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

Assad Abu-Jasser,, Mahmoud Ashour


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.


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

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