A Deep Learning Technology Based UPQC To Enhance The Power Quality Problems In Grid Integrated To Solar Photovoltaic System

The country's need for power is growing with a rise in population. Sources of renewable energy, such as the sun and wind can be employed alongside conventional sources to satisfy peak load demands. Photo-Voltaic (PV) energy generation has lower installation and production costs than wind energy generation. However, due to the extensive use of nonlinear electronics, grid-connected PV systems have increased power quality problems. The proposed system will overcome above problem and it will give the better performance results also reduce the Total Harmonic Distortion (THD) other than the traditional techniques. To create the most electricity possible from photovoltaic solar energy plants, the Deep Learning algorithm technique is suggested. The proposed system presents a novel technique for enhancing grid-integrated PV system based power quality. When compared to a traditional UPQC-based PI controller, the creation of an advanced DNN (Deep Neural Network) controller-based UPQC (Unified Power Quality Conditioner) enhances power quality and lowers THD value for the grid integration of PV system. The suggested approach is used to analyze the performance of power quality concerns in the MATLAB/Simulink platform.