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New Feature Extraction Method for Photovoltaic Array Output Time Series and Its Application in Fault Diagnosis
IEEE Journal of Photovoltaics ( IF 3 ) Pub Date : 2020-07-01 , DOI: 10.1109/jphotov.2020.2981833
Honglu Zhu , Yucheng Shi , Haizheng Wang , Lingxing Lu

Photovoltaic array produces massive running data, and such data are time series of strong coupling features with each other. In addition, photovoltaic output data has a strong fluctuating and nonlinear feature; it brings extra difficulty to photovoltaic array fault feature extraction and its fault diagnosis. To solve these problems, this article proposes a fault diagnosis method using the time series features for photovoltaic array. The features of the photovoltaic array output are described in this article. From the perspective of distance analysis and similarity analysis, this article proposes a feature extraction method for photovoltaic array output time series, and features of output time series under different fault conditions are analyzed. Taking similarity index and distance index as input, the fuzzy system is built for identifying faults for photovoltaic array. The operational data analysis shows that the time series feature indexes proposed can successfully characterize different fault types, and this method can effectively diagnose typical faults of photovoltaic array.

中文翻译:

光伏阵列输出时间序列特征提取新方法及其在故障诊断中的应用

光伏阵列产生海量运行数据,这些数据是相互间具有强耦合特征的时间序列。此外,光伏输出数据具有很强的波动性和非线性特征;这给光伏阵列故障特征提取及其故障诊断带来了额外的困难。针对这些问题,本文提出了一种利用时间序列特征对光伏阵列进行故障诊断的方法。本文介绍了光伏阵列输出的特点。本文从距离分析和相似度分析的角度,提出了光伏阵列输出时间序列的特征提取方法,分析了不同故障条件下输出时间序列的特征。以相似度指数和距离指数为输入,模糊系统被建立用于识别光伏阵列的故障。运行数据分析表明,所提出的时间序列特征指标能够成功表征不同故障类型,该方法能够有效诊断光伏阵列典型故障。
更新日期:2020-07-01
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