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 language | English |
|---|---|
| Pages (from-to) | 81-102 |
| Number of pages | 22 |
| Journal | Neural Processing Letters |
| Volume | 35 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Feb 2012 |
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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
Feature selection and extraction methods for power systems transient stability assessment employing computational intelligence techniques. / Wahab, Noor Izzri Abdul; Mohamed, Azah; Hussain, Aini.
In: Neural Processing Letters, Vol. 35, No. 1, 02.2012, p. 81-102.Research output: Contribution to journal › Article
}
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
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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 -