IEEE Transactions on Power Systems · 2004 · 356 citations · 17 references
ComputingComputer ScienceElectrical EngineeringFeature EngineeringMachine LearningStatistical LearningClassificationFeature SelectionPattern RecognitionPower System StabilityPower SystemsSupport Vector MachinesClassification AccuracyElectrical Power SystemsNonlinear Support Vector MachinesPattern Recognition ModelsSupport Vector ClassificationSupport Vector Machine ClassificationSvmsTransient Stability
The pattern recognition approach to transient stability analysis (TSA) has been presented as a promising tool for online application. This paper applies a recently introduced learning-based nonlinear classifier, the support vector machine (SVM), showing its suitability for TSA. It can be seen as a different approach to cope with the problem of high dimensionality. The high dimensionality of power systems has led to the development and implementation of feature selection techniques to make the application feasible in practice. SVMs' theoretical motivation is conceptually explained and they are tested with a 2684-bus Brazilian system. Aspects of model adequacy, training time, classification accuracy, and dimensionality reduction are discussed and compared to stability classifications provided by multilayer perceptrons.
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Yuhai Wu, Vladimir Vapnik · Technometrics · 1999 · 27K citations
Decision trees for real-time transient stability prediction
S.M. Rovnyak, S. Kretsinger, James S. Thorp et al. · IEEE Transactions on Power Systems · 1994 · 231 citations