Neuro-fuzzy classifier for fault detection and classification in photovoltaic module

摘要

The reliability increase of a photovoltaic (PV) module, thus PV systems, passes through an efficient diagnosis process. This can be achieved by the use of two main techniques such as fault detection and fault classification. Identifying faults by using classification algorithms gives us a better knowledge about a fault nature. However, faults may have the same numeric signature by using conventional methods that consider only a presence or absence of symptoms or residues. This paper proposes a fault detection and classification algorithm using a neuro-fuzzy classifier. The faults being in concerns are relating to series losses, defect by-pass diode and blocking diode faults. The fault diagnosis based parameters are maximum power and, opencircuit voltage deviations, that are used as defect PV system symptoms. The method has been found proficient for fault detection and shows good discrimination for the most frequently occurring PV module faults.