Introduction. This research work focuses on the design and experimental validation of fault detection techniques in grid-connected solar photovoltaic system operating under Maximum Power Point Tracking mode and subjected to various operating conditions. Purpose. Six fault scenarios are considered in this study including partial shading, open circuit in the photovoltaic array, complete failure of one of the six IGBTs of the inverter and some parametric faults that may appear in controller of the boost converter. Methods. The fault detection technique developed in this work is based on artificial neural networks and uses discrete wavelet transform to extract the features for the identification of the underlying faults. By applying discrete wavelet transform, the time domain inverter output current is decomposed into different frequency bands, and then the root mean square values at each frequency band are used to train the neural network. Results. The proposed fault diagnosis method has been extensively tested on the above faults scenarios and proved to be very effective and extremely accurate under large variations in the irradiance and temperature. Practical significance. The results obtained in the binary numerical system allow it to be used as a machine code and the simulation results has been validated by MATLAB / Simulink software.

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.

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