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Strathprints serves world leading Open Access research by the University of Strathclyde, including research by the Strathclyde Institute of Pharmacy and Biomedical Sciences (SIPBS), where research centres such as the Industrial Biotechnology Innovation Centre (IBioIC), the Cancer Research UK Formulation Unit, SeaBioTech and the Centre for Biophotonics are based.

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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.