Power Quality Disturbances Diagnosis in Microgrid Integrated Electric Vehicle Charging Stations

Electric Vehicle (EV) charging stations are becoming increasingly popular, and as a result, there is a growing concern about the impact they have on the power quality of the electric grid. This paper proposes a novel approach for detecting and classifying power quality disturbances caused by EV charging stations. The proposed approach is based on analyzing the voltage and current waveforms at the charging station, and then applying a voting classifier machine learning algorithm to classify the disturbance. The approach is evaluated on a dataset of power quality disturbances collected from simulations of EV charging stations. The results show that the proposed approach can accurately detect and classify a range of power quality disturbances, including voltage sags, swells, fluctuations and harmonics. The classification accuracy achieved by the proposed approach is higher than that of existing approaches, demonstrating its effectiveness in identifying power quality disturbances caused by EV charging stations. Overall, the proposed approach provides a promising solution for addressing the power quality issues arising from EV charging stations, and could be a valuable tool for utility companies and EV charging station operators in monitoring and improving the power quality of the electric grid.