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

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.