Advanced Diagnostics Methods for Hybrid Photovoltaic Systems

Stability of the power converters is therefore crucial in the effective performance of stand-alone hybrid PV systems. This research provides a diagnostic framework that is specifically designed for power converters in such systems. The new approach is based on the implementation of enhanced monitoring methods, fault diagnostics, and prognosis of maintenance actions to improve the system dependability and performance. The offered diagnostic framework is tested under different operating conditions and fault cases based on MATLAB for the investigation of diagnostic framework performance. The results obtained by the fault detection algorithms are quite noticeable. The statistical deviation method yielded an FDR of 92%, FAR of 5%, MTTD of 17 min, and a mean time to repair of 22 min. The performance of model-based diagnosis was evaluated in terms of FDR, FAR, MTTD, and MTTR, and it was recorded as 88%, 7%, 21 min, and 25 min, respectively. The outcomes emphasize the significance of high efficiency in the identification of the fault in enhancing the performance and dependability of standalone hybrid PV systems. The finally designed fault detection algorithms are characterized by high FDR, which, in turn, would ensure the correct identification of the faults and the reliability of the system, while low FAR values would point out the effectiveness of these algorithms in detecting the real anomalies.