Fault-Detection-Based Machine Learning Approach to Multicellular Converters Used in Photovoltaic Systems

Today, solar energy systems based on photovoltaic (PV) panels associated with power converters are increasingly used to supply isolated sites. This structure has attracted several studies as a cost-effective, freely available, efficient source of clean and low-cost energy. However, the faults in power converters can affect the stability of the control system by supplying the isolated site with unwanted current and voltage. Therefore, this paper presents a comparative study using a fault-detection-based k-nearest neighbor (KNN) approach, between sliding mode control and exact linearization control applied to an isolated PV-system-based multicellular power converter, in order to assess the robustness and the performance of the two control strategies against the flying capacitor faults. The results obtained for both control methods in different fault cases are analyzed in terms of time series and feature spaces. These results, obtained with MATLAB software, prove the superiority of sliding mode control over exact linearization control in terms of response time, precision, and oscillations of flying capacitor voltages, as well as better separation (classification) between different fault cases in feature space.