A pattern recognition and associative memory approach to power system security assessment
IEEE Transactions on Systems, Man and Cybernetics
The nature of statistical learning theory
The nature of statistical learning theory
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Intelligent Systems and Signal Processing in Power Engineering
Intelligent Systems and Signal Processing in Power Engineering
Feature reduction techniques for power system security assessment
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Damage detection based on improved particle swarm optimization using vibration data
Applied Soft Computing
A hybrid network intrusion detection system using simplified swarm optimization (SSO)
Applied Soft Computing
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Security is recognized as an important problem in planning, design and operation stages of electric power systems. Power system security assessment deals with the system's ability to continue to provide service in the event of an unforeseen contingency. This paper proposes a particle swarm optimization (PSO) based classification for static security evaluation in power systems. A straightforward and quick procedure is used to select a small number of variables as features from a large set of variables which are normally available in power systems. A simple first order security function is designed using the selected features for classification. The training of weights in the classifier function (security function) is carried out by PSO technique. The PSO algorithm has minimized the error rate in classification. The procedure to determine the security function (classifier) is discussed. The performance of the algorithm is tested on IEEE 14 Bus, IEEE 57 Bus and IEEE 118 Bus systems. Simulation results show that the PSO classifier gives a fairly high classification accuracy and less misclassification rate.