This paper focuses on the latest research of switching arc both in vacuum and SF6 substitutes in our group. The crucial characteristics of vacuum arc are illustrated, including the motion and distribution of single cathode spot and multiple cathode spots, the influence of axial magnetic field on arc plasma characteristics, the influence of composite magnetic field on cathode jets, and the study of anode activities. Meanwhile, the arc characteristics in SF6 and its substitutes (Ar, CO2 and N2) at different pressures and gap distances are investigated by experiments and simulation.

The paper is devoted to the experimental study of the arc plasma characteristics in SF6, N2 and CO2. To one flexible model of gas circuit breaker, short circuit experiments have been carried out considering the influence of contact gap (4–12 mm), gas pressure (1–5 atm), short circuit current (1–5 kA effective value) as well as gas species particularly. During the experiments, the arc image, arc current and arc voltage are recorded by the high speed camera, shunt and voltage transducer, respectively. It demonstrates that to the above mentioned three kinds of gases, the arc radius and arc voltage increase with the short circuit current and gas pressure normally; however, under the same experimental conditions, N2 arc holds the minimum arc radius and the maximum arc voltage, and the arc voltage of SF6 arc is the lowest.

Anode activity in vacuum arc plays a very important role in both characteristics of vacuum arc and the interruption capacity of vacuum interrupters. In this paper, the transient thermal processes of anode in vacuum arc in a half of arc current cycle with frequency of 50 Hz are simulated by finite element analysis software, ANSYS. Some important phenomena of anode, e.g., the melt and solidification, mass loss due to evaporation, are investigated.

Using the Vibration signatures obtained during the operations as the original data, a mechanical condition monitoring method for vacuum circuit breaker is developed in this paper. The method combined the time-frequency analysis and the condition recognition based on artificial neural network. During preprocessing, the vibration signature was decomposed into individual frequency bands using the arithmetic of wavelet packets. The signal energy in the main frequency bands was used to form the condition feature vector, which was input to the artificial neural network for condition recognition. By introducing the parameter of approximation degree, a new recognition arithmetic based on Radial Basis Function was constructed. This approach could not only distinguish these conditions that belong to different known condition modes but also distinguish new condition modes.