A novel primary and backup relaying scheme considering internal and external faults in HVDC transmission lines - Swetapadma, Aleena; Agarwal, Shobha; Abdelaziz, Almoataz Y.; Kotb, Hossam; AboRas, Kareem M.; Flah, Aymen; Shouran, Mokhtar (Frontiers in Energy Research, 2022)
Discrimination of different DC faults near a converter end of a DC section consisting of a filter, a smoothing reactor and a transmission line is not an easy task. The faults that occur in the AC section can be easily distinguished, but the internal and near-side external faults in the DC section are very similar, and the relay may cause a false tripping. In this proposed paper, the extraction of voltage signals at the start of the transmission line and at the end of the converter filter can be used to distinguish external and internal faults that occur in the DC section. The difference in signals is given to an intelligent controller to detect and discriminate the faults. The proposed method uses only one terminal voltage signal which avoids the requirements of any communication link. The input signal is given to Machine Learning (ML) and Deep Learning (DL) techniques for fault detection. The Long Short-Term Memory Network (LSTM)-based relay gives better results than other methods for the distinction of faults. The proposed work suggests a primary relay that detects faults quickly within a fraction of milliseconds. Also, the proposed method proposes a backup relay in case the primary relay is unable to operate. Hence, the proposed method is able to overcome all the drawbacks of travelling wave-based relay. The results show that the LSTM-based protection scheme provides higher sensitivity and reliability under different operation modes than conventional travelling wave-based relay.