Development of a Hybrid DLDH Fault Detection and Localization Algorithm for Two Types of PV Technologies with Experimental Validation

The work lies in its ability to detect and locate photovoltaic (PV) (DLD) faults, which is essential for diagnosing and monitoring the efficiency of a PV field. The paper focuses on presenting the DLD algorithm, providing details on the approach and implementation of the diagnostic algorithm based on the fault detection and localization flowchart. To enhance diagnostic accuracy and fault detection, a hybrid diagnostic method is employed, combining fuzzy logic with the thresholding method. Additionally, fault location diagrams for monocrystalline and polycrystalline PV modules are developed. The algorithm developed allows for the differentiation of defects with the same signature (behavior). This is achieved by introducing a range of symptoms and applying an intelligent method alongside the "threshold method" to distinguish between faults with identical symptom signatures. The proposed algorithm's effectiveness relies on prior knowledge and definitions of the field's behavior regarding various types of defects and the designation of symptoms for each defect. Experimental validation is provided for specific faults.