In order to extract the disturbance features accurately in strong noisy environment, a power quality disturbance detection and location algorithm based on improved wavelet threshold function denoising and Variational Mode Decomposition（VMD） is proposed. The improved wavelet threshold function is used to denoise the noisy power quality disturbance signal. The default scale can be determined by the Fourier transform. This paper uses the variational mode decomposition to decompose signals into some intrinsic modes. Hilbert transform is used to extract the characteristic information such as the amplitude and frequency of each mode. Meanwhile, the effective location of the start and stop time of the disturbance signal is realized by the principle of singular value decomposition. A power quality disturbance detection platform based on PXI and LabVIEW is also built based on the above algorithm. The accuracy and effectiveness of the proposed algorithm are verified by single disturbance, complex disturbance and actual disturbance data. Compared with the existing empirical mode decomposition and ensemble empirical mode decomposition, the proposed algorithm not only has the ability of resisting modal aliasing and false components, but also has high accuracy and robustness under the environment of strong noise.