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LVRT Capability Improvement in a Grid-Connected DFIG Wind Turbine System Using Neural Network-Based Dynamic Voltage Restorer

  • Arun Kumar Puliyadi Kubendran
  • L. Ashok Kumar
Conference paper
  • 34 Downloads

Abstract

The wind power plant is one of the fastest growing electrical power sources. The integration of wind turbine into the power grid originates various power quality problems. Low-voltage ride through (LVRT) capability improvement is impregnating exigency in recent power quality issue, which is acquainted with various renewable power generation resources like solar PV and wind power plant. Dynamic voltage restorer (DVR) is a custom power device (CPD), which is connected in series with the electrical system, and it is a contemporary and efficient CPD expended in the distribution system to curb the power quality problems. In this paper, LVRT capability of doubly fed induction generator (DFIG) wind turbine system connected to a power grid is enhanced by means of DVR. LVRT capability is improved by the introduction of neural network controller. This controller fulfils the various grid code requirements. Thus, the performance of the DVR becomes far superior by the robust control technique. The simulation results were compared with conventional PI controller and the proposed artificial neural network (ANN) controller. It is proved that the proposed system raises the reliability of grid-connected DFIG wind turbine system.

Keywords

Wind farm Low-voltage ride through DVR Custom power device DFIG Neural network Power quality 

Abbreviation

ANN

artificial neural network

CPD

custom power device

DFIG

doubly fed induction generator

DVR

dynamic voltage restorer

EMF

electromotive force

FNN

feed-forward neural network

GSC

grid side converter

LVRT

low-voltage ride through

PQ

power quality

RSC

rotor side converter

STATCOM

static synchronous compensator

SVC

static VAR compensator

WECS

wind energy conversion system

WT

wind turbine

WTG

wind turbine generator

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Arun Kumar Puliyadi Kubendran
    • 1
  • L. Ashok Kumar
    • 2
  1. 1.Saranathan College of EngineeringTiruchirappalliIndia
  2. 2.Department of Electrical and Electronics EngineeringPSG College of TechnologyCoimbatoreIndia

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