Research on Fault Diagnosis of Photovoltaic Array Based on Hunter-prey Optimization Support Vector Machine

To enhance the operational efficiency of photovoltaic power plants, extensive research has been conducted on fault detection methods for photovoltaic arrays, and a fault detection method based on hunter-prey optimization support vector machine (HPO-SVM) has been established. This method utilizes five parameters of the photovoltaic array model as fault feature vectors for fault detection, and evaluates the effectiveness and reliability of the method based on simulation data and experimental data. When conducting fault detection based on simulation data, the recognition accuracy of HPO-SVM reached 99.5556%, with an average accuracy of 98.2641% in 10-fold cross-validation. Compared to support vector machine (SVM), the accuracy of HPO-SVM improved by 8.4445% and 8.6086% respectively. Similarly, on experimental data, the HPO-SVM achieves a recognition accuracy of 98.0645%, with an average accuracy of 94.2995% in 10-fold cross-validation, surpassing the SVM method by 7.7419% and 4.5702% respectively. These results highlight the superior accuracy, reliability, and generalization performance of the HPO-SVM approach.