Multi-innovation stochastic gradient method for harmonic modelling of power signals

The harmonic parameter identification and modelling problem for power signals are studied. In order to model power signals, the multi-innovation stochastic gradient (MI-SG) is derived based on the multi-innovation identification theory. The proposed MI-SG algorithm repeatedly uses past innovations b...

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Vydané v:IET signal processing Ročník 10; číslo 7; s. 737 - 742
Hlavní autori: Zhou, Lincheng, Li, Xiangli, Xu, Huigang, Zhu, Peiyi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: The Institution of Engineering and Technology 01.09.2016
Predmet:
Algorithms
fast Fourier transform analysis
fast Fourier transforms
Fourier transforms
gradient methods
harmonic parameter identification
Harmonics
Mathematical models
MI‐SG algorithm
Modelling
multiinnovation identification theory
multiinnovation stochastic gradient
multiinnovation stochastic gradient method
parameter estimation
Parameter identification
power signal
power system harmonics
power system parameter estimation
Scalars
stochastic processes
Stochasticity
vectors
power system harmonics
fast Fourier transforms
power system parameter estimation
harmonic parameter identification
power signal
fast Fourier transform analysis
multiinnovation identification theory
vectors
multiinnovation stochastic gradient method
multiinnovation stochastic gradient
parameter estimation
stochastic processes
gradient methods
MI-SG algorithm
ISSN:1751-9675, 1751-9683, 1751-9683
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Popis
Shrnutí:The harmonic parameter identification and modelling problem for power signals are studied. In order to model power signals, the multi-innovation stochastic gradient (MI-SG) is derived based on the multi-innovation identification theory. The proposed MI-SG algorithm repeatedly uses past innovations by expanding the scalar innovation to the innovation vector and can obtain more accurate parameter estimates than the stochastic gradient algorithm. Finally, the simulation results indicate that the proposed algorithm is effective and has a close estimation accuracy compared with the fast Fourier transform analysis.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1751-9675
1751-9683
1751-9683
DOI:10.1049/iet-spr.2015.0280