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

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交流XLPE電纜典型絕緣缺陷的PD特性與類型識(shí)別

來源:電工電氣發(fā)布時(shí)間:2020-06-18 15:18 瀏覽次數(shù):996
交流XLPE電纜典型絕緣缺陷的PD特性與類型識(shí)別
 
何若冰1,陳佳2,朱勁松1,楊旭2,姚雨杭3,潘成3,唐炬3
(1 廣東電網(wǎng)有限責(zé)任公司陽(yáng)江供電公司,廣東 陽(yáng)江 529500;2 國(guó)網(wǎng)電力科學(xué)研究院武漢南瑞有限責(zé)任公司,湖北 武漢 430074;
3 武漢大學(xué) 電氣與自動(dòng)化學(xué)院,湖北 武漢 430072)
 
    摘 要:針對(duì)交聯(lián)聚乙烯(XLPE) 電纜及其附件常見的9種絕緣缺陷類型,制作了相應(yīng)的缺陷模型,研究了9種缺陷在不同電壓下的局部放電特性。發(fā)現(xiàn)不同缺陷的譜圖形狀、放電的相位分布等表現(xiàn)出不同特點(diǎn),每種缺陷的放電重復(fù)率與平均放電量均隨著電壓的升高而增大,其中氣隙缺陷的最大放電量和放電重復(fù)率高于其他缺陷,電樹枝缺陷的放電重復(fù)率最低。對(duì)不同缺陷的局部放電譜圖進(jìn)行了特征量提取,并利用基于L-M算法的BP神經(jīng)網(wǎng)絡(luò),實(shí)現(xiàn)了故障類型的識(shí)別,最低識(shí)別率達(dá)到89.17%,取得了較好的識(shí)別效果。
    關(guān)鍵詞:交聯(lián)聚乙烯(XLPE) 電纜;附件;交流電壓;絕緣缺陷;局部放電;故障識(shí)別
    中圖分類號(hào):TM247;TM855     文獻(xiàn)標(biāo)識(shí)碼:A     文章編號(hào):1007-3175(2020)06-0005-09
 
Partial Discharge Characteristics and Type Identification of Typical Insulation Defects of AC XLPE Cables
 
HE Ruo-bing1, CHEN Jia2, ZHU Jin-song1, YANG Xu2, YAO Yu-hang3, PAN Cheng3, TANG Ju3
(1 Guangdong Power Grid Co.,Ltd, Yangjiang Power Supply Company, Yangjiang 529500, China;
2 Wuhan Nari Limited Liability Company of State Grid Electric Power Research Institute, Wuhan 430074, China;
3 School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China)
 
    Abstract: This paper aims at the nine types of insulation defects of AC XLPE cables and accessories, corresponding defect models were made, and the partial discharge characteristics of nine defects at different voltages were studied. It was found that the shape of the spectrum of different defects and the phase distribution of the discharge have different characteristics. The discharge repetition rate and average discharge of each defect increased with the increasing voltage. Among them, the maximum discharge amount and discharge repetition rate of insulation cavity defects were higher than other defects, and the discharge repetition rate of electrical tree defects was the lowest. Feature quantities were extracted from the partial discharge spectra of different defects, and the BP neural network based on the L-M algorithm was used to realize the fault type identification. The minimum recognition rate was 89.17%, and a good recognition effect was achieved.
    Key words: XLPE cable; accessories; AC voltage; insulation defects; partial discharge; fault identification
 
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