Neuro-Fuzzy fault detection method for photovoltaic systems

Abstract In this work we present a faults detection method for photovoltaic systems (PVS). This method is based on the calculation of sets of parameters of a PV module in different operating conditions, by means of a Neuro-Fuzzy approach. The PV system status is determined by evaluation and comparison of norms based on the aforementioned parameters, with threshold values. This intelligent system developed in Matlab&Simulink environment, consists on the study of the crucial information that the six parameters in normal and faulty condition contain. They are calculated using the I-V curves and synthesized by "hybrid" models. Results show that the diagnosis system is able to discern between normal and faulty operation conditions and with the same defective existence of noise and disturbances

Neuro-Fuzzy Fault Detection Method for Photovoltaic Systems

AbstractIn this work we present a faults detection method for photovoltaic systems (PVS). This method is based on the calculation of sets of parameters of a PV module in different operating conditions, by means of a Neuro-Fuzzy approach. The PV system status is determined by evaluation and comparison of norms based on the aforementioned parameters, with threshold values. This intelligent system developed in Matlab&Simulink environment, consists on the study of the crucial information that the six parameters in normal and faulty condition contain. They are calculated using the I-V curves and synthesized by “hybrid” models. Results show that the diagnosis system is able to discern between normal and faulty operation conditions and with the same defective existence of noise and disturbances