Title :
Harmonic Detection Using the Direct Weight Determination Neural Network
Author :
Li Han ; Ruan Xiu-Kai ; Zhu Xiang-Ou
Author_Institution :
Key Lab. of Low-voltage Apparatus Intellectual Technol., Zhejiang Wenzhou Univ., Wenzhou, China
Abstract :
This paper presents a novel harmonic detection algorithm using the direct weight determination neural network for the electric power system. A new ANN structure is designed to strengthen the real-time capability of harmonic detection. The proposed algorithm employs the weight computation with sine base function to address the problem of harmonic detection. The optimal weight of this ANN with sine base function can be achieved by direct computation. This ANN can avoid the tediously long weight training and get the proper weight including the information of phase and amplitude of harmonic detection. The simulation computation demonstrates this algorithm has high precision and low computational complexity, and it has value in the harmonic detection of electric power system.
Keywords :
computational complexity; neural nets; power engineering computing; power system harmonics; power system reliability; ANN structure; amplitude information; computational complexity; direct weight determination neural network; electric power system; harmonic detection algorithm; long weight training; phase information; real-time harmonic detection capability; simulation computation; sine base function; Artificial neural networks; Harmonic analysis; Mathematical model; Power harmonic filters; artificial neural network; electric power system; harmonic detection; weight computation;
Conference_Titel :
Information Technology and Applications (ITA), 2013 International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4799-2876-7
DOI :
10.1109/ITA.2013.77