DocumentCode :
3457845
Title :
An artificial neural network based method for harmonic detection in power system
Author :
Na, He ; Lina, Huang ; Jian, Wu ; Dianguo, Xu
Author_Institution :
Dept. of Electr. Eng., Harbin Inst. of Technol., Harbin
fYear :
2008
fDate :
24-28 Feb. 2008
Firstpage :
456
Lastpage :
461
Abstract :
A novel advanced harmonic detection method based on neural network (NN) is proposed in this paper. It is an adaptive harmonic detection method with variable step-size based on adaptive linear NN and self-adaptive noise countervailing principle. And this proposed method adopts a sliding integrator to extract the real tracing error and then uses a fuzzy adjuster with self-adjustable factor to modify the step-size. So the novel harmonic detection method can obtain fast convergence speed and high steady-state precision at the same time. Comparisons are made between conventional harmonic detection methods based on NN and the advanced method based on NN proposed in this paper. Finally detailed simulation and experimental results verify the validity and superiority of the advanced methods.
Keywords :
fuzzy set theory; neural nets; power engineering computing; power system harmonics; adaptive harmonic detection method; adaptive linear NN; artificial neural network; fuzzy adjuster; high steady-state precision; power system; self-adaptive noise countervailing principle; sliding integrator; variable step-size; Active filters; Artificial neural networks; Convergence; Fuzzy systems; Neural networks; Power harmonic filters; Power system harmonics; Power system simulation; Robustness; Steady-state; Harmonic detection; fuzzy adjustor; neural network (NN); selfadaptive noise countervailing principle;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Applied Power Electronics Conference and Exposition, 2008. APEC 2008. Twenty-Third Annual IEEE
Conference_Location :
Austin, TX
ISSN :
1048-2334
Print_ISBN :
978-1-4244-1873-2
Electronic_ISBN :
1048-2334
Type :
conf
DOI :
10.1109/APEC.2008.4522761
Filename :
4522761
Link To Document :
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