HIL Investigations on Intelligently Tuned PV Integrated DSTATCOM to Enhance Power Quality

Abstract

Power quality management is a widespread requirement in this renewal energy source (RES) integrated environment. A few of the key challenges faced during power quality management are total harmonic distortion (THD), poor power factor and reactive power compensation. This paper tries to improve these power quality issues using synchronous reference frame (SRF) theory-based distributed static compensator (DSTATCOM). An Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm has been used to optimize the operation of the DSTATCOM and to obtain maximum power from a PV array. This algorithm optimizes the requirement of compensating current from the PV fed DSTATCOM to make the source current smoother. System dynamic performance is investigated in the presence of balanced/unbalanced nonlinear and reactive loads. The results obtained using ANFIS- and SRF-based methods are compared to other conventional/AI-based methods and found to be superior. The proposed method is validated in real time using Opal-RT on FPGA computation engine to closely emulate actual hardware.