Investigation of condition monitoring system for grid connected photovoltaic (GCPV) system with power electronics converters using machine learning techniques

Power systems protection has become more vital in recent years to ensure stability, reliability, security, and power quality due to the exponential growth of grid-connected photovoltaic (GCPV) systems. As a result, several nations have set new grid codes for the grid integration of PV plant installations to overcome these concerns. Investigating Fault Ride Through (FRT) capacity is one of the primary criteria for grid codes. For 3-phase GCPV systems, fault detection and classification approaches are proposed in this study. Firstly, different faults that occurred in the GCPV system are categorized and compared, with the critical and analytical evaluation of grid codes, particularly FRT requirements such as Low Voltage Ride Through (LVRT) and High Voltage Ride Through (HVRT) for different nations. Further, a detailed classification and a comparison of the existing FRT techniques are presented for better control methods based on system complexity, detection accuracy, and other evolutionary criteria. To ensure smooth grid operation, accurate fault detection and condition monitoring of the systems are required. This paper discusses a machine learning (ML) based technique for detecting faults in 3-phase GCPV systems. Multi-peak phenomena caused by FRT capabilities and anti-islanding detection are the main problems experienced while integrating a PV system into the local grid. The fault classification technique is developed using weighted K-nearest neighbor (WKNN) and fine Gaussian support vector machine (FGSVM) based ML approaches utilizing Wavelet Transform. The proposed ML-based findings show that the fault detection algorithm-based classification accuracy has significantly improved.