A Method for Generating Partial Discharge Signals Based on Time-varying Dipole Model

With the development of power systems, the safe and stable operation of distribution networks has become a focal point of attention. In this study, we propose a method for generating partial discharge signals based on the time-varying dipole model. By employing the dipole model and combining it with the Hermite interpolation method, we establish a gap-frequency correspondence, allowing the generation of fault signals corresponding to different gap sizes based on existing measurement data. This approach provides a novel method for generating partial discharge fault signals, addressing the challenges of fault signal collection and dataset creation, and opening new prospects for the application of deep learning methods in the field of distribution networks.