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KNOWLEDGE-BASED NEURAL NETWORK FOR LINE FLOW CONTINGENCY SELECTION AND RANKING

Malik, Mr. Nitin and Srivastava, Mrs. Laxmi (2005) KNOWLEDGE-BASED NEURAL NETWORK FOR LINE FLOW CONTINGENCY SELECTION AND RANKING. [Conference Paper] (In Press)

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Abstract

The Line flow Contingency Selection and Ranking (CS & R) is performed to rank the critical contingencies in order of their severity. An Artificial Neural Network based method for MW security assessment corresponding to line outage events have been reported by various authors in the literature. One way to provide an understanding of the behaviour of Neural Networks is to extract rules that can be provided to the user. The domain knowledge (fuzzy rules extracted from Multi-layer Perceptron model trained by Back Propagation algorithm) is integrated into a Neural Network for fast and accurate CS & R in an IEEE 14-bus system, for unknown load patterns and are found to be suitable for on-line applications at Energy Management Centers. The system user is provided with the capability to determine the set of conditions under which a line-outage is critical, and if critical, then how severe it is, thereby providing some degree of transparency of the ANN solution.

Item Type:Conference Paper
Keywords:MW security assessment, Energy Management Centers, Fuzzy rules, Domain Knowledge, IEEE 14-bus system
Subjects:Neuroscience > Neural Modelling
Computer Science > Neural Nets
ID Code:4361
Deposited By: Malik, Dr. Nitin
Deposited On:20 May 2005
Last Modified:11 Mar 2011 08:56

References in Article

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