access icon free Artificial intelligence-based short-circuit fault identifier for MT-HVDC systems

© The Institution of Engineering and Technology
Received 20/09/2017, Accepted 30/01/2018, Revised 14/01/2018, Published 09/02/2018

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.

Inspec keywords: HVDC power convertors; power system stability; poles and towers; electric sensing devices; HVDC power transmission; discrete wavelet transforms; fuzzy neural nets; pattern recognition; power transmission faults; electric current measurement; power cables; fault location; power system identification; power engineering computing

Other keywords: artificial intelligence; inherent travelling wave; discrete wavelet examiner; stability; fuzzy-neural pattern recogniser; single current sensor; renewable energy source; MT-HVDC administration centre; fault locating technique; short-circuit DC fault identifier; software toolbox; RES; short-circuit fault identifier; multiterminal high-voltage DC system; cable; voltage-source converter

Subjects: Current measurement; Integral transforms; AC-DC power convertors (rectifiers); Sensing devices and transducers; Power line supports, insulators and connectors; Power cables; Neural computing techniques; d.c. transmission; Integral transforms; DC-AC power convertors (invertors); Power system control; Power engineering computing

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