Photovoltaic Module Fault Detection Technology Based on Remote Sensing Technology and Deeplabv3+ Model

With the rapid advancement of renewable energy, the fault detection of photovoltaic modules has become a key link to ensure their efficient operation. The study first utilizes remote sensing technology to obtain high-resolution images of photovoltaic power plants, and then uses the Deeplabv3+model to segment the images and identify faulty components. Combining remote sensing technology with Deeplabv3+ model, fast and accurate photovoltaic module fault detection can be achieved. The research results indicated that bilateral filtering and gamma filtering algorithms showed superior performance in testing, with the highest indicator evaluation results. Meanwhile, the structural similarity index value was also very close to 1. The error of the improved K-means was mainly concentrated between 0.000 and 0.014, while the traditional K-means was mainly distributed between 0.022 and 0.051. The statistical test results showed that the improved K-means algorithm was significantly better than the traditional K-means in clustering accuracy, and its average error was only 0.008, which was much lower than the 0.035 of the traditional K-means. The bilateral filtering showed the highest accuracy, with a classification result of 88%. This method can detect faults in photovoltaic modules, which has significant advantages over traditional methods. It provides a new and efficient method for photovoltaic module fault detection, which helps to optimize the operational efficiency and reliability of photovoltaic power plants and promote the sustainable development of renewable energy.