Application of signal processing techniques for islanding detection of distributed generation in distribution network: A review

Safdar Raza, Hazlie Mokhlis, Hamzah Arof, J. A. Laghari, Li Wang

Research output: Contribution to journalReview article

64 Citations (Scopus)

Abstract

High penetration of distributed generation resources (DGR) in distribution network provides many benefits in terms of high power quality, efficiency, and low carbon emissions in power system. However, efficient islanding detection and immediate disconnection of DGR is critical in order to avoid equipment damage, grid protection interference, and personnel safety hazards. Islanding detection techniques are mainly classified into remote, passive, active, and hybrid techniques. From these, passive techniques are more advantageous due to lower power quality degradation, lower cost, and widespread usage by power utilities. However, the main limitations of these techniques are that they possess a large non detection zones and require threshold setting. Various signal processing techniques and intelligent classifiers have been used to overcome the limitations of passive islanding. Signal processing techniques, in particular, are adopted due to their versatility, stability, cost effectiveness, and ease of modification. This paper presents a comprehensive overview of signal processing techniques used to improve common passive islanding detection techniques. A performance comparison between the signal processing based islanding detection techniques with existing techniques are also provided. Finally, this paper outlines the relative advantages and limitations of the signal processing techniques in order to provide basic guidelines for researchers and field engineers in determining the best method for their system.

Original languageEnglish
Pages (from-to)613-624
Number of pages12
JournalEnergy Conversion and Management
Volume96
DOIs
Publication statusPublished - 2015 May 15

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Nuclear Energy and Engineering
  • Fuel Technology
  • Energy Engineering and Power Technology

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