Share Email Print

Proceedings Paper

Intelligent harmonic load model based on neural networks
Author(s): Pyeong-Shik Ji; Dae-Jong Lee; Jong-Pil Lee; Jae-Won Park; Jae-Yoon Lim
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

In this study, we developed a RBFNs(Radial Basis Function Networks) based load modeling method with harmonic components. The developed method implemented by using harmonic information as well as fundamental frequency and voltage which are essential input factors in conventional method. Thus, the proposed method makes it possible to effectively estimate load characteristics in power lines with harmonics. The RBFNs have certain advantage such as simple structure and rapid computation ability compared with multilayer perceptron which is extensively applied for load modeling. To show the effectiveness, the proposed method has been intensively tested with various dataset acquired under the different frequency and voltage and compared it with conventional methods such as polynominal 2nd equation method, MLP and RBF without considering harmonic components.

Paper Details

Date Published: 9 January 2008
PDF: 6 pages
Proc. SPIE 6794, ICMIT 2007: Mechatronics, MEMS, and Smart Materials, 679450 (9 January 2008); doi: 10.1117/12.784118
Show Author Affiliations
Pyeong-Shik Ji, Chungju National Univ. (South Korea)
Dae-Jong Lee, Chungbuk National Univ. (South Korea)
Jong-Pil Lee, Chungbuk National Univ. (South Korea)
Jae-Won Park, Chungju National Univ. (South Korea)
Jae-Yoon Lim, Daeduk College (South Korea)

Published in SPIE Proceedings Vol. 6794:
ICMIT 2007: Mechatronics, MEMS, and Smart Materials
Minoru Sasaki; Gisang Choi Sang; Zushu Li; Ryojun Ikeura; Hyungki Kim; Fangzheng Xue, Editor(s)

© SPIE. Terms of Use
Back to Top