Artificial Neural Network Based Dynamic Voltage Restorer for Improvement of Power Quality

Md Samiul Haque Sunny, Eklas Hossain, Mikal Ahmed, Fuad Un-Noor

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Scopus citations

Abstract

Dynamic Voltage Restorer (DVR) is a custom power device used as an effective solution in protecting sensitive loads from voltage disturbances in power distribution systems. The efficiency of the control technique, that conducts the switching of the inverters, determines the DVR efficiency. Proportional-Integral-Derivative (PID) control is the general technique to do that. The power quality restoration capabilities of this controller are limited, and it produces significant amount of harmonics - all of which stems from this linear technique's application for controlling non-linear DVR. As a solution, this paper proposes an Artificial Neural Network (ANN) based controller for enhancing restoration and harmonics suppression capabilities of DVR. A detailed comparison of Neural Network controller with PID driven controller and Fuzzy logic driven controller is also illustrated, where the proposed controller demonstrated superior performance with a mere 13.5% Total Harmonic Distortion.

Original languageEnglish
Title of host publication2018 IEEE Energy Conversion Congress and Exposition, ECCE 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5565-5572
Number of pages8
ISBN (Electronic)9781479973118
DOIs
StatePublished - 3 Dec 2018
Event10th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2018 - Portland, United States
Duration: 23 Sep 201827 Sep 2018

Publication series

Name2018 IEEE Energy Conversion Congress and Exposition, ECCE 2018

Conference

Conference10th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2018
Country/TerritoryUnited States
CityPortland
Period23/09/1827/09/18

Keywords

  • Artificial Neural Network (ANN)
  • Dynamic Voltage Restorer (DVR)
  • Fuzzy logic
  • PID
  • Power quality

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