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Modeling of Photovoltaic Power Systems using Clustering Algorithm and Modular Networks
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 Title & Authors
Modeling of Photovoltaic Power Systems using Clustering Algorithm and Modular Networks
Lee, Chang-Sung; Ji, Pyeong-Shik;
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 Abstract
The real-world problems usually show nonlinear and multi-variate characteristics, so it is difficult to establish concrete mathematical models for them. Thus, it is common to practice data-driven modeling techniques in these cases. Among them, most widely adopted techniques are regression model and intelligent model such as neural networks. Regression model has drawback showing lower performance when much non-linearity exists between input and output data. Intelligent model has been shown its superiority to the linear model due to ability capable of effectively estimate desired output in cases of both linear and nonlinear problem. This paper proposes modeling method of daily photovoltaic power systems using ELM(Extreme Learning Machine) based modular networks. The proposed method uses sub-model by fuzzy clustering rather than using a single model. Each sub-model is implemented by ELM. To show the effectiveness of the proposed method, we performed various experiments by dataset acquired during 2014 in real-plant.
 Keywords
ELM;Modular networks;Photovoltaic power system;
 Language
Korean
 Cited by
 References
1.
Jong-Jae Choi, Chan-Gyu Hwang, Chae-Joo Moon, "Development of Evaluation Model for the Korean New & Renewable Energy Policies : Focusing on RPS & FIT," The Korea Institute of Electronic Communication Sciences, Vol. 8, No. 9, pp. 1333-1342, 2013. crossref(new window)

2.
K. D. King, "The Development of the Short-Term Predict Model for Solar Power Generation," Journal of the Korean Solar Energy Society, Vol. 33, No. 6, pp. 62-69, 2013. crossref(new window)

3.
A. Molki, "Dust affects solar cell efficiency," Physics Education, Vol. 45, pp. 456-458, 2010. crossref(new window)

4.
Pratish Rawat, Pardeep Kumar, "Performance evaluation of solar photovoltaic/thermal systems," International Journal of Science and Research, Vol. 4, Issue 8, pp. 1466-1471, 2015.

5.
C. S. Chin, A. Babu, W. McBride, "Design, modeling and testing of a standalone single axis active solar tracker using MATLAB/Simulink," Renewable Energy, vol. 36, no. 11, pp. 3075-3090, 2011. crossref(new window)

6.
Modeling and fault diagnosis of a photovoltaic systems," Electrical Power Research, Vol. 78, No. 1, pp. 97-105, 2008. crossref(new window)

7.
A. Mellit, S. Kalogirou, "ANFIS-based modelling for photovoltaic power supply system: A case study," Renewable Energy, vol. 36, no. 1, pp. 250-258, 2011. crossref(new window)

8.
Hyun Cheol Cho, Young Jin Jung, "Probabilistic Modeling of Photovoltaic Power Systems with Big Learning Data Sets," Korean Institute of Intelligent Systems, Vol. 23, No. 5, pp. 412-417, 2013. crossref(new window)

9.
Jae-Ju Song, Yoon-Su Jeong, Sang-Ho Lee, "Analysis of prediction model for solar power generation," Journal of Digital Convergence , Vol. 12, No. 3, pp. 243-248, 2014.

10.
Kim Kwang-Deuk, "The Development of the Short-Term Predict Model for Solar Power Generation," The Korean Solar Energy Society, Vol. 33, No. 6, pp. 62-69. 2013. crossref(new window)

11.
Chang-Sung Lee, Pyeong-Shik Ji, "Development of Daily PV Power Forecasting Models using ELM," The Korean Institute of Electrical Engineers, Vol. 64P, No. 3, pp. 164-168. 2015.

12.
J. C. Bezdek, Pattern recognition with fuzzy objective function algorithms, Plenum Press, New York, 1981.

13.
G. B. Huang, Q. Y. Zhu, and C. K. Siew, "Extreme learning machine: a new learning scheme of feedforward neural networks," in Proc. 2004 IEEE Int. Conf. Neural Networks, Vol. 2, pp. 985-990, 2004.