In order to improve the detection capability of power quality disturbances, a power quality disturbance detection and location algorithm based on improved wavelet threshold function and complementary ensemble empirical mode decomposition(CEEMD) are proposed. After using CEEMD to process the power quality disturbance, it calculated the random noise intensity of each IMF component by permutation entropy, and the noise intensity is higher than the permutation entropy value by the improved wavelet threshold denoise. The remaining components retain and reconstruct the signal. The HilbertHuang transform locates parameters such as the start point and end of the disturbance and frequency of the disturbance. Compared with CEEMD’s rejection of highfrequency component noise suppression and method based on wavelet threshold denoise, it proved that the proposed algorithm has higher noise immunity. Moreover, through the example of threephase short circuit and twophase short circuit in PSCAD/EMTC Doublefed wind power system, the simulation verified the effectiveness of the proposed algorithm. Finally, it built a platform of power quality disturbance based on PXI and LabVIEW platform. The algorithm laid the foundation for application in engineering practice.