A Backpropagation Feedforward NN for Fault Detection and Classifying of Overhead Bipolar HVDC TL Using DC Measurements

Assad Abu-Jasser,, Mahmoud Ashour

Abstract


This paper suggests the use of back-propagation feed-forward artificial neural networks (NN) for fault detection and classification in the high voltage direct current (HVDC) transmission line (TL). To achieve these tasks, post-fault measurements of the dc voltages and currents at the rectifier station related to the pre-fault measurements are used as inputs to the neural network. A bipolar HVDC TL model of 940 km long and ±500 kV is chosen to be studied. This paper handles most frequent kinds of overhead bipolar HVDC TL power faults, and the results obtained are completely satisfactory.

Keywords


HVDC Transmission Lines - Fault Detection - Fault Classification – NN - FeedforwardBackpropagation – Bipolar – Overhead - DC Measurements - Three Gorges Changzhou (3GC).

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