Application and research of deep learning algorithms in the core technology of photovoltaic inverter diagnosis

This study aims to explore and analyze the application effects of deep learning algorithms in the core technology of photovoltaic (PV) inverter diagnosis. A combined approach of literature review and experimental evaluation was adopted. By organizing and summarizing relevant literature, the research direction was clarified, the research structure was outlined, and the conduct of the study was guided, thereby providing theoretical support for the research. Furthermore, experimental evaluation was conducted to test the application effects of the research content, ensuring the practical research value of the study. In this research, the direct processing of raw data and automated feature extraction capabilities of deep learning algorithms were found to be suitable for various application scenarios in PV inverter diagnosis technology. Consequently, during experimental evaluation, the Empirical Mode Decomposition-Convolutional Neural Network (EMD-CNN) demonstrated robust performance in open-circuit fault diagnosis of PV inverters, offering a practical and effective method for inverter fault diagnosis. The research findings indicate that the application of deep learning algorithms in the core technology of PV inverter diagnosis can effectively enhance the fault diagnosis efficiency and accuracy of PV inverter systems. This, in turn, strengthens the management level and maintenance quality of renewable energy PV arrays, providing stable facility support for the development and utilization of new energy sources.