NDLI: Inter-harmonics analysis method based on particle swarm optimization algorithm

Content Provider	IEEE Xplore Digital Library
Author	Guo Tian-dong Wang Jing-yu Guo Song-lin
Copyright Year	2013
Description	Author affiliation: Coll. of Electr. & Inf. Eng., Heilongjiang Inst. of Sci. & Technol., Harbin, China (Guo Tian-dong; Wang Jing-yu; Guo Song-lin)
Abstract	With the deterioration of harmonics pollution in power system, it is of great importance to accurately find out the harmonics component for the safe and economical operation of the power system. To effectively detect the harmonics and inter harmonics in electrical signals, an input matrix is built with series of time delay sampling signals. The waveform of each component can be obtained by independent component analysis (ICA) based on maximum likelihood estimation, and frequencies of the fundamental wave, harmonics and inter- harmonics in signal can be obtained also. Using these values as initial condition, particle swarm optimization (PSO) algorithm is run and the amplitude and phase of each component consisting of the signals are accurately computed. Matlab simulation results show that this method is more accurate to analysis inter-harmonic parameters, especially to the inter-harmonics between closer frequency, still be able to accurately analyze their parameters.
Sponsorship	IEEE Harbin Sect.
Starting Page	1296
Ending Page	1301
File Size	5628312
Page Count	6
File Format	PDF
e-ISBN	9781479913923
DOI	10.1109/MIC.2013.6758197
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Time-frequency analysis Maximum likelihood estimation Matlab simulation Particle swarm optimization (PSO) Power system harmonics Harmonic analysis Frequency estimation Independent component analysis (ICA) Particle swarm optimization Inter-harmonic Optimization
Content Type	Text
Resource Type	Article

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