A Photovoltaic System Fault Identification Method Based on Improved Deep Residual Shrinkage Networks
Fengxin Cui, Yanzhao Tu, Wei Gao
Abstract
With the increasing installed capacity of photovoltaic (PV) power generation, it has become a significant challenge to detect abnormalities and faults of PV modules in a timely manner. Considering that all the fault information of the PV module is contained in the current-voltage (I-V) curve, this pioneering study takes the I-V curve as the input and proposes a PV-fault identification method based on improved deep residual shrinkage networks (DRSN). This method can not only identify single faults (e.g., short-circuit, partial-shading, and abnormal aging), but also effectively identify the simultaneous existence of hybrid faults. Moreover, it can achieve end-to-end fault diagnosis. The diagnostic accuracy of the proposed method on the measured data reaches 97.73%, is better than the convolutional neural network (CNN), the support vector machine (SVM), the deep residual network (ResNet), and the stage-wise additive modeling using multi-class exponential loss function based on the classification and regression tree (SAMME-CART). In addition, the possibility of the aforementioned method running on the Raspberry Pi has been verified in this study, which is of great significance for realizing the edge diagnosis of PV fault.