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End effect restraint of Hilbert-Huang transform and its application in power quality monitoring

LI Keliang

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Abstract

HHT(Hilbert-Huang Transform)is used to acquire accurate instantaneous frequency and amplitude in power quality monitoring,but its end effect influences the detection results seriously. A method combining artificial neural network and mirror extension is proposed to extend short signal series for end effect restraint.A three-layer BP neural network is used to extend both ends of signal series and the EMD(Empirical Mode Decomposition)with mirror extension procedure to decompose the extended signal,by which IMFs(Intrinsic Mode Functions)with same length as original signal are obtained.To restrain the end effect of Hilbert transform,BP neural network is used again to extend the obtained IMFs and the Hilbert transform of the extended IMFs is then carried out to get accurate instantaneous frequency and amplitude.Its application in the harmonic analysis of power system shows its effectiveness.

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What this paper is about

HHT(Hilbert-Huang Transform)is used to acquire accurate instantaneous frequency and amplitude in power quality monitoring,but its end effect influences the detection results seriously. A method combining artificial neural network and mirror extension is proposed to extend short signal series for end effect restraint.A three-layer BP neural network is used to extend both ends of signal series and the EMD(Empirical Mode Decomposition)with mirror extension procedure to decompose the extended signal,by which IMFs(Intrinsic Mode Functions)with same length as original signal are obtained.To restrain the end effect of Hilbert transform,BP neural network is used again to extend the obtained IMFs and the Hilbert transform of the extended IMFs is then carried out to get accurate instantaneous frequency and amplitude.Its application in the harmonic analysis of power system shows its effectiveness.

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Available abstract

HHT(Hilbert-Huang Transform)is used to acquire accurate instantaneous frequency and amplitude in power quality monitoring,but its end effect influences the detection results seriously. A method combining artificial neural network and mirror extension is proposed to extend short signal series for end effect restraint.A three-layer BP neural network is used to extend both ends of signal series and the EMD(Empirical Mode Decomposition)with mirror extension procedure to decompose the extended signal,by which IMFs(Intrinsic Mode Functions)with same length as original signal are obtained.To restrain the end effect of Hilbert transform,BP neural network is used again to extend the obtained IMFs and the Hilbert transform of the extended IMFs is then carried out to get accurate instantaneous frequency and amplitude.Its application in the harmonic analysis of power system shows its effectiveness.

Key concepts: Hilbert–Huang transform, Hilbert transform, Instantaneous phase, SIGNAL (programming language), Artificial neural network, Hilbert spectral analysis, Series (stratigraphy), Mode (computer interface)

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