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Critical start-time and end-time are important indices for power quality disturbances. But the sampling signals often have noisy component, the locations of start-time and end-time are hard to get. Wavelet is an effective tool for those non-stationary signal processing and has been used in this field. Local feature in the signal can be enlarged after the transformation using the scalar wavelet. But scalar wavelets cannot contain orthogonality, symmetry, compact support and higher order of vanishing moments simultaneously. In this thesis, multi-wavelets GHM is used to detect and locate power quality disturbances. Multi-wavelets offer many excellent properties such as the same approximation order but more compact support. The dependence of the multi-wavelets coefficients varies with the level, so neighboring coefficients dependent on level scheme is used to decrease the effect of noise. The size of neighbor varies with the dependence of the coefficients. After de-noising, the critical starttime and end-time can be detected easily. Simulation results show the correctness of the proposed method.