本文選題：局部放電 + 特征提取　； 參考：《中國(guó)礦業(yè)大學(xué)》2015年碩士論文

【摘要】：電力變壓器是電力系統(tǒng)中最關(guān)鍵的設(shè)備之一,其安全可靠運(yùn)行對(duì)電網(wǎng)至關(guān)重要。而局部放電是引起電力變壓器絕緣老化和絕緣故障的主要因素,不同局部放電類型對(duì)絕緣損害的程度不同,其形成的機(jī)理也各有差異。因此,針對(duì)不同的局部放電類型進(jìn)行模式識(shí)別,對(duì)快速診斷變壓器絕緣狀態(tài)及辨識(shí)故障位置具有重要意義。本文深入研究分析了局部放電機(jī)理及危害,通過搭建局部放電實(shí)驗(yàn)平臺(tái)對(duì)4種典型的缺陷模型進(jìn)行試驗(yàn),研究各種類型的放電圖譜并對(duì)提取統(tǒng)計(jì)特征參數(shù)進(jìn)行定量分析。本文主要內(nèi)容如下:首先,根據(jù)CMII標(biāo)準(zhǔn)制作了4種典型的放電模型,用于模擬不同類型的油紙絕緣變壓器局部放電故障缺陷。通過在高壓實(shí)驗(yàn)室搭建局部放電實(shí)驗(yàn)平臺(tái)及檢測(cè)電路,利用脈沖電流法檢測(cè)放電產(chǎn)生的脈沖信號(hào),并分析研究了不同種類缺陷模型的放電特性,總結(jié)其放電機(jī)理及波形特征。其次,在研究小波變換消噪方法的基礎(chǔ)上,采用db8小波對(duì)實(shí)測(cè)局部放電信號(hào)進(jìn)行5層分解,濾除含有的噪聲干擾。根據(jù)局部放電相位分布PRPD模式,繪制得到各放電類型的二維、三維譜圖,并提取能夠表征圖譜特性的30個(gè)統(tǒng)計(jì)特征,為后續(xù)量化分析識(shí)別各放電類型提供依據(jù)。第三,應(yīng)用主成分分析和核主成分析法對(duì)提取的30個(gè)統(tǒng)計(jì)特征參數(shù)進(jìn)行降維處理,并將兩者的降維效果進(jìn)行對(duì)比。結(jié)果表明:運(yùn)用主成分分析法降維后得到9個(gè)新特征量,而利用核主成分分析法降維后只需6維數(shù)據(jù)特征,且前三個(gè)主成分的累計(jì)貢獻(xiàn)率已達(dá)到降維目的。降維后的特征量明顯減少,綜合變量保留了原數(shù)據(jù)的特征信息,為后續(xù)多分類支持向量機(jī)對(duì)局部放電類型識(shí)別奠定了基礎(chǔ)。第四,構(gòu)造多分類優(yōu)化參數(shù)支持向量機(jī)SVM分類器用于不同類型局部放電的識(shí)別。首先采用網(wǎng)格搜索算法實(shí)現(xiàn)支持向量機(jī)的參數(shù)優(yōu)化,將降維后的局部放電綜合特征作為分類特征量,并將訓(xùn)練集采用5折交叉驗(yàn)證法尋找最優(yōu)訓(xùn)練SVM模型;然后,結(jié)合M-ary分類思想,將支持向量機(jī)的兩類分類問題擴(kuò)展為多類分類領(lǐng)域;最后,將測(cè)試數(shù)據(jù)分別輸入到未優(yōu)化SVM和優(yōu)化參數(shù)多分類SVM模型中進(jìn)行分類測(cè)試識(shí)別。比較各分類器的識(shí)別準(zhǔn)確率及可靠性。實(shí)驗(yàn)結(jié)果表明,該方法計(jì)算速度快,且獲得了較好地識(shí)別效果,適用于局部放電類型識(shí)別。
[Abstract]:Power transformer is one of the most important equipments in power system. Partial discharge (PD) is the main factor that causes insulation aging and insulation failure of power transformer. Different types of partial discharge have different degree of insulation damage, and their formation mechanism is different. Therefore, pattern recognition for different partial discharge types is of great significance for rapid diagnosis of transformer insulation and identification of fault location. In this paper, the mechanism and harm of partial discharge (PD) are deeply studied and analyzed. Four typical defect models are tested by setting up a PD experimental platform, and the discharge patterns of various types are studied and the statistical characteristic parameters extracted are quantitatively analyzed. The main contents of this paper are as follows: firstly, four typical discharge models are made according to CMII standard to simulate different types of partial discharge faults of oil-paper insulated transformers. By setting up a partial discharge experimental platform and detecting circuit in the high voltage laboratory, the pulse current method is used to detect the pulse signal generated from the discharge, and the discharge characteristics of different kinds of defect models are analyzed, and the discharge mechanism and waveform characteristics are summarized. Secondly, based on the research of wavelet transform denoising method, db8 wavelet is used to decompose the measured PD signal in five layers to filter the noise interference. According to the PRPD mode of partial discharge phase distribution, the 2D and 3D spectra of each discharge type are drawn, and 30 statistical features which can characterize the characteristics of the spectrum are extracted, which provide the basis for the subsequent quantitative analysis and identification of each discharge type. Thirdly, principal component analysis (PCA) and kernel principal component analysis (KPCA) are used to reduce the dimensionality of 30 statistical characteristic parameters, and the effects of them are compared. The results show that nine new characteristic quantities are obtained by using principal component analysis (PCA), but only 6 dimensional data features are needed after dimensionality reduction by kernel principal component analysis (KPCA), and the cumulative contribution rate of the first three principal components has reached the goal of dimensionality reduction. After dimensionality reduction, the feature quantity is obviously reduced, and the feature information of the original data is preserved by the comprehensive variables, which lays the foundation for the recognition of partial discharge types by the subsequent multi-classification support vector machines. Fourthly, SVM classifier based on multi-classification optimization parameter support vector machine (SVM) is constructed to identify different types of partial discharges (PD). Firstly, the parameter optimization of support vector machine is realized by using mesh search algorithm. The feature of partial discharge synthesis after dimensionality reduction is taken as the classification feature quantity, and the training set is used to find the optimal training SVM model by 5 fold cross validation method. Combined with the idea of M-ary classification, the two kinds of classification problems of support vector machine are extended to multi-class classification field. Finally, the test data are input into the unoptimized SVM model and the multi-classification SVM model with optimized parameters for classification and identification. The recognition accuracy and reliability of each classifier are compared. The experimental results show that the proposed method has high speed and good recognition effect and is suitable for partial discharge type recognition.
【學(xué)位授予單位】：中國(guó)礦業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TM855

