Enhancing Fault Detection and Classification in Grid-Tied Solar Energy Systems Using Radial Basis Function and Fuzzy Logic-Controlled Data Switch

This study integrates fuzzy logic-controlled data switching and the radial basis function neural network (RBFNN) for fault detection and classification in grid-tied solar energy systems. The fuzzy logic controller fil-ters out invalid sensor data through a data switch, en-suring that the fault detection and classification system receives reliable input. Training data were prepared through data normalization using the [-score function and principal component analysis, thereby reducing data complexity and standardizing the inputs. The re-sulting RBFNN model exhibited a low mean squared error with a value of 7.67 x 10-4, indicating its ability to classify faults based on the actual system scenarios. Performance evaluation metrics, including accuracy, precision, recall, and F1-score, were used to assess the effectiveness of the RBFNN model. The model demon-strated high accuracy (96.4%), precision (98.281%), recall (98.013%), and F1-score (98.147%), indicating the suitability and effectiveness of the RBFNN model to identify and classify faults in grid-tied solar energy systems.