Abstract
In this paper wind turbines operational states classification is considered. The fuzzy-ART neural network is proposed as a classifying system. Applying of stereographic projection as an input signals normalization procedure is introduced. Both theoretical justification is discussed and results of experiments are presented. It turns out that the introduced normalization procedure improves classification results.
The paper was supported by the Polish Ministry of Science and Higher Education under Grant No. N504 147838.
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References
Banakar, H., Ooi, B.T.: Clustering of wind farms and its sizing impact. IEEE Transactions on Energy Conversion 24, 935–942 (2009)
Bielecki, A., Bielecka, M., Chmielowiec, A.: Input signals normalization in Kohonen neural networks. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 3–10. Springer, Heidelberg (2008)
Barszcz, T., Bielecki, A., Wójcik, M.: ART-type artificial neural networks applications for classification of operational states in wind turbines. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS (LNAI), vol. 6114, pp. 11–18. Springer, Heidelberg (2010)
Carpenter, G.A., Grossberg, S.: A massively parallel architecture for a self-organizing neural pattern recognition machine. Computer Vision, Graphics, and Image Processing 37, 54–115 (1987)
Carpenter, G.A., Grossberg, S.: ART2: self-organization of stable category recognition codes for analog input pattern. Applied Optics 26, 4919–4930 (1987)
Carpenter, G.A., Grossberg, S., Rosen, D.B.: Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks 4, 759–771 (1991)
Ezio, S., Claudio, C.: Exploitation of wind as an energy source to meet the worlds electricity demand. Wind Engineering 74-76, 375–387 (1998)
Frank, T., Kraiss, K.F., Kuhlen, T.: Comparative analysis of fuzzy ART and ART-2A network clustering performance. IEEE Transactions on Neural Networks 9, 544–559 (1998)
Gancarzewicz, J.: Differential Geometry, PWN, Warszawa (1987) (in Polish)
Georgiopoulos, M., Fernlund, H., Bebis, G., Heileman, G.L.: Order of search in fuzzy ART and fuzzy ARTMAP: effect of the choice parameter. Neural Networks 9, 1541–1559 (1996)
Hameeda, Z., Honga, Y.S., Choa, T.M., Ahnb, S.H., Son, C.K.: Condition monitoring and fault detection of wind turbines and related algorithms: A review. Renewable and Sustainable Energy Reviews 13, 1–39 (2009)
Huang, J., Georgiopoulos, M., Heileman, G.L.: Fuzzy ART properties. Neural Networks 8, 203–213 (1995)
Paska, J., Sałek, M., Surma, T.: Current status and perspectives of renewable energy sources in Poland. Renewable and Sustainable Energy Reviews 13, 142–154 (2009)
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Barszcz, T., Bielecka, M., Bielecki, A., Wójcik, M. (2011). Wind Turbines States Classification by a Fuzzy-ART Neural Network with a Stereographic Projection as a Signal Normalization. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20267-4_24
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DOI: https://doi.org/10.1007/978-3-642-20267-4_24
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