Physics-Guided and Data-Driven Fusion Framework for Tree Species Identification in Tree-Caused High-Impedance Faults

Tree-induced single-phase high-impedance faults (THIFs) threaten the reliability of distribution networks because their fault currents typically fall below the operating thresholds of conventional overcurrent relays. Accurately identifying tree species based on fault records is crucial for implementing differentiated vegetation management; however, the limited size of sample sets obtained from field experiments leads to severe overfitting in deep learning models. This paper proposes a framework that integrates physics-guided and data-driven approaches. First, a branch utilizing manually designed physical features extracts time-domain and frequency-domain metrics, while a MiniRocket branch captures multi-resolution temporal patterns. A bootstrap-based stability selection procedure is employed to retain discriminative features, and a rigorous “leave-one-file-out” cross-validation (LOFO-CV) scheme is used to train a strongly regularized Ridge classifier. Additionally, ablation studies are conducted to compare the feature information content at sampling rates of 100 kHz and 10 kHz under the experimental conditions. Finally, a carbonization degradation index (CDI) threshold is proposed. Experiments involving 37 fault records across five tree species demonstrate that the Hybrid + Ridge method achieves a file-level F1 score of 97.3%, achieving a file-level F1 of 97.3%, higher than standalone handcrafted features and MiniRocket.