论文信息 - Strategies for fault classification in transmission lines, using learning vector quantization neural networks

Strategies for fault classification in transmission lines, using learning vector quantization neural networks

This paper analyses different approaches to fault classification, in two-terminal overhead transmission lines, using learning vector quantization (LVQ) neural networks, just verifying its efficiency. The objective is to classify the fault using the fundamental components of 50/60 Hz of fault and pre-fault voltage and current magnitudes. These magnitudes are measured in each phase at the reference end. The accuracy of these methods has been checked using properly validated fault simulation software developed with MATLAB. This software allows simulating faults in any location of the line, to obtain the fault and prefault voltage and current values. With these values, the fault can be classified. Copyright © 2006 John Wiley & Sons, Ltd.

Inmaculada Zamora | K. J. Sagastabeitia | A. J. Mazon | V. Valverde

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