Machine learning approach to power system dynamic security analysis

TitleMachine learning approach to power system dynamic security analysis
Publication TypeConference Paper
Year of Publication2004
AuthorsNiimura, T., H. S. Ko, H. Xu, A. Moshref, and K. Morison
Conference NamePower Systems Conference and Exposition, 2004. IEEE PES
Pagination1084 - 1088 vol.2
Date Publishedoct.
Keywordsdata clustering, machine learning approach, neural nets, neural networks, pattern clustering, pattern recognition approach, pattern-learning, power system analysis computing, power system dynamic security analysis, power system security, power system transient stability, precontingency power system state, transient stability

In this paper, the authors present a pattern-learning/recognition approach for dynamic security classification using neural networks with a limited number of input data. The input is a set of data representing the precontingency power system state (voltages, angles, etc.), and the output is the possible system status (stable/unstable) after contingency. Data clustering is applied to reduce the number of input representing the cases. The reduced input data are then used to train the neural network that learns the input patterns for a possible post-contingency status. The overall accuracy of the classification is considered to be reasonable for a practical-scale power system application.


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