Support vector machines for transient stability analysis of large-scale power systems
2004
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.
Keywords:
- Feature selection
- Dimensionality reduction
- Perceptron
- Support vector machine
- Artificial neural network
- Nonlinear system
- Machine learning
- Curse of dimensionality
- Multilayer perceptron
- Artificial intelligence
- Pattern recognition
- Computer science
- Data mining
- transient stability analysis
- Electric power system
- high dimensionality
- training time
- Correction
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