To investigate the decomposition law of SF6 under negative direct current partial discharge (PD) at different energies, a SF6 PD hybrid numerical model based on fluid dynamics and plasma chemical reaction models, in which 14 particle species and 24 chemical reactions are considered, is proposed. The effectiveness of the proposed model is validated with the current pulse waveform and the V– I discharge curve obtained by experiment. The influence of discharge energy on SF6 PD characteristic quantities and SF6 decomposition products is investigated with simulation and experiment. The results show that most of the discharge area of SF6 is neutral, and the cation clouds only exist in the ionosphere (4.79–5 mm). With the increase in applied voltage, the electric field intensity of the needle plate gap does not increase completely and even decreases in some areas. Moreover, different from the traditional opinion, the generation of SO2F2 under PD is mainly generated by the hydrolysis reaction of SOF4, which is formed by [SF5], [SF4], and [OH], [O]. The reaction path of [SF2] with O2 is not important. Thus, c(SO2F2)/ c(SOF2 + SO2) can be used as the energy characteristic component ratio because of its ability to represent the low-fluorine sulfide ratio n([SF5])/ n([SF4]).

Predicting telemetry data is vital for the proper operation of orbiting spacecraft. The Grey–Markov model with sliding window (GMSW) combines Grey model (GM (1, 1)) and Markov chain forecast model, which allows it to describe the fluctuation of telemetry data. However, the Grey–Markov model with sliding window does not provide better predictions of telemetry series with the pseudo-periodic phenomenon. To overcome this drawback, we improved the GMSW model by applying particle swarm optimization (PSO) algorithm a sliding window for better prediction of spacecraft telemetry data (denoted as PGMSW model). In order to produce more accurate predictions, background-value optimization is specially carried out using the particle swarm optimization technique in conventional GM (1, 1). For verifying PGMSW, it is utilized in the prediction of the cyclic fluctuation of telemetry series data and exponential variations therein. The simulation results indicate that the PGMSW model provides accurate solutions for prediction problems similar to the pseudo-periodic telemetry series.

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