Multi Classification Online Monitoring of Photovoltaic Array Fault Diagnosis Algorithm

With the rapid development of photovoltaic power generation, the fault of photovoltaic array is receiving increasing attention. In response to this issue, proposes an online monitoring photovoltaic array fault diagnosis algorithm based on Support Vector Machines. Firstly, collect the voltage and current of the photovoltaic array for feature extraction. Next, the extracted feature data is normalized and input into a SVM to train the classifier and identify and judge the faults that occur. In the experiment, simulation datasets were used for testing, and online detection and verification were conducted on the real datasets generated by the actual prototype. The results showed that this method can effectively diagnose photovoltaic array faults, with high accuracy and reliability. The online detection photovoltaic array fault diagnosis method based on support vector machine proposed in this paper has high fault detection rate and accuracy, which can provide technical support and guarantee for the maintenance and management of photovoltaic power generation system.