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Classifier design for static security assessment using particle swarm optimization
Published in
2011
Volume: 11
   
Issue: 1
Pages: 658 - 666
Abstract
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. © 2010 Elsevier B.V. All rights reserved.
About the journal
JournalApplied Soft Computing Journal
ISSN15684946
Open AccessNo
Concepts (21)
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    Classification accuracy
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    CLASSIFIER DESIGN
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    Design and operations
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    ERROR RATE
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    First order
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    IEEE 118-BUS SYSTEM
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    MISCLASSIFICATION RATES
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    PARTICLE SWARM
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    POWER SYSTEM SECURITY ASSESSMENT
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    Power systems
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    Pso algorithms
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    PSO CLASSIFIER
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    SECURITY FUNCTIONS
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    Simulation result
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    STATIC SECURITY
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    STATIC SECURITY ASSESSMENT
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    Buses
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    Classifiers
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    Electric power systems
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    Feature extraction
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    Particle swarm optimization (pso)