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 張占龍;王科;唐炬;周軍;楊振山;何為;;變壓器電暈放電紫外脈沖檢測(cè)法[J];電力系統(tǒng)自動(dòng)化;2010年02期

2 唐炬;林俊亦;卓然;陶加貴;;基于支持向量數(shù)據(jù)描述的局部放電類型識(shí)別[J];高電壓技術(shù);2013年05期

3 程養(yǎng)春;刁常晉;劉少宇;鄧春;馬振麗;李成榕;;恒定電壓下油紙絕緣局部放電發(fā)展規(guī)律及缺陷狀態(tài)診斷[J];高電壓技術(shù);2013年05期

4 尚海昆;苑津莎;王瑜;靳松;;基于統(tǒng)計(jì)特征參數(shù)與相關(guān)向量機(jī)的變壓器局部放電類型識(shí)別[J];電測(cè)與儀表;2014年05期

5 唐耀華;郭為民;高靜懷;;基于核相似性差異最大化的支持向量機(jī)參數(shù)選擇算法[J];模式識(shí)別與人工智能;2010年02期

6 唐炬;黃江岸;張曉星;姚陳果;;局部放電在線監(jiān)測(cè)中混頻周期性窄帶干擾的抑制[J];中國(guó)電機(jī)工程學(xué)報(bào);2010年13期

相關(guān)碩士學(xué)位論文 前1條

1 鄭升訊;基于混沌特性的變壓器局部放電特征提取及絕緣劣化的診斷[D];重慶大學(xué);2010年

，

本文編號(hào)：1928329