A backpropagation feedforward NN for fault detection and classifying of overhead bipolar HVDC TL using DC measurements

Abstract EN

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.