Regarding the problem of insufficient identification accuracy caused by noise interference and difficult determination of key parameters when using the variational mode decomposition (VMD) algorithm to decompose the sub-synchronous oscillation signals generated during the grid connection process of wind power, this paper proposes a signal decomposition algorithm based on wavelet threshold denoising (WTD) and genetic algorithm (GA) optimized VMD, combining with the sub-synchronous oscillation mode identification method of autoregressive moving average model (ARMA). Firstly, wavelet threshold denoising is used to process the active power output of the wind turbine; secondly, VMD is used to decompose the denoised signal, obtaining K intrinsic mode components. In order to achieve the optimal VMD decomposition effect, an adaptive genetic algorithm is used to optimize the penalty factor α and the number of decomposition layers K. Finally, the signal is restructured and an ARMA model is established to directly identify the frequency and damping ratio of the sub-synchronous oscillation signal. By building a simulation experiment platform for direct-drive wind turbine grid connection model and collecting sub-synchronous oscillation signals for mode identification, the simulation results show that, compared with other identification algorithms, the proposed VMD-based method has better feasibility and superiority.