Artificial intelligence‐based short‐circuit fault identifier for MT‐HVDC systems. Issue 10 (13th April 2018) - Hossam‐Eldin, Ahmed; Lotfy, Ahmed; Elgamal, Mohammed; Ebeed, Mohammed
Abstract : 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.