Fault Diagnosis Research of Distributed Photovoltaic System Based on Support Vector Machine

In order to improve the precision and efficiency of fault diagnosis of distributed photovoltaic (PV) systems, an efficient fault diagnosis model is constructed by combining the classification ability of support vector machine and the advantages of distributed computing. The analysis results show that the model has a classification accuracy of 97.6%, a recall of 96.5% and an F1 value of 96.8% on the experimental dataset, which can adapt to a variety of operating states and fault scenarios, and maintains a high diagnostic performance under the condition of multiple fault superposition, with a classification accuracy of 91.8%-94.2%, demonstrating a strong applicability and robustness, and providing a reliable technological support for the intelligent operation and maintenance of PV systems. It shows strong applicability and robustness, and provides reliable technical support for intelligent operation and maintenance of PV systems.