Intelligent harmonic load model based on neural networks

Pyeong-Shik Ji; Dae-Jong Lee; Jong-Pil Lee; Jae-Won Park; Jae-Yoon Lim

doi:10.1117/12.784118

9 January 2008 Intelligent harmonic load model based on neural networks

Pyeong-Shik Ji, Dae-Jong Lee, Jong-Pil Lee, Jae-Won Park, Jae-Yoon Lim

Proceedings Volume 6794, ICMIT 2007: Mechatronics, MEMS, and Smart Materials; 679450 (2008) https://doi.org/10.1117/12.784118
Event: ICMIT 2007: Mechatronics, MEMS, and Smart Materials, 2007, Gifu, Japan

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.

Citation Download Citation

Pyeong-Shik Ji, Dae-Jong Lee, Jong-Pil Lee, Jae-Won Park, and Jae-Yoon Lim "Intelligent harmonic load model based on neural networks", Proc. SPIE 6794, ICMIT 2007: Mechatronics, MEMS, and Smart Materials, 679450 (9 January 2008); https://doi.org/10.1117/12.784118

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