The current–voltage characteristics (I–V curves) of photovoltaic (PV) modules contain a lot of information about their health. In the literature, only partial information from the I–V curves is used for diagnosis. In this study, a methodology is developed to make full use of I–V curves for PV fault diagnosis. In the pre-processing step, the I–V curve is first corrected and resampled. Then fault features are extracted based on the direct use of the resampled vector of current or the transformation by Gramian angular difference field or recurrence plot. Six machine learning techniques, i.e., artificial neural network, support vector machine, decision tree, random forest, k-nearest neighbors, and naive Bayesian classifier are evaluated for the classification of the eight conditions (healthy and seven faulty conditions) of PV array. Special effort is paid to find out the best performance (accuracy and processing time) when using different input features combined with each of the classifier. Besides, the robustness to environmental noise and measurement errors is also addressed. It is found out that the best classifier achieves 100% classification accuracy with both simulation and field data. The dimension reduction of features, the robustness of classifiers to disturbance, and the impact of transformation are also analyzed.

光伏 (PV) 模块的电流-电压特性（I-V 曲线）包含许多有关其健康状况的信息。在文献中，只有来自 I-V 曲线的部分信息用于诊断。在这项研究中，开发了一种方法来充分利用 I-V 曲线进行光伏故障诊断。在预处理步骤中，首先对 I-V 曲线进行校正和重新采样。然后直接利用重采样的电流矢量或通过格拉姆角差场或递推图变换提取故障特征。六种机器学习技术，即人工神经网络、支持向量机、决策树、随机森林、k-最近邻和朴素贝叶斯分类器被评估用于光伏阵列的八种条件（健康和七种故障条件）的分类。当使用不同的输入特征与每个分类器相结合时，特别努力找出最佳性能（精度和处理时间）。此外，还解决了对环境噪声和测量误差的鲁棒性问题。结果表明，最好的分类器在模拟和现场数据下都能达到 100% 的分类准确率。还分析了特征的降维、分类器对干扰的鲁棒性以及变换的影响。