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In this paper, a novel relaying scheme is proposed for bipolar line commutated converter (LCC) high voltage direct current (HVDC) transmission lines that detects the fault, identifies the pole of fault and estimates the location of the fault. The scheme uses features extracted from rectifier end DC current and voltage signals. Long short term memory (LSTM), a deep learning method has been designed as classifier as well as predictor for carrying out different relaying tasks. Three modules have been designed namely LSTM-FD (LSTM module for fault detection (FD)), LSTM-FI (LSTM module for fault pole identification (FI)) and LSTM-FL (LSTM module for fault location estimation (FL)). The voltage and current signals are obtained from measuring units. Then with a moving window of one cycle, the RMS of signals is calculated. The current and voltage features are obtained in the time domain. The voltage and current features obtained are used as input to fault detection and fault pole identification module. For fault location estimation, half cycle samples of RMS current and voltage after the fault have been taken. From half cycle sample, maximum value of current and minimum value of voltage has been obtained. This single value of current and voltage are used as input feature to the fault location module. All the relaying modules have been tested varying fault types, locations, resistances, smoothing reactors, noisy signals, etc. Sensitivity and reliability of the proposed relaying 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. Additionally, reliability and sensitivity of proposed method is very high, hence can be used as an alternative to travelling wave based relaying schemes.

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