Multisegmented Intelligent Solution for MT-HVDC Grid Protection

Abstract

Fault detection continues to be a relevant and ongoing topic in multiterminal High Voltage Direct Current (MT-HVDC) grid protection. In MT-HVDC grids, however, high DC-fault currents result from a failure of a complex protective threshold in traditional protection schemes, making Voltage Source Converter (VSC) vulnerable to such potent transient currents. In this innovative single-ended DC protection scheme, multiple time window segments are used to consider the effects of the transient period across limiting inductors at each end of the link. Multiple segments of 0–0.8, 0.8–1.5, and 1.5–3.0 ms reduce relay failure and improve the sensitivity to high fault impedance while requiring minimal computational effort. It employs feature extraction tools such as Stationary Wavelet Transform and Random Search (RS)-based Artificial Neural Networks (ANNs) for detecting transmission line faults within DC networks. Its goal is to improve the accuracy and reliability of protective relays as a result of various fault events. Simulations showed that the proposed algorithms could effectively identify any input data segment and detect DC transmission faults up to 500 ohms. Accuracy for the first segment is 100% for fault impedance up to 200 ohms, whereas the second and third segments show 100% accuracy for high impedance faults up to 400 ohms. In addition, they maintain 100% stability even under external disturbances.