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Actual power quality signal which is often affected by noise pollution impacts the analysis results of the disturbance signal. In this paper, EEMD (Ensemble Empirical Mode Decomposition) -based threshold de-noising method is proposed for power quality signal with different SNR ( S ignal-to- N oise R atio ). A s a comparison, we use other four thresholds, namely, the heuristic threshold, the self-adaptive threshold, the fixed threshold and the minimax threshold to filter the noises from power quality signal. Through the analysis and comparison of three characteristics of the signal pre-and-post de-noised, including waveforms, SNR and MSE ( M ean S quare E rror), furthermore the instantaneous attribute of corresponding time by HHT (Hilbert Huang T ransform). Simulation results show that EEMD threshold de-noising method can make the wavefor m clos e to the actual value . T he SNR is high er and the MSE is small er c ompared wit h other four thresholds . T he instantaneous attribute can reflect the actual disturbance signal more exactly. The o ptimal threshold EEMD -based algorithm is proposed for power quality disturbance signal de-noising. Meanwhile, EEMD threshold de-noising method with adaptivity is suitable for composite disturbance signal de-noising.

De-noising of Power Quality Disturbance Detection Based on Ensemble Empirical Mode Decomposition Threshold Algorithm