基于伪标签-1D DenseNet-KNN的光伏阵列开集复合故障诊断方法

提出了一种基于伪标签-1D DenseNet-KNN的光伏阵列故障诊断方法，实现在少标签样本下的光伏阵列复合故障开集识别。首先，分析了各种常见单一故障及灰尘覆盖下复合故障的I-V曲线特性。然后，为了克服常规的半监督机器学习算法需手动提取数据特征，采用一种伪标签与1D DenseNet相结合的半监督方法自动提取特征。最后，将对训练数据提取的特征、训练数据预测的标签及测试样本提取的特征输入K最近邻（KNN）算法进行开集复合故障诊断。实验表明，该方法不仅能准确分类各种已知类样本，而且能识别出未知类别故障，并且模型的训练仅需少量标签数据。

Photovoltaic array open set compound fault diagnosis based on pseudo-label-1D DenseNet-KNN

A fault diagnosis method based on pseudo-label-1D DenseNet-KNN with fewer labeled samples is proposed to classify open-set of compound faults for photovoltaic arrays. First, the I-V characteristic curves of various common single faults and dust covered compound faults are analyzed. Since the semi-supervised machine learning algorithms need to manually extract some features from the data, then a semi-supervised method combining pseudo labels with 1D DenseNet is used to automatically extract features. Finally, the features extracted from the training data, the predicted labels of the training data and the features extracted from the test samples are inputted into the K-Nearest Neighbor(KNN) algorithm for open set compound fault diagnosis. Experiments show that this method can not only classify various known class samples accurately, but also identify unknown class faults, and the training of the model only needs a small amount of labeled data.