A new converter fault discrimination method for a 12-pulse high-voltage direct current system based on wavelet transform and Hidden Markov Models

Abstract

The progressive development in high-voltage direct current (HVDC) transmission enhances the need to implement an efficient protection scheme to distinguish the minimum faulty part of the system and to relieve the stressed equipment. This paper proposes a new classification method based on Hidden Markov Models and wavelet transform to discriminate HVDC converter faults. In the proposed technique, probabilistic characteristics of signals discriminate fault signals without any deterministic index, so more flexible classification in different system conditions is achieved. Based on this method, high-speed protection decisions with small computational time could be performed in approximately 5 ms for severe system faults, and in 12.5 ms for faults that are restricted by protective control decision. PSCAD/EMTDC software simulations demonstrate suitable performance of this scheme for different fault types and system conditions in the International Council for Large Electric Systems (CIGRE) HVDC benchmark. All simulation results validate the stability and robustness of proposed scheme in different conditions, such as noisy systems.

Keywords

converter faults Discrete Hidden Markov Models fault discrimination high-voltage direct current wavelet transform

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