A Fault Detection Method Based on CNN and Symmetrized Dot Pattern for PV Modules

Abstract

The photovoltaic (PV) module is a key technological advancement in renewable energy. When the PV modules fail, the overall generating efficiency will decrease, and the power system’s operation will be influenced. Hence, detecting the fault type when the PV modules are failing becomes important. This study proposed a hybrid algorithm by combining the symmetrized dot pattern (SDP) with a convolutional neural network (CNN) for PV module fault recognition. Three common faults are discussed, including poor welding, breakage, and bypass diode failure. Moreover, a fault-free module was added to the experiment for comparison. First, a high-frequency square signal was imported into the PV module, and the original signal was captured by the NI PXI-5105 high-speed data acquisition (DAQ) card for the hardware architecture. Afterward, the signal was imported into the SDP for calculation to create a snowflake image as the image feature for fault diagnosis. Finally, the PV module fault recognition was performed using CNN. There were 3200 test data records in this study, and 800 data records (200 data records of each fault) were used as test samples. The test results show that the recognition accuracy was as high as 99.88%. It is better than the traditional ENN algorithm, having an accuracy of 91.75%. Therefore, while capturing the fault signals effectively and displaying them in images, the proposed method accurately recognizes the PV modules’ fault types.