De-noising of Power Quality Disturbance Detection Based on Ensemble Empirical Mode Decomposition Threshold Algorithm
Abstract: 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 (Signal-to-Noise Ratio). As 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 (Mean
Square Error), furthermore the instantaneous attribute of corresponding time by
HHT (Hilbert Huang Transform). Simulation results show that EEMD threshold
de-noising method can make the waveform close to the actual value. The SNR is
higher and the MSE is smaller compared with other four thresholds. The
instantaneous attribute can reflect the actual disturbance signal more exactly.
The optimal 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.
Author: Zhang Xuhong, Han Gang
Han Gang, Chen Liping
Journal Code: jptkomputergg130094
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