two-dimensional, three-dimensional and gray-scale maps of different insulation fault types are drawn, and the 36-dimensional statistical characteristic is reduced to 8-dimensional physical characteristic by principal component factor analysis (PCA). In order to extract the most essential information of PD fault to the maximum extent, the autoregressive model algorithm is used to extract PD signal features, and these two kinds of features are classified and recognized by BP neural network and SVM. The results show that the recognition rate of partial discharge based on the coefficients of autoregressive model is higher than that of statistical feature. Thirdly, the hyperspherical support vector machine is used to identify the partial discharge types of different insulation faults. The detection of partial discharge signals is complicated, the corresponding fault types are various, the number of partial discharge samples is limited and the characteristic quantity is nonlinear, which makes the recognition rate of BP neural network and SVM low. Based on the feature of autoregressive coefficient, a hyperspherical support vector machine based on particle swarm optimization (PSO) is used to identify the partial discharge (PD) of different insulation fault types, and the recognition rate is high. It has certain guiding significance to improve partial discharge pattern recognition rate. Finally, the full wave 3D electromagnetic field simulation software (XFDTD) is used to study the propagation characteristics of electromagnetic waves generated by partial discharge in transformer windings. The simulation model of transformer winding is constructed by using XFDTD, the partial discharge signal is simulated by pulse signal source, and the attenuation of electromagnetic wave is measured by setting the imaginary monitoring point. The simulation results show that when the electromagnetic wave passes through the winding, The attenuation rate of the amplitude is about 65%, and when the width of the winding wire-cake becomes larger, the amplitude attenuation degree of the electromagnetic wave passing through the winding becomes more serious, and the delay difference of the electromagnetic wave propagation will also become larger.
【學位授予單位】：中國礦業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM41

【參考文獻】

相關(guān)期刊論文 前10條

1 范蟠果;薛小斌;王恩俊;;基于Hu矩、Zernike矩和小波矩的局部放電識別分析[J];電氣應(yīng)用;2016年19期

2 張建文;王恩俊;陳煥栩;王曼;丁冬;;基于自回歸模型和超球面支持向量機的局部放電模式識別[J];電工電能新技術(shù);2016年09期

3 沈鑫;束洪春;曹敏;李劍;翟少磊;張林山;林中愛;;變壓器局部放電超高頻在線監(jiān)測天線研究[J];電測與儀表;2016年S1期

4 王恩俊;張建文;馬曉偉;馬鴻宇;;基于CEEMD-EEMD的局部放電閾值去噪新方法[J];電力系統(tǒng)保護與控制;2016年15期

5 范小龍;謝維成;蔣文波;李毅;黃小莉;;一種平穩(wěn)小波變換改進閾值函數(shù)的電能質(zhì)量擾動信號去噪方法[J];電工技術(shù)學報;2016年14期

6 印海洋;朱學成;李成榕;高自偉;張強;鄭書生;;基于特高頻波形形態(tài)的套管局部放電定位時延分析方法[J];高壓電器;2016年04期

7 王劉旺;朱永利;賈亞飛;李莉;;局部放電大數(shù)據(jù)的并行PRPD分析與模式識別[J];中國電機工程學報;2016年05期

8 張淑清;師榮艷;董玉蘭;李盼;任爽;姜萬錄;;雙變量小波閾值去噪和改進混沌預(yù)測模型在短期電力負荷預(yù)測中的應(yīng)用[J];中國電機工程學報;2015年22期

9 宋亞奇;周國亮;朱永利;李莉;王德文;;云平臺下并行總體經(jīng)驗?zāi)B(tài)分解局部放電信號去噪方法[J];電工技術(shù)學報;2015年18期

10 李軍浩;韓旭濤;劉澤輝;李彥明;;電氣設(shè)備局部放電檢測技術(shù)述評[J];高電壓技術(shù);2015年08期

相關(guān)博士學位論文 前5條

1 王瑜;基于支持向量機和多信息融合的局部放電故障診斷研究[D];華北電力大學;2015年

2 陶加貴;組合電器局部放電多信息融合辨識與危害性評估研究[D];重慶大學;2013年

3 李潤鑫;約束向量優(yōu)化問題的近似拉格朗日乘子和KKT條件[D];云南大學;2013年

4 嚴家明;油浸絕緣紙局部放電損傷特性研究[D];重慶大學;2011年

5 金卓睿;變壓器局部放電超高頻監(jiān)測分形天線與最優(yōu)小波去噪及信號識別研究[D];重慶大學;2008年

相關(guān)碩士學位論文 前9條

1 吳俊鋒;局部放電信號經(jīng)油紙絕緣套管向外傳播特性及檢測方法研究[D];重慶大學;2016年

2 褚鑫;油紙絕緣典型缺陷局部放電特征提取與模式識別研究[D];中國礦業(yè)大學;2015年

3 桂曉雷;基于自回歸模型深度恢復算法CUDA加速[D];天津大學;2014年

4 孫博;電力變壓器局部放電信號去噪及特征量提取方法研究[D];中國礦業(yè)大學;2014年

5 王鵬;換流變壓器局部放電超高頻傳播特性及監(jiān)測傳感器研究[D];重慶大學;2013年

6 高翔;超球支持向量機在語音識別中的應(yīng)用研究[D];太原理工大學;2011年

7 王浩;220kV變壓器內(nèi)部局部放電超寬帶射頻定位的試驗研究[D];華北電力大學（北京）;2010年

8 蔣慶華;一種基于小波變換及自回歸模型的網(wǎng)絡(luò)流量預(yù)測算法[D];吉林大學;2006年

9 楊麗君;基于局部放電特征量多元統(tǒng)計分析的油紙絕緣老化研究[D];重慶大學;2004年

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