Neural Network Based ILST Control Strategy for a DSTATCOM with Solar Photovoltaic System for Power Quality Improvement

J. Jayachandran; S. Malathi

doi:10.15866/ireaco.v10i5.12654

Neural Network Based ILST Control Strategy for a DSTATCOM with Solar Photovoltaic System for Power Quality Improvement

J. Jayachandran^(1*), S. Malathi⁽²⁾

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/ireaco.v10i5.12654

Abstract

The power quality compensator chosen in this paper is a DSTATCOM which integrates a three phase four leg Voltage Source Converter (VSC) with a DC capacitor (Cdc). A single stage Solar Photovoltaic (SPV) system is implemented for maintaining the DC link voltage (Vdc) of the Shunt Active Power Filter (SAPF). The system proposed, improves the Power Quality (PQ) of the utility system as well as feeds the solar energy extracted into the system. Maximum Power Point Tracking (MPPT) is implemented to excerpt solar energy. The purpose of the proposed system is to mitigate harmonics and neutral current (Isn),to compensate reactive power and to maintain power factor near unity. The SAPF improves the PQ of the proposed system. The control algorithm proposed for SAPF is Neural Network (NN) based Improved Linear Sinusoidal Tracer (ILST) algorithm. An instantaneous compensation technique controls the variations in PV power of the SPV system with fast dynamic response. The proposed system is also compared with battery operated Boost Converter (BC) SPV fed SAPF system. The battery operated SPV system boost the DC link voltage and thus improves PQ throughout the day. The NN based control strategy and the SPV system are modeled, developed and validated in MATLAB SIMULINK. The simulation results justify the effectiveness of the propounded system.
Copyright © 2017 Praise Worthy Prize - All rights reserved.

Keywords

DSTATCOM; Neural Network; Neutral Current; THD; SPV; MPPT; ILST

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