DocumentCode :
1864460
Title :
Harmonic Detection Method Using APFFT and Neural Network
Author :
Xiaodong Zhu ; Changguo Shen ; Xuemei Ren
Author_Institution :
Sch. of Autom., Beijing Inst. of Technol., Beijing, China
Volume :
1
fYear :
2013
fDate :
26-27 Aug. 2013
Firstpage :
356
Lastpage :
359
Abstract :
A new algorithm, which utilizes all-phase FFT (APFFT) and artificial neural network (ANN), is presented to detect integer harmonics and non-integer harmonics in power system. In order to improve the accuracy of harmonic parameter estimation, the method incorporates the property of phase invariant of APFFT with high speed sought optimization solution function of ANN. First, the sampled data is processed by windowed APFFT algorithm, and then some of harmonic parameters can be obtained, including the number of harmonics, accurate phases of harmonics, inaccurate magnitudes and frequencies of harmonics. Second, the number of neural nodes, the initial weights and the iterative initial parameters of base function of neural network are set according to the results analyzed with APFFT. Finally, accurate harmonic parameters can be obtained by training ANN. Simulation results demonstrate that the method can detect harmonic parameters with high precision.
Keywords :
fast Fourier transforms; iterative methods; learning (artificial intelligence); neural nets; optimisation; parameter estimation; power engineering computing; power system harmonics; ANN training; APFFT phase invariant property; all-phase FFT; artificial neural network; base function; harmonic detection method; harmonic parameter estimation accuracy improvement; harmonics frequencies; harmonics magnitudes; integer harmonics detection; iterative initial parameters; neural nodes; noninteger harmonics detection; optimization solution function; power system; sampled data processing; windowed APFFT algorithm; Algorithm design and analysis; Artificial neural networks; Frequency measurement; Harmonic analysis; Power system harmonics; Training; all-phase FFT; artificial neural network; harmonic detection; power system;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2013 5th International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-0-7695-5011-4
Type :
conf
DOI :
10.1109/IHMSC.2013.91
Filename :
6643903
Link To Document :
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