Data-driven Fault Detection and Classification for MTDC Systems by Integrating HCTSA and Softmax Regression

The requirement of fast fault isolation poses a great challenge to the safe operation of multi-terminal direct current (MTDC) systems. In order to make a better tradeoff between the speed and reliability of the protection scheme, it is imperative to mine more valuable information from fault transient signals. This paper puts forward a data-driven fault detection and classification framework for MTDC systems. Highly comparative time-series analysis (HCTSA) is first adopted to extract extensive features with clear physical interpretations from the original DC fault current waveform, and the most useful features to fault identification are then selected utilizing the greedy forward search. Based on these reduced features, a simple but powerful softmax regression classifier (SRC) is further proposed to calculate the probability of each fault category, so that fault identification can be fast achieved with minor computational burden. Numerical simulations carried out in PSCAD/EMTDC have demonstrated the accuracy of proposed approach under various fault conditions and noise corruptions. In addition, comprehensive comparison studies with the conventional derivative-based method and some typical data-driven methods have been conducted, and it is well-verified that via rationally selecting and fully integrating multi-dimensional features, the proposed approach possesses higher fault classification accuracy than existing methods.