Suzhou Electric Appliance Research Institute
期刊號(hào): CN32-1800/TM| ISSN1007-3175

Article retrieval

文章檢索

首頁(yè) >> 文章檢索 >> 往年索引

基于多源數(shù)據(jù)融合的變壓器典型故障診斷模型研究

來(lái)源:電工電氣發(fā)布時(shí)間:2023-07-27 12:27 瀏覽次數(shù):280

基于多源數(shù)據(jù)融合的變壓器典型故障診斷模型研究

胡晨1,尹恩韜1,樂(lè)健2
(1 國(guó)網(wǎng)江西省電力有限公司吉安供電公司,江西 吉安 343000;
2 武漢大學(xué) 電氣與自動(dòng)化學(xué)院,湖北 武漢 430072)
 
    摘 要:準(zhǔn)確評(píng)估輸變電設(shè)備運(yùn)行狀態(tài)是電力企業(yè)生產(chǎn)技術(shù)工作的核心內(nèi)容。為提高電力變壓器故障診斷精度,對(duì)典型變壓器故障特征理論進(jìn)行研究,建立了區(qū)內(nèi)和區(qū)外的故障仿真模型,在此基礎(chǔ)上提出了基于多源數(shù)據(jù)融合的變壓器典型故障診斷模型。模型采用小波包分析法提取故障特征量,并進(jìn)行特征融合。實(shí)驗(yàn)結(jié)果表明,所提的變壓器故障判別策略判斷結(jié)果更加精確且診斷時(shí)間較快。
    關(guān)鍵詞: 變壓器;故障診斷;數(shù)據(jù)特征;數(shù)據(jù)融合
    中圖分類(lèi)號(hào):TM407     文獻(xiàn)標(biāo)識(shí)碼:A     文章編號(hào):1007-3175(2023)07-0041-05
 
Research on Typical Fault Diagnosis Model of
Transformers Based on Multi-Source Data Fusion
 
HU Chen1, YIN En-tao1, LE Jian2
(1 Ji’an Power Supply Company of Jiangxi Electric Power Co., Ltd, Ji’an 343000, China;
2 School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)
 
    Abstract: Accurate evaluation of operation status of power transmission and transformation equipments is the core of production technology for electric power enterprises. In order to improve the accuracy of power transformer fault diagnosis, the paper makes research on typical transformer fault characteristics theories, builds fault simulation models in and out of the region, and then puts forward a typical faults diagnosis model of transformers based on multi-source data fusion. This model adopts the wavelet packet analysis method to extract fault characteristic quantity and makes them fused. According to the experimental results, this transformer fault diagnosis strategy is more accurate with less diagnosis time.
    Key words: transformer; fault diagnosis; data characteristics; data fusion
 
參考文獻(xiàn)
[1] 劉云鵬,和家慧,許自強(qiáng),等.基于 SVM SMOTE 的電力變壓器故障樣本均衡化方法[J].高電壓技術(shù),2020,46(7):2522-2529.
[2] 方健,楊帆,童銳,等. 基于紅外圖像處理和半監(jiān)督學(xué)習(xí)的變壓器故障診斷方法[J] . 全球能源互聯(lián)網(wǎng)(英文版),2021,4(6):596-607.
[3] 李兵,韓睿,何怡剛,等.改進(jìn)隨機(jī)森林算法在電機(jī)軸承故障診斷中的應(yīng)用[J].中國(guó)電機(jī)工程學(xué)報(bào),2020,40(4):1310-1319.
[4] 唐文虎,牛哲文,趙柏寧,等. 數(shù)據(jù)驅(qū)動(dòng)的人工智能技術(shù)在電力設(shè)備狀態(tài)分析中的研究與應(yīng)用[J] .高電壓技術(shù),2020,46(9):2985-2999.
[5] ZHANG C , TAN K C , LI H , et a1. Acost-sensitive deep belief network for imbalanced classification[J].IEEE Transactions on Neural Networks and Learning Systems,2019,30(1):109-122.
[6] 陳顥,王伏亮,李澄,等. 基于 KPCA-LSSVM 的智能斷路器故障診斷方法研究[J] . 自動(dòng)化儀表,2021,42(12):19-22.
[7] 陳如清,李嘉春,尚濤,等. 改進(jìn)煙花算法和概率神經(jīng)網(wǎng)絡(luò)智能診斷齒輪箱故障[J] . 農(nóng)業(yè)工程學(xué)報(bào),2018,34(17):192-198.
[8] 畢建權(quán),鹿鳴明,郭創(chuàng)新,等. 一種基于多分類(lèi)概率輸出的變壓器故障診斷方法[J].電力系統(tǒng)自動(dòng)化,2015,39(5):88-93.
[9] 蘇磊,陳璐,徐鵬,等. 基于 GRNN 和 KPCA 組合模型的變壓器油中氣體體積分?jǐn)?shù)短期預(yù)測(cè)[J] . 高壓電器,2021,57(1):82-88.
[10] 陳鐵,呂長(zhǎng)欽,張欣,等. 基于 KPCA-WPA-SVM 的變壓器故障診斷模型[J] . 電測(cè)與儀表,2021,58(4):158-164.
[11] 吳君,丁歡歡,馬星河,等. 改進(jìn)自適應(yīng)蜂群優(yōu)化算法在變壓器故障診斷中的應(yīng)用[J] . 電力系統(tǒng)保護(hù)與控制,2020,48(9):174-180.
[12] 吳杰康,覃煒梅,梁浩浩,等. 基于自適應(yīng)極限學(xué)習(xí)機(jī)的變壓器故障識(shí)別方法[J] . 電力自動(dòng)化設(shè)備,2019,39(10):181-186.
[13] HEIDARI A A, MIRJALILI S, FARIS H,et al.Harris hawks optimization:Algorithm and applications[J].Future Generation Computer Systems,2019,97:849-872.
[14] SAHRI Z B , YUSOF R B . Support vector machine-based fault diagnosis of power transformer using k nearest-neighbor imputed DGA dataset [J] . Journal of Computer & Communications,2014,2(9):22-31.
[15] 楊凌霄,朱亞麗. 基于概率神經(jīng)網(wǎng)絡(luò)的高壓斷路器故障診斷[J] . 電力系統(tǒng)保護(hù)與控制,2015,43(10):62-67.