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ANN-based automatic contingency selection for electric power system

Lo, K.L. and Luan, W.P. and Given, M.J. and Bradley, M. and Wan, H. (2002) ANN-based automatic contingency selection for electric power system. COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 21 (2). pp. 193-207. ISSN 0332-1649

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Abstract

Automatic contingency selection aims to quickly predict the impact of a set of next contingencies on an electric power system without actually performing a full ac load flow. Artificial neural network methods have been employed to overcome the masking effects or slow execution associated with existing methods. However, the large number of input features for the ANN limits its applications to large power systems. In this paper, a novel feature selection method, named the Weak Nodes method, based on a heuristic approach is proposed for an ANN-based automatic contingency selection for electric power system, especially for the voltage ranking problem. Pre-contingency state variables of weak nodes in the power system are adopted as input features for the ANN. The method is tested on the 77 busbar NGC derived network by Counter-propagation Method and it is proved that it reduces the input features for ANN dramatically without losing ranking accuracy.

Item type: Article
ID code: 3496
Keywords: artificial neural networks, power devices, voltage, electrical engineering, computer science, Electrical engineering. Electronics Nuclear engineering, Electronic computers. Computer science, Computational Theory and Mathematics, Applied Mathematics, Computer Science Applications, Electrical and Electronic Engineering
Subjects: Technology > Electrical engineering. Electronics Nuclear engineering
Science > Mathematics > Electronic computers. Computer science
Department: Faculty of Engineering > Electronic and Electrical Engineering
Related URLs:
    Depositing user: Strathprints Administrator
    Date Deposited: 20 Jun 2007
    Last modified: 04 Sep 2014 11:05
    URI: http://strathprints.strath.ac.uk/id/eprint/3496

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