Feature Selection and Extraction Methods for Power Systems Transient Stability Assessment Employing Computational Intelligence Techniques |
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Authors: | Noor Izzri Abdul Wahab Azah Mohamed Aini Hussain |
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Affiliation: | (1) Department of Electrical Engineering, IIT Madras, Chennai, 600036, India |
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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. |
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