In recent years, many supervised learning algorithms have been successfully applied for photovoltaic (PV) fault diagnosis. In practice, it is not possible to effectively obtain labels of large samples, limiting the engineering application of these algorithms. As for the unsupervised learning algorithm, it is completely adaptive learning, requiring a large number of samples to better learn the potential features in the data. To address the above problems, an improved online fault diagnosis method is proposed, which uses a small number of labeled samples to train the semisupervised ladder network (SSLN) fault diagnosis model to realize the diagnosis of line-to-line faults, open-circuit faults, partial shadow faults, and hybrid faults. In the proposed method, only the real-time operating voltage and current of PV array are needed for fault diagnosis. The sequential voltage and current of the PV array are first normalized, and the sequential power waveforms are obtained through numerical calculation. Then, the SSLN is used to extract the fault features from the sequence power waveforms. Finally, the classification is realized using the SSLN's noiseless encoder. To eliminate overfitting and improve convergence, the activation function, optimizer, and loss function of the SSLN is studied and improved. Meanwhile, numerical simulations and measured data verify that the proposed method provides strong anti-interference, and the diagnostic accuracies of both exceed 98%. Comparative experiments show that the proposed method outperforms algorithms such as squared-loss mutual information regularization, semisupervised support vector machine, graph-based semisupervised learning, and semisupervised extreme learning machine.

中文翻译：

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通过带电压和电流测量的半监督梯形网络进行光伏故障诊断

近年来，许多监督学习算法已成功应用于光伏（PV）故障诊断。在实践中，无法有效获取大样本的标签，限制了这些算法的工程应用。至于无监督学习算法，完全是自适应学习，需要大量的样本才能更好地学习数据中的潜在特征。针对上述问题，提出一种改进的在线故障诊断方法，利用少量标记样本训练半监督梯形网络（SSLN）故障诊断模型，实现线间故障、开路故障诊断。断层、部分阴影断层和混合断层。在所提出的方法中，故障诊断只需要光伏阵列的实时工作电压和电流。首先对光伏阵列的时序电压和电流进行归一化，通过数值计算得到时序功率波形。然后，使用 SSLN 从序列电源波形中提取故障特征。最后，使用 SSLN 的无噪声编码器实现分类。为消除过拟合和提高收敛性，对SSLN的激活函数、优化器和损失函数进行了研究和改进。同时，数值模拟和实测数据验证了该方法具有较强的抗干扰性，两者的诊断准确率均超过98%。对比实验表明，该方法优于平方损失互信息正则化、半监督支持向量机、基于图的半监督学习、