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一種光伏陣列串聯電弧故障智能檢測方法

來源：電工電氣發布時間：2023-02-06 16:06 瀏覽次數：671

一種光伏陣列串聯電弧故障智能檢測方法

金輝，高偉，楊耿杰

(福州大學電氣工程與自動化學院，福建福州 350108)

摘要：由于串聯電弧故障特征表現不足以及樣本不平衡的問題，導致傳統的診斷算法檢測效果不佳。提出了一種基于圖像識別的光伏陣列串聯電弧故障診斷方法：利用格拉姆角和場(GASF)將發生串聯電弧故障時的暫態電流數據編碼為二維圖像，從而放大電弧故障的本質特征；深度卷積生成對抗網絡(DCGAN)被用來增擴電弧故障 GASF 特征圖像，以均衡正常與故障樣本數量；訓練一個 LeNet-5 診斷模型完成電弧故障的識別。經過實驗驗證，所提方法有效提升了光伏陣列串聯電弧故障的辨識度，且具備優秀的抗干擾能力，對實測數據的整體識別準確率高達99.5%。

關鍵詞： 光伏陣列；串聯電弧故障；格拉姆角和場；深度卷積生成對抗網絡

中圖分類號：TM615 文獻標識碼：A 文章編號：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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一種光伏陣列串聯電弧故障智能檢測方法