To obtain the SF

6

decomposition characteristics under different types of negative direct-current（DC） partial discharge

in preparation for further diagnosis and assessment of faults severity

we designed four typical internal insulation defects of DC inflatable wall-through bushings

and performed a series of experiments. The experimental results show that the partial discharges caused by the four defects decompose the SF

6

gas and generate five stable decomposed components

namely

CF

4

CO

2

SO

2

F

2

SOF

2

and SO

2

. The concentration

effective formation rate

and concentration ratio of SF

6

decomposed components can be associated with the PD types. Furthermore

back propagation neural network algorithm is used to recognize the PD types. The recognition results show that

compared with the concentrations of SF

6

decomposed components

their concentration ratios are more suitable as the characteristic quantity for PD recognition

which can obtain a satisfactory effect in recognizing the PD types.