Enhanced Power Quality with PV-Based DSTATCOM Using Cascaded Boost Converter and Neural Network Control Strategy

Climate conditions that cause Power Quality (PQ) issues greatly affect the electricity produced by Renewable Energy Sources (RES), such as Photovoltaic (PV) systems, which need quick adjustment of energy transmission and distribution systems. To mitigate reactive power compensation and lower voltages well, sag, and Total Harmonic Distortion (THD), the Distribution Static Synchronous Compensator (DSTATCOM) is exploited. The function of DSTATCOM is effectively managed by a Decoupled Neural Network (DNN) controller. The DSTATCOM receives the power from the PV system, and its voltage is enhanced by an improved cascaded boost converter. Furthermore, the Horse Held Optimization (HHO) based Fuzzy Maximum Power Point Tracking (MPPT) is employed to track peak power from the PV system. MATLAB/Simulink platform is employed to assess the validation of the developed DSTATCOM with the control method under the rated capacity of distribution generation. Finally, the findings in this research demonstrate the enhanced performance of PV-DSTATCOM while ensuring grid integration and improved PQ with Total Harmonic Distortion (THD) values of 1.10%, 0.71%, and 1.06%.