Introduction: Very Fast Transient Overvoltages (VFTO) generated during disconnector operations in substations can cause severe insulation stress and threaten the reliability of equipment. However, existing arc models, such as fixed-parameter or time-varying resistance models, fail to accurately capture the transient arc dynamics influenced by temperature variation. This study aims to improve the simulation accuracy of VFTO by developing a temperature-coupled arc model based on the classical Cassie formulation, enabling real-time parameter updates driven by plasma temperature evolution. Method: Plasma simulations conducted in COMSOL Multiphysics were used to establish a power- law relationship between the arc temperature and the model parameters, including the time constant (τc) and the voltage constant (uo). These temperature-dependent parameters were then incorporated into the Cassie model and implemented in an equivalent circuit simulation environment using ATP-EMTP. Results: The proposed model was validated against capacitive small-current test data and compared with the time-varying resistance, segmented arc, and combined Mayr–Cassie models. The simulation results show that the proposed model achieves relative errors of only 2.04% in amplitude and 1.57% in rise time, providing a closer match to experimental data. Conclusion: The integration of temperature-dependent parameters significantly enhances the VFTO simulation performance of the Cassie model. This method offers a more accurate theoretical foundation for transient overvoltage analysis and insulation coordination in power systems. Discussion: The findings demonstrate that incorporating plasma-temperature evolution into arc modeling can effectively bridge the gap between simplified electrical models and the physical behavior of high-temperature plasma during disconnector operations. While the method substantially improves accuracy, its dependence on precomputed plasma simulations may limit real-time applications. Future refinements could include adaptive parameter identification or the integration of pressure and electrode-geometry effects to further enhance model generality.
Wang et al. (Mon,) studied this question.