Development of Intelligent Fault Diagnosis and Predictive Maintenance Algorithm in Photovoltaic Power Generation System

With the global awareness of renewable energy and environmental protection rising, photovoltaic power generation system (PPGS), as an important part of clean energy, its stable operation and fault management has become a research hotspot. Aiming at the problem of fault diagnosis and predictive maintenance in PPGS, this paper proposes an intelligent fault diagnosis and predictive maintenance algorithm based on deep learning and time series analysis technology. By monitoring the operation data of key equipment in PPGS in real time, the algorithm uses features extraction techniques such as wavelet transform (WT) and deep neural network (DNN) model to automatically learn the deep-level features in the data, thus realizing accurate fault prediction and health status assessment of photovoltaic inverter. In order to quantitatively evaluate the health status of inverter, this study constructs the health status index (HSI) based on the output of deep learning model, and uses ARIMA model to predict the trend of HSI, so as to warn the possible faults in advance. The experimental results show that the proposed algorithm can effectively reflect the health state changes of the inverter, which provides strong support for preventive maintenance. This algorithm is superior to the comparison algorithm in the accuracy and timeliness of fault prediction, and has high feasibility and practical application value.