Photovoltaic Fault Diagnosis Via Semisupervised Ladder Network With String Voltage and Current Measures

Abstract

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.