An Intelligent Anti-Islanding Scheme for Synchronous-Based Distributed Generation Using Reduced-Noise Morphological Gradient

This paper presents a pattern recognition-based scheme for detection of islanding conditions in synchronous- based distributed generation (DG) systems. The main idea behind the proposed scheme is the use of spatial features of system parameters such as the frequency, magnitude of positive sequence v...

Full description

Bibliographic Details
Main Authors: S. Shadpey, M. Sarlak
Format: Article
Language:English
Published: Iran University of Science and Technology 2020-12-01
Series:Iranian Journal of Electrical and Electronic Engineering
Subjects:
Online Access:http://ijeee.iust.ac.ir/article-1-1378-en.html
id doaj-839589a08cdc44c6baad16470c8a99eb
record_format Article
spelling doaj-839589a08cdc44c6baad16470c8a99eb2020-11-25T03:37:12ZengIran University of Science and TechnologyIranian Journal of Electrical and Electronic Engineering1735-28272383-38902020-12-01164524535An Intelligent Anti-Islanding Scheme for Synchronous-Based Distributed Generation Using Reduced-Noise Morphological GradientS. Shadpey0M. Sarlak1 Faculty of Electrical and Computer Engineering, Jundi-Shapur University of Technology, Dezful, Iran. Faculty of Electrical and Computer Engineering, Jundi-Shapur University of Technology, Dezful, Iran. This paper presents a pattern recognition-based scheme for detection of islanding conditions in synchronous- based distributed generation (DG) systems. The main idea behind the proposed scheme is the use of spatial features of system parameters such as the frequency, magnitude of positive sequence voltage, etc. In this study, the system parameters sampled at the point of common coupling (PCC) were analyzed using reduced-noise morphological gradient (RNMG) tool, first. Then, the spatial features of the RNMG magnitudes were calculated. Next, to optimize and increase the ability of the proposed scheme for islanding detection, the best features with a much discriminating power were selected based on separability index (SI) calculation. Finally, to distinguish the islanding conditions from the other normal operation conditions, a support vector machine (SVM) classifier was trained based on the selected features. To investigate the power of the proposed scheme for islanding detection, the results of examinations on the various islanding conditions including system loading and grid operating state were presented.  These results show that the proposed algorithm reliably detect the islanding condition within 32.7 ms.http://ijeee.iust.ac.ir/article-1-1378-en.htmlreduced-noise morphological gradientsynchronous-based distributed generationislanding detectionsupport vector machineseparability index.
collection DOAJ
language English
format Article
sources DOAJ
author S. Shadpey
M. Sarlak
spellingShingle S. Shadpey
M. Sarlak
An Intelligent Anti-Islanding Scheme for Synchronous-Based Distributed Generation Using Reduced-Noise Morphological Gradient
Iranian Journal of Electrical and Electronic Engineering
reduced-noise morphological gradient
synchronous-based distributed generation
islanding detection
support vector machine
separability index.
author_facet S. Shadpey
M. Sarlak
author_sort S. Shadpey
title An Intelligent Anti-Islanding Scheme for Synchronous-Based Distributed Generation Using Reduced-Noise Morphological Gradient
title_short An Intelligent Anti-Islanding Scheme for Synchronous-Based Distributed Generation Using Reduced-Noise Morphological Gradient
title_full An Intelligent Anti-Islanding Scheme for Synchronous-Based Distributed Generation Using Reduced-Noise Morphological Gradient
title_fullStr An Intelligent Anti-Islanding Scheme for Synchronous-Based Distributed Generation Using Reduced-Noise Morphological Gradient
title_full_unstemmed An Intelligent Anti-Islanding Scheme for Synchronous-Based Distributed Generation Using Reduced-Noise Morphological Gradient
title_sort intelligent anti-islanding scheme for synchronous-based distributed generation using reduced-noise morphological gradient
publisher Iran University of Science and Technology
series Iranian Journal of Electrical and Electronic Engineering
issn 1735-2827
2383-3890
publishDate 2020-12-01
description This paper presents a pattern recognition-based scheme for detection of islanding conditions in synchronous- based distributed generation (DG) systems. The main idea behind the proposed scheme is the use of spatial features of system parameters such as the frequency, magnitude of positive sequence voltage, etc. In this study, the system parameters sampled at the point of common coupling (PCC) were analyzed using reduced-noise morphological gradient (RNMG) tool, first. Then, the spatial features of the RNMG magnitudes were calculated. Next, to optimize and increase the ability of the proposed scheme for islanding detection, the best features with a much discriminating power were selected based on separability index (SI) calculation. Finally, to distinguish the islanding conditions from the other normal operation conditions, a support vector machine (SVM) classifier was trained based on the selected features. To investigate the power of the proposed scheme for islanding detection, the results of examinations on the various islanding conditions including system loading and grid operating state were presented.  These results show that the proposed algorithm reliably detect the islanding condition within 32.7 ms.
topic reduced-noise morphological gradient
synchronous-based distributed generation
islanding detection
support vector machine
separability index.
url http://ijeee.iust.ac.ir/article-1-1378-en.html
work_keys_str_mv AT sshadpey anintelligentantiislandingschemeforsynchronousbaseddistributedgenerationusingreducednoisemorphologicalgradient
AT msarlak anintelligentantiislandingschemeforsynchronousbaseddistributedgenerationusingreducednoisemorphologicalgradient
AT sshadpey intelligentantiislandingschemeforsynchronousbaseddistributedgenerationusingreducednoisemorphologicalgradient
AT msarlak intelligentantiislandingschemeforsynchronousbaseddistributedgenerationusingreducednoisemorphologicalgradient
_version_ 1724546563409182720