Design and Performance Monitoring of Artificial Neural Network based Three-Phase Solar PV System with Integrated UPQC

The model of a three-phase solar PV system using the integrated UPQC, its performance analysis and control operations of UPQC by adopting an artificial intelligence(ANN) are discussed in this paper. The shunt and series voltage compensators are the basic components of PV-UPQC that are integrated in series and parallel with a single DC link. Shunt compensator is meant to manage power through PV array as well as compensate the load current harmonics. Moving average filter (MAF) enhanced the system performance and improving synchronous reference frame control. The series voltage compensator is meant to compensate disputes in quality of power at grid-side such as voltage falls and surges. The voltage is injected at the grid both in phase or out-of-phase appropriately. The main benefits due to this proposed system is to generate clean energy and improve power quality. The MATLAB Simulink is adopted to analyze the system's performance in both stable state and vigorous conditions. The primary focuses is to implement an ANN (Artificial Neural Network) rather than a PI controller for better performance and minimize THD issues at various conditions of irradiance, PCC voltage sag/swell, and load unbalancing. ANN will yield the best results and enhance power quality.