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Partial discharge (PD) source signals concurring at different internal positions in gas insulated switchgear (GIS) may be mixed linearly or nonlinearly when they are propagating through GIS pipes. To identify the interior insulation defects in GIS, it is important and intricate to extract these individual PD source signals from the mixed PD signals. This paper applies the blind source separation (BSS) theory to acquire individual source signals assisted by complex wavelet transform. First, a maximizing signal-to-noise ratio (MSNR) BSS algorithm is introduced. Then simulated PD mixed signals are constructed from four individual ultra-high frequency theoretical PD signals and employed to implement BSS separation. Finally, two typical individual PD signals sampled from GIS model are adopted for testing. Both can produce satisfying result, which shows that it is feasible to apply BSS to separate the mixed PD signals in GIS.