Abstract: To address the challenges of small-sample characteristics in transmission line fault data and the identification of single-phase-to-ground fault causes, this paper proposes a fault cause identification method based on multi-dimensional robust fisher feature selection(MDRffS). first, multi-dimensional features are extracted from fault recording data in the time domain, frequency domain, and time-frequency domain to construct a candidate feature set. By jointly considering feature discriminative ability, mutual information relevance, and robustness indicators, an MDRffS-based feature selection mechanism is developed to achieve effective feature screening and dimensionality reduction, yielding a set of high-quality feature vectors. On this basis, a multi-support vector machine(multi-SVM) identification model incorporating a priority-based decision strategy is constructed to accurately identify five typical fault causes, including lightning strikes, bird interference, wildfire, ice damage and wind-induced faults. finally, case studies based on actual power grid fault recording data demonstrate that the proposed method achieves an average identification accuracy of 92.314%, verifying its feasibility and effectiveness under small-sample conditions.

Key words: single-phase grounding fault, fault cause identification, MDRffS, support vector machine (SVM)