【摘要】：局部放電與電氣設(shè)備內(nèi)部絕緣材料劣化和絕緣體的擊穿密切相關(guān)。不同類型的局部放電產(chǎn)生的機理和發(fā)生的位置各不相同,對設(shè)備絕緣的破壞程度也不盡相同。因此,準(zhǔn)確,快速地識別局部放電類型對電氣設(shè)備運行維護(hù)人員確定放電位置,合理安排檢修計劃有著重要意義。本文在分析局部放電特性基礎(chǔ)上主要研究了電力設(shè)備局部放電信號的特征提取及分類方法,主要工作內(nèi)容如下:提出了一種基于變分模態(tài)分解和多尺度排列熵的特征提取方法。利用變分模態(tài)分解算法對實驗室條件下采集的4種局部放電信號進(jìn)行分解,得到數(shù)個包含不同頻帶信息的有限帶寬的固有模態(tài)分量。求得相應(yīng)的多尺度排列熵,并將其組合成原始特征量。同時使用最大相關(guān)最小冗余準(zhǔn)則對原始特征量進(jìn)行優(yōu)選降維。最后使用支持向量機分類器實現(xiàn)分類。該方法提取的特征能夠有效表征PD信號在不同頻帶下的不確定性和復(fù)雜性,并且魯棒性強,識別率高。研究并建立了基于變量預(yù)測模型的局部放電模式識別算法(VPMCD)。從放電信號中提取37維統(tǒng)計特征量和9維時頻特征量,使用VPMCD完成局部放電信號的分類。實驗結(jié)果表明,VPMCD算法在識別率和計算效率均高于BP神經(jīng)網(wǎng)絡(luò)和支持向量機。針對局部放電有效樣本過少而導(dǎo)致分類器性能下降的問題,提出一種基于改進(jìn)的VPMCD方法。該方法利用正交完備基函數(shù)建立特征值的預(yù)測模型,通過網(wǎng)格搜索策略和移動最小二乘法求解模型參數(shù),提高了模型的精度。在只有較少局部放電樣本的情況下,改進(jìn)的VPMCD算法的識別正確率要高于VPMCD、BP神經(jīng)網(wǎng)絡(luò)和SVM算法。
[Abstract]:Partial discharge is closely related to internal insulation material deterioration and insulator breakdown. The mechanism and location of partial discharge are different, and the damage degree of insulation is different. Therefore, accurate and rapid identification of partial discharge types is of great significance for electrical equipment maintenance personnel to determine the discharge location and arrange the maintenance plan reasonably. Based on the analysis of PD characteristics, this paper mainly studies the feature extraction and classification of PD signals in power equipment. The main work is as follows: a feature extraction method based on variational mode decomposition and multi-scale permutation entropy is proposed. The variational mode decomposition (VMD) algorithm is used to decompose the four PD signals collected under laboratory conditions, and several inherent modal components with limited bandwidth are obtained. The corresponding multi-scale permutation entropy is obtained and combined into the original characteristic quantity. At the same time, the maximum correlation minimum redundancy criterion is used to optimize and reduce the dimension of the original feature. Finally, support vector machine classifier is used to realize classification. The features extracted by this method can effectively represent the uncertainty and complexity of PD signals in different frequency bands, and have strong robustness and high recognition rate. The partial discharge pattern recognition algorithm (VPMCD). Based on variable prediction model is studied and established. The 37-dimensional statistical characteristic and 9-dimensional time-frequency characteristic are extracted from the discharge signal, and the partial discharge signal is classified by VPMCD. The experimental results show that the recognition rate and computational efficiency of VPMCD algorithm are higher than those of BP neural network and support vector machine. A modified VPMCD method is proposed to solve the problem that the performance of classifier is degraded due to the small number of effective partial discharge samples. In this method, the prediction model of eigenvalue is established by orthogonal complete basis function, and the precision of the model is improved by solving the model parameters by grid search strategy and moving least square method. The recognition accuracy of the improved VPMCD algorithm is higher than that of the VPMCDBP-BP neural network and the SVM algorithm with only a small number of PD samples.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM855

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 陳攀;姚陳果;廖瑞金;陳昱;米彥;;分頻段能量譜及馬氏聚類算法在開關(guān)柜局部放電模式識別中的應(yīng)用[J];高電壓技術(shù);2015年10期

2 黃亮;唐炬;凌超;張曉星;;基于多特征信息融合技術(shù)的局部放電模式識別研究[J];高電壓技術(shù);2015年03期

3 王余奎;李洪儒;葉鵬;;基于多尺度排列熵的液壓泵故障識別[J];中國機械工程;2015年04期

4 唐炬;樊雷;張曉星;劉欣;;用諧波小波包變換法提取GIS局部放電信號多尺度特征參數(shù)[J];電工技術(shù)學(xué)報;2015年03期

5 汪可;廖瑞金;王季宇;楊麗君;李劍;;局部放電UHF脈沖的時頻特征提取與聚類分析[J];電工技術(shù)學(xué)報;2015年02期

6 張曉星;舒娜;徐曉剛;李鑫;唐炬;;基于三維譜圖混沌特征的GIS局部放電識別[J];電工技術(shù)學(xué)報;2015年01期

7 尚海昆;苑津莎;王瑜;張利偉;;多核多分類相關(guān)向量機在變壓器局部放電模式識別中的應(yīng)用[J];電工技術(shù)學(xué)報;2014年11期

8 任靜波;孫根正;陳冰;羅明;;基于多尺度排列熵的銑削顫振在線監(jiān)測方法[J];機械工程學(xué)報;2015年09期

9 尚海昆;苑津莎;王瑜;靳松;;基于交叉小波變換和相關(guān)系數(shù)矩陣的局部放電特征提取[J];電工技術(shù)學(xué)報;2014年04期

10 饒國強;馮輔周;司愛威;謝金良;;排列熵算法參數(shù)的優(yōu)化確定方法研究[J];振動與沖擊;2014年01期

相關(guān)博士學(xué)位論文 前3條

1 尹金良;基于相關(guān)向量機的油浸式電力變壓器故障診斷方法研究[D];華北電力大學(xué);2013年

2 李堅;代理模型近似技術(shù)研究及其在結(jié)構(gòu)可靠度分析中的應(yīng)用[D];上海交通大學(xué);2013年

3 王聚豐;插值型移動最小二乘法及其無網(wǎng)格方法的誤差估計[D];上海大學(xué);2013年

，

本文編號：2154331