From data to decision: a holistic data-driven method for HVDC commutation failure pre-judgement

Commutation failure poses a significant operational risk on HVDC transmission systems, with its prediction facing challenges from complex underlying mechanisms and stringent real-time requirements. Data-driven techniques show promise, but practical implementation of fully data-driven solutions remains unresolved. This paper introduces a novel fully data-driven fast prediction framework for commutation failures, featuring three principal innovations: (i) A second-order determinant-based feature extraction algorithm that compresses data dimensionality while preserving critical disturbance characteristics; (ii) A cost-sensitive learning technique integrated with sample augmentation strategies to address sample bias problem; (iii) A classification performance evaluation protocol tailored for engineering applications. Experimental validation on the modified CIGRE benchmark system demonstrates 1.25 and 1% false alarm rate and miss rate respectively, with response time reduced to within 2 ms (at 2.5 kHz sampling rate). The proposed methodology significantly reduces dependency on extensive training datasets, offering a viable purely data-driven solution for real-time commutation failure pre-judgement.