Solar panel fault diagnosis based on the intelligent recursive method

Abstract

The solar panel or solar cell is one of the most important components of the solar system that produces electrical energy with high efficiency compatible with electrical loads, but any defect in this cell can cause its efficiency to decrease. The objective of this work is to establish a fault diagnosis method that can be implemented in a real structure. These faults are diagnosed and located by implementing an algorithm based on the measured values of the solar panel using an intelligent recursive least squares approach. Our objective is to contribute to the diagnosis of faults in photovoltaic systems based on fuzzy logic in a recurrent manner. The integration of recursive least squares (RLS) with fuzzy logic are essential to improve system efficiency and reliability. This approach enables rapid identification and resolution of faults, helping to avoid energy losses, reduce downtime and support proactive maintenance. It guarantees the optimal functioning of solar panels, maximizing energy production and improving return on investment. Quantitatively, this method achieves high diagnostic accuracy (over 90%), reduces error rates by up to 30% under dynamic conditions, and provides real-time fault detection with minimal latency. The combination of RLS and fuzzy logic improves fault diagnosis by effectively handling uncertainties and handling ambiguous situations better than traditional methods.