Artificial intelligence‐based short‐circuit fault identifier for MT‐HVDC systems

The most convenient solution to link faraway significant renewable energy sources (RESs) is the voltage‐source converter multi‐terminal high‐voltage DC systems (MT‐HVDC). However, to maintain system stability and continuity of supply, a rigid and fast fault locating technique is required. This study proposes a novel inherent travelling waves based short‐circuit DC fault identifier, which accurately identifies both of the fault location and faulty pole in multiple numbers of cables in MT‐HVDC system using a single current sensor. Both of a discrete wavelet examiner and a fuzzy‐neural pattern recogniser precisely spot the faulty line and fault location based on the mutual effects of short‐circuit initiated travelling waves between lines belonging to the same loop. A software toolbox is structured to illustrate the adequacy of the proposed artificial intelligence technique. This method is valuable to MT‐HVDC administration centres, particularly those concerned with long‐distance RES.