Artificial intelligence‐based short‐circuit fault identifier for MT‐HVDC systems. Issue 10 (13th April 2018)

• Record Type: Journal Article
• Authors: Hossam‐Eldin, Ahmed; Lotfy, Ahmed; Elgamal, Mohammed; Ebeed, Mohammed
• Is Part Of: IET generation, transmission & distribution. Volume 12:Issue 10(2018)
• Page Start: 2436
• Page End: 2443
• Publication Date: 2018-04-13
• Subjects: power system identification -- HVDC power transmission -- HVDC power convertors -- power system stability -- fault location -- poles and towers -- power cables -- electric current measurement -- electric sensing devices -- discrete wavelet transforms -- pattern recognition -- fuzzy neural nets -- power engineering computing -- power transmission faults; artificial intelligence -- short‐circuit fault identifier -- renewable energy source -- RES -- voltage‐source converter -- multiterminal high‐voltage DC system -- stability -- fault locating technique -- inherent travelling wave -- short‐circuit DC fault identifier -- single current sensor -- discrete wavelet examiner -- fuzzy‐neural pattern recogniser -- software toolbox -- MT‐HVDC administration centre -- cable; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2017.1345
• Languages: English
• ISSNs: 1751-8687

Abstract

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.