关键词: 电能质量复合扰动, 去噪, 奇异值分解（SVD）, 自适应多尺度奇异值分解（AMSVD）, 特征检测

Abstract: To improve de-noising performance of Power Quality Mixed Disturbance（PQMD） signals and solve the problem of feature detection for PQMD, a new de-noising method and mathematical framework using Adaptive Multi-resolution Singular Value Decomposition（AMSVD） are proposed. First, the distribution of singular values of Gaussian white noise decomposed by multi-resolution SVD is used to analyze de-noising theory for noisy signal, and then the rule for optimal decomposition level is given according to the difference of approximation signal and detail under different levels, through these procedures adaptive de-noising method for multi-resolution SVD can be implemented for unknown prior information signals. The results show that AMSVD is effective than other 5 filter algorithms in PQMD signals processing with different noise variance. To further verify the feasibility of AMSVD method, the characteristic of the disturbance can be extracted with Hilbert-Huang Transform（HHT）. The simulation results prove that AMSVD is feasible and robust.

Key words: Power Quality Mixed Disturbances（PQMD）, de-noising, Singular Value Decomposition（SVD）, Adaptive Multi-resolution Singular Value Decomposition（AMSVD）, feature detection