Feature selection and extraction methods for power systems transient stability assessment employing computational intelligence techniques

Noor Izzri Abdul Wahab, Azah Mohamed, Aini Hussain

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

One of the important aspects in achieving better performance for transient stability assessment (TSA) of power systems employing computational intelligence (CI) techniques is by incorporating feature reduction techniques. For small power system the number of features may be small but when larger systems are considered the number of features increased as the size of the systems increases. Apart from employing faster CI techniques to achieve faster and accurate TSA of power system, feature reduction techniques are needed in reducing the input features while preserving the needed information so as to make faster training of the CI technique. This paper presents feature reductions techniques used, namely correlation analysis and principle component analysis, in reducing number of input features presented to two CI techniques for TSA, namely probabilistic neural network (PNN) and least squares support vector machines (LS-SVM). The proposed feature reduction techniques are implemented and tested on the IEEE 39-bus test system and 87-bus Malaysia's power system. Numerical results are presented to demonstrate the performance of the feature reduction techniques and its effects on the accuracies and time taken for training the two CI techniques.

Original languageEnglish
Pages (from-to)81-102
Number of pages22
JournalNeural Processing Letters
Volume35
Issue number1
DOIs
Publication statusPublished - Feb 2012

Fingerprint

Artificial Intelligence
Artificial intelligence
Feature extraction
Motor Vehicles
Malaysia
Least-Squares Analysis
Support vector machines
Neural networks

Keywords

  • Computational intelligence techniques
  • Correlation analysis
  • Principle component analysis
  • Transient stability assessment

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Computer Networks and Communications
  • Neuroscience(all)

Cite this

@article{c1d7a6c7090d4f6f821a2b588d451ae0,
title = "Feature selection and extraction methods for power systems transient stability assessment employing computational intelligence techniques",
abstract = "One of the important aspects in achieving better performance for transient stability assessment (TSA) of power systems employing computational intelligence (CI) techniques is by incorporating feature reduction techniques. For small power system the number of features may be small but when larger systems are considered the number of features increased as the size of the systems increases. Apart from employing faster CI techniques to achieve faster and accurate TSA of power system, feature reduction techniques are needed in reducing the input features while preserving the needed information so as to make faster training of the CI technique. This paper presents feature reductions techniques used, namely correlation analysis and principle component analysis, in reducing number of input features presented to two CI techniques for TSA, namely probabilistic neural network (PNN) and least squares support vector machines (LS-SVM). The proposed feature reduction techniques are implemented and tested on the IEEE 39-bus test system and 87-bus Malaysia's power system. Numerical results are presented to demonstrate the performance of the feature reduction techniques and its effects on the accuracies and time taken for training the two CI techniques.",
keywords = "Computational intelligence techniques, Correlation analysis, Principle component analysis, Transient stability assessment",
author = "Wahab, {Noor Izzri Abdul} and Azah Mohamed and Aini Hussain",
year = "2012",
month = "2",
doi = "10.1007/s11063-011-9205-x",
language = "English",
volume = "35",
pages = "81--102",
journal = "Neural Processing Letters",
issn = "1370-4621",
publisher = "Springer Netherlands",
number = "1",

}

TY - JOUR

T1 - Feature selection and extraction methods for power systems transient stability assessment employing computational intelligence techniques

AU - Wahab, Noor Izzri Abdul

AU - Mohamed, Azah

AU - Hussain, Aini

PY - 2012/2

Y1 - 2012/2

N2 - One of the important aspects in achieving better performance for transient stability assessment (TSA) of power systems employing computational intelligence (CI) techniques is by incorporating feature reduction techniques. For small power system the number of features may be small but when larger systems are considered the number of features increased as the size of the systems increases. Apart from employing faster CI techniques to achieve faster and accurate TSA of power system, feature reduction techniques are needed in reducing the input features while preserving the needed information so as to make faster training of the CI technique. This paper presents feature reductions techniques used, namely correlation analysis and principle component analysis, in reducing number of input features presented to two CI techniques for TSA, namely probabilistic neural network (PNN) and least squares support vector machines (LS-SVM). The proposed feature reduction techniques are implemented and tested on the IEEE 39-bus test system and 87-bus Malaysia's power system. Numerical results are presented to demonstrate the performance of the feature reduction techniques and its effects on the accuracies and time taken for training the two CI techniques.

AB - One of the important aspects in achieving better performance for transient stability assessment (TSA) of power systems employing computational intelligence (CI) techniques is by incorporating feature reduction techniques. For small power system the number of features may be small but when larger systems are considered the number of features increased as the size of the systems increases. Apart from employing faster CI techniques to achieve faster and accurate TSA of power system, feature reduction techniques are needed in reducing the input features while preserving the needed information so as to make faster training of the CI technique. This paper presents feature reductions techniques used, namely correlation analysis and principle component analysis, in reducing number of input features presented to two CI techniques for TSA, namely probabilistic neural network (PNN) and least squares support vector machines (LS-SVM). The proposed feature reduction techniques are implemented and tested on the IEEE 39-bus test system and 87-bus Malaysia's power system. Numerical results are presented to demonstrate the performance of the feature reduction techniques and its effects on the accuracies and time taken for training the two CI techniques.

KW - Computational intelligence techniques

KW - Correlation analysis

KW - Principle component analysis

KW - Transient stability assessment

UR - http://www.scopus.com/inward/record.url?scp=84855829510&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84855829510&partnerID=8YFLogxK

U2 - 10.1007/s11063-011-9205-x

DO - 10.1007/s11063-011-9205-x

M3 - Article

VL - 35

SP - 81

EP - 102

JO - Neural Processing Letters

JF - Neural Processing Letters

SN - 1370-4621

IS - 1

ER -