Fault diagnosis of power converters in a grid connected photovoltaic system using artificial neural networks





grid connected photovoltaic system, artificial neural network, power converters, open circuit failure of IGBT, fault detection


Introduction. The widespread use of photovoltaic systems in various applications has spotlighted the pressing requirement for reliability, efficiency and continuity of service. The main impediment to a more effective implementation has been the reliability of the power converters. Indeed, the presence of faults in power converters that can cause malfunctions in the photovoltaic system, which can reduce its performance. Novelty. This paper presents a technique for diagnosing open circuit failures in the switches (IGBTs) of power converters (DC-DC converters and three-phase inverters) in a grid-connected photovoltaic system. Purpose. To ensure supply continuity, a fault-diagnosis process is required throughout all phases of energy production, transfer, and conversion. Methods. The diagnostic approach is based on artificial neural networks and the extraction of features corresponding to the open circuit fault of the IGBT switch. This approach is based on the Clarke transformation of the three-phase currents of the inverter output as well as the calculation of the average value of these currents to determine the exact angle of the open circuit fault. Results. This method is able to effectively identify and localize single or multiple open circuit faults of the DC-DC converter IGBT switch or the three-phase inverter IGBT switches.

Author Biographies

A. Mimouni, University of Tiaret

PhD Student, Energy Engineering and Computer Engineering (L2GEGI) Laboratory

S. Laribi, University of Tiaret

Doctor of Electrical Engineering, Energy Engineering and Computer Engineering (L2GEGI) Laboratory

M. Sebaa, University of Tiaret

Professor, Energy Engineering and Computer Engineering (L2GEGI) Laboratory

T. Allaoui, University of Tiaret

Professor of Electrical Engineering, Energy Engineering and Computer Engineering (L2GEGI) Laboratory

A. A. Bengharbi, University of Tiaret

PhD Student, Energy Engineering and Computer Engineering (L2GEGI) Laboratory


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How to Cite

Mimouni, A., Laribi, S., Sebaa, M., Allaoui, T., & Bengharbi, A. A. (2023). Fault diagnosis of power converters in a grid connected photovoltaic system using artificial neural networks. Electrical Engineering & Electromechanics, (1), 25–30. https://doi.org/10.20998/2074-272X.2023.1.04



Industrial Electronics