Swiftly and accurately analyze non-stationary disturbance signals within the power grid,a location and de-tection method for transient power quality disturbance that combines singular value decomposition(SVD)and im-proved local mean decomposition(ILMD)is proposed.First,noise information is processed by using ILMD and a fuzzy membership function threshold to mitigate noise interference.Then,a difference signal is formulated,and a sliding window SVD is employed to amplify the disturbance features while further suppressing noise interference.In conclusion,an adaptive threshold truncation-based approach for localizing and detecting transient power quality dis-turbances is proposed,utilizing the feature-enhanced signal.Simulation analysis and algorithm comparisons confirm that the proposed method exhibits precise location,robust resistance to noise,and low computational complexity.Moreover,it demonstrates excellent performance in detecting zero-crossing and minor disturbances.
transient power qualitydisturbance location and detectiondifference signalSVDLMD
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