Photovoltaic fault diagnosis model based on dynamic switching

Abstract

PV module fault diagnosis is mostly based on the prediction of photovoltaic module's power generation in the short term, and the fault of the module can be judged by comparing the difference between the actual power generation and the predicted power generation. However, the photoelectric conversion efficiency of photovoltaic modules varies significantly with seasons and weather, single prediction model is lack of stability, the season in a significant and a change in the weather forecast results will appear large deviation, this will lead to frequent false alarms of fault diagnosis, make power plant maintenance and repair costs unnecessarily. According to the characteristics that the photoelectric conversion efficiency of photovoltaic modules is greatly affected by weather and season, a fault diagnosis method for photovoltaic power generation based on dynamic model switching is proposed. when meet preset conditions, using the different quarters of historical data, combined with recent data to update the model, improved the fitness of model for the current weather and season. Compared with a single fixed model, the results show that the proposed method has better adaptability to different seasons and different weather.