A Novel Relaying Scheme Using Long Short Term Memory for Bipolar High Voltage Direct Current Transmission Lines

Abstract

In this paper, a novel protection scheme is proposed for bipolar line commutated converter (LCC) high voltage direct current (HVDC) transmission lines which detects the fault, identifies the pole of fault and estimates the location of fault. The scheme uses features extracted from rectifier end DC current and voltage signals. Long short term memory (LSTM) deep learning method has been designed as classifier as well as predictor for carrying out different protection tasks. Three modules have been designed namely LSTM-FD for fault detection, LSTM-FI for fault pole identification and LSTM-FL for fault location estimation. The extracted features are given as inputs to the specific protection module to carry out the tasks. All the protection modules have been tested varying fault type, location, resistance, smoothing reactors, noisy signals, etc. Sensitivity and reliability of the proposed protection scheme is 100% with all the tested fault cases. Error in location estimation is within 1% for all the tested fault cases. Advantage of the method is that it does not require communication link. Another advantage of the proposed method is that it can work with low sampling frequency. Yet another advantage of proposed method is that its reliability and sensitivity is very high, hence can be used as an alternative to travelling wave based protection schemes.