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

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一種光伏陣列串聯(lián)電弧故障智能檢測方法

來源：電工電氣發(fā)布時(shí)間：2023-02-06 16:06 瀏覽次數(shù)：468

一種光伏陣列串聯(lián)電弧故障智能檢測方法

金輝，高偉，楊耿杰
(福州大學(xué) 電氣工程與自動(dòng)化學(xué)院，福建 福州 350108)
 
    摘 要：由于串聯(lián)電弧故障特征表現(xiàn)不足以及樣本不平衡的問題，導(dǎo)致傳統(tǒng)的診斷算法檢測效果不佳。提出了一種基于圖像識(shí)別的光伏陣列串聯(lián)電弧故障診斷方法：利用格拉姆角和場(GASF)將發(fā)生串聯(lián)電弧故障時(shí)的暫態(tài)電流數(shù)據(jù)編碼為二維圖像，從而放大電弧故障的本質(zhì)特征；深度卷積生成對(duì)抗網(wǎng)絡(luò)(DCGAN)被用來增擴(kuò)電弧故障 GASF 特征圖像，以均衡正常與故障樣本數(shù)量；訓(xùn)練一個(gè) LeNet-5 診斷模型完成電弧故障的識(shí)別。經(jīng)過實(shí)驗(yàn)驗(yàn)證，所提方法有效提升了光伏陣列串聯(lián)電弧故障的辨識(shí)度，且具備優(yōu)秀的抗干擾能力，對(duì)實(shí)測數(shù)據(jù)的整體識(shí)別準(zhǔn)確率高達(dá)99.5%。
    關(guān)鍵詞： 光伏陣列；串聯(lián)電弧故障；格拉姆角和場；深度卷積生成對(duì)抗網(wǎng)絡(luò)
    中圖分類號(hào)：TM615     文獻(xiàn)標(biāo)識(shí)碼：A     文章編號(hào)：1007-3175(2023)01-0043-05
 
An Intelligent Detection Method for Series Arc Fault of Photovoltaic Array
 
JIN Hui, GAO Wei, YANG Geng-jie
（College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China）
 
    Abstract: The traditional diagnostic algorithms have poor performance because of inadequate characteristic expression of series arc fault (SAF) and sample imbalance. A detection method for series arc fault of photovoltaic array based on image recognition is put forward. First,according to Gramian angular summation field (GASF), the paper encodes the transient current data of SAF into two-dimensional image which amplifies the essential characteristics of SAF. Second, the deep convolution generative adversarial network (DCGAN) is adopted to enlarge GASF fault characteristic expression image of SAF to achieve balance between normal and fault sample numbers. Finally, a LeNet-5 diagnostic model is trained to recognize SAF. The experimental results show that this method efficiently improves the SAF of photovoltaic arrays identification accuracy to 99.5% and has great anti-interference ability.
    Key words: photovoltaic array; series arc fault; Gramian angular summation field; deep convolution generative adversarial network
 
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