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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.

Fault location estimation methods have been suggested by researchers decades back after the development of transmission lines for power transmission. But there are several drawbacks exist such as incapability to locate high resistance faults, boundary faults, intercircuit faults, change in line parameters, evolving faults, etc. Hence, a method should be developed, which can address all the problems of conventional fault location estimation methods. One of the solutions can be artificial intelligence (AI) due to the ability to learn and think. In this article, an AI-based fault location estimation method has been suggested for transmission lines, which use the measurement from one end of the line. A comparative study of four AI methods has been carried out, and one suitable method has been chosen. Three methods are used for signal processing, and one suitable method has been chosen for signal processing and feature selection. The proposed AI-based method has error in location within 1 km for most fault cases, and maximum error in locating fault is 2.91 km. The performance enhancement of the proposed AI-based method over existing methods is that it does not require a communication link as it uses only one end signal; it can locate high resistance faults, boundary faults, intercircuit faults, unaffected by variation in line parameters, variation in power flow angle, etc. The proposed method can be implemented in real power network, which is feasible, easy, low cost, and robust.