Power Quality Enhancement in IOT Based Hybrid Renewable Energy Systems Using D-STATCOM

Now-a-days, society largely depends on sources of clean energy. Nevertheless, incorporating it into the power system is complicated and has technical difficulties. Voltage sag and swell are the main issues with power quality (PQ) that arise from the unpredictability of clean energy sources. In this paper, a D-STATCOM is employed to compensate PQ issues arising due to solar and wind farms. The main contribution of this work is that a photovoltaic (PV) and wind fed D-STATCOM is used to mitigate the issues of PQ, SUC voltage sag, swell, harmonics and flickers. In order to enhance functionality of wind system the squirrel cage induction generator (SCFG) is employed. To stabilize the output voltage of PV system, a Re-Lift Luo converter is introduced. Adaptive neuro-fuzzy interface system (ANFIS) based maximum power point tracking (MPPT) is intended to maximize the power from PV array and is used efficiently to maintain the system reliability. The PWM rectifier increases the current flow through PWM operation and regulates the dc link voltage at the converter side. Here, the recurrent neural network (RNN) is utilized to generate the reference current for hysteresis current controller (HCC), which minimizes the current distortions. The experimental prototype of this work has been realized employing a Node MCU Wi-Fi module and MATLAB simulation platform.