Power quality diagnosis in distribution networks using support vector regression based S-transform technique

MohamedFuad Faisal, Azah Mohamed, Hussain Shareef

Research output: Contribution to journalArticle

Abstract

This paper presents a novel method for performing automatic power quality diagnosis to identify the causes of short duration voltage disturbances such as voltage sags and swells. Such voltage disturbances can be caused by permanent or non permanent faults. A permanent fault causes permanent damage and power interruption to the customers whereas a non permanent fault can be categorized as either transient or incipient faults. In the proposed power quality diagnosis method, a time frequency analysis technique called as the S-transform is used to analyse and extract features of voltage disturbances recorded from the power quality monitoring system. The support vector regression which is an intelligent technique is then used identify whether the voltage disturbances are caused by permanent, non permanent, transient or incipient faults. Test results proved that the proposed power quality diagnosis method can provide accurate diagnosis on the causes of short duration voltage disturbances.

Original languageEnglish
Pages (from-to)97-104
Number of pages8
JournalEngineering Intelligent Systems
Volume18
Issue number2
Publication statusPublished - Jun 2010

Fingerprint

Power quality
Electric power distribution
Mathematical transformations
Electric potential
Monitoring

Keywords

  • Power quality diagnosis
  • S-transform
  • Support vector regression

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications

Cite this

Power quality diagnosis in distribution networks using support vector regression based S-transform technique. / Faisal, MohamedFuad; Mohamed, Azah; Shareef, Hussain.

In: Engineering Intelligent Systems, Vol. 18, No. 2, 06.2010, p. 97-104.

Research output: Contribution to journalArticle

@article{4c89092caebf4301b8b1078f3be7cff7,
title = "Power quality diagnosis in distribution networks using support vector regression based S-transform technique",
abstract = "This paper presents a novel method for performing automatic power quality diagnosis to identify the causes of short duration voltage disturbances such as voltage sags and swells. Such voltage disturbances can be caused by permanent or non permanent faults. A permanent fault causes permanent damage and power interruption to the customers whereas a non permanent fault can be categorized as either transient or incipient faults. In the proposed power quality diagnosis method, a time frequency analysis technique called as the S-transform is used to analyse and extract features of voltage disturbances recorded from the power quality monitoring system. The support vector regression which is an intelligent technique is then used identify whether the voltage disturbances are caused by permanent, non permanent, transient or incipient faults. Test results proved that the proposed power quality diagnosis method can provide accurate diagnosis on the causes of short duration voltage disturbances.",
keywords = "Power quality diagnosis, S-transform, Support vector regression",
author = "MohamedFuad Faisal and Azah Mohamed and Hussain Shareef",
year = "2010",
month = "6",
language = "English",
volume = "18",
pages = "97--104",
journal = "Engineering Intelligent Systems",
issn = "1472-8915",
publisher = "CRL Publishing",
number = "2",

}

TY - JOUR

T1 - Power quality diagnosis in distribution networks using support vector regression based S-transform technique

AU - Faisal, MohamedFuad

AU - Mohamed, Azah

AU - Shareef, Hussain

PY - 2010/6

Y1 - 2010/6

N2 - This paper presents a novel method for performing automatic power quality diagnosis to identify the causes of short duration voltage disturbances such as voltage sags and swells. Such voltage disturbances can be caused by permanent or non permanent faults. A permanent fault causes permanent damage and power interruption to the customers whereas a non permanent fault can be categorized as either transient or incipient faults. In the proposed power quality diagnosis method, a time frequency analysis technique called as the S-transform is used to analyse and extract features of voltage disturbances recorded from the power quality monitoring system. The support vector regression which is an intelligent technique is then used identify whether the voltage disturbances are caused by permanent, non permanent, transient or incipient faults. Test results proved that the proposed power quality diagnosis method can provide accurate diagnosis on the causes of short duration voltage disturbances.

AB - This paper presents a novel method for performing automatic power quality diagnosis to identify the causes of short duration voltage disturbances such as voltage sags and swells. Such voltage disturbances can be caused by permanent or non permanent faults. A permanent fault causes permanent damage and power interruption to the customers whereas a non permanent fault can be categorized as either transient or incipient faults. In the proposed power quality diagnosis method, a time frequency analysis technique called as the S-transform is used to analyse and extract features of voltage disturbances recorded from the power quality monitoring system. The support vector regression which is an intelligent technique is then used identify whether the voltage disturbances are caused by permanent, non permanent, transient or incipient faults. Test results proved that the proposed power quality diagnosis method can provide accurate diagnosis on the causes of short duration voltage disturbances.

KW - Power quality diagnosis

KW - S-transform

KW - Support vector regression

UR - http://www.scopus.com/inward/record.url?scp=79952909385&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79952909385&partnerID=8YFLogxK

M3 - Article

VL - 18

SP - 97

EP - 104

JO - Engineering Intelligent Systems

JF - Engineering Intelligent Systems

SN - 1472-8915

IS - 2

ER -