Estimation of operational parameters of the counter-rotating wind turbine with artificial neural networks

• Autorzy: Romański L. , Bieniek J. , Komarnicki P. , Dębowski M. , Detyna J.

Identyfikatory

DOI

10.1016/j.acme.2017.04.010

• Języki publikacji: EN

Abstrakty

EN

The article presents the possibility of using artificial neural networks to model the operating parameters of a counter-rotation mini wind turbine. The work is based on data from wind turbine research results conducted in an aerodynamic tunnel in the Institute of Agricultural Engineering at the University of Environmental and Life Sciences in Wrocław. The correlation between the depended variable (generated power P) and independent variables (average velocity of the air stream v, wedge angle of the first rotor blades, wedge angle of the second rotor blades, distance between rotors) was examined. The quality of the network was verified based on the mean-square error. The construction of the turbine allows to steplessly change of the blades wedge angle and the distance between rotors. Hence in the future the constructed network model can be used for programming the controller allowing to optimize the operating parameters of the wind turbine to maximize the generated power.

Słowa kluczowe

EN

exploitation; counter-rotation mini wind turbine; aerodynamic tunnel; twin rotor wind turbine

PL

eksploatacja; turbina wiatrowa; tunel aerodynamiczny

• Wydawca: Springer ; Wrocław University of Science and Technology
• Czasopismo: Archives of Civil and Mechanical Engineering
• Rocznik: 2017
• Tom: Vol. 17, no. 4
• Strony: 1019--1028
• Opis fizyczny: Bibliogr. 30 poz., fot., rys., tab., wykr.

Twórcy

autor

Romański L.

• Institute of Agricultural Engineering at the Univ

autor

Bieniek J.

• Institute of Agricultural Engineering at the Univ

autor

Komarnicki P.

• Institute of Agricultural Engineering at the Univ

autor

Dębowski M.

• Institute of Agricultural Engineering at the Univ

autor

Detyna J.

• Faculty of Mechanical Engineering, Department of Mechanics, Materials Science and Engineering, Wrocław University of Science and Technology, 25 Smoluchowskiego St., 50-370 Wrocław, Poland

Bibliografia

• [1] J.F. Manwell, J.G. McCowