A Hybrid Framework for Photovoltaic Monitoring and Fault Diagnosis: I–V Curve Analysis, IEC 60891 Standardization, and TLBO-Based Single-Diode Model Parameter Estimation

The rapid expansion of photovoltaic (PV) installations necessitates advanced monitoring solutions to ensure optimal performance, early fault detection, and long-term reliability. This study presents a hybrid methodology for real-time PV module monitoring and fault diagnosis, integrating I–V curve analysis, IEC 60891 standardization, and Teaching–Learning-Based Optimization (TLBO) to estimate parameters of the Single Diode Model (SDM). An I-V Swinger-based test bench equipped with Arduino data acquisition and environmental sensors was developed to measure I–V characteristics under various operating conditions (OPC), including low irradiance, elevated temperature, and partial shading. The IEC 60891 procedure enabled OPC-to-STC conversion, facilitating accurate performance evaluation and degradation rate estimation. Concurrently, the TLBO algorithm efficiently extracted SDM parameters (IL, Io, Rs, Rsh, a), achieving a mean absolute percentage error (MAPE) below 5% and an R2 of 0.99, confirming the method’s robustness. Experimental results on an 11-year-old LX-80M module revealed moderate degradation (<13%) and demonstrated the system’s ability to capture shading-induced power losses and fill factor collapse, distinguishing between permanent aging and transient faults. A comparative analysis with the recent literature confirms that this integrated, low-cost, and high-accuracy approach bridges the gap between academic PV diagnostics and industrial monitoring needs. The proposed framework provides a foundation on which IoT-enabled predictive maintenance and digital-twin integration could subsequently be built; neither extension was implemented or validated in the present study.