Abstract: Aiming at the problem of poor diagnostic accuracy caused by the complex characteristics of single-phase grounding faults in distribution networks, an early diagnosis method for single-phase grounding faults in distribution networks based on improved SVM is proposed. After integrating multidimensional fault features such as zero sequence current fundamental wave, fifth harmonic, active power, and wavelet packet energy, principal component analysis is used for feature fusion and dimensionality reduction to construct a comprehensive feature library that can accurately characterize the early state of the fault. Combining intelligent parameter optimization and structural improvement optimization SVM, and adopting a "one-to-one" strategy to construct multiple classifiers, to achieve identification of faulty lines. Experimental results have shown that the diagnostic results of this method are completely consistent with the real situation, providing an effective solution for the rapid handling of single-phase grounding faults in distribution networks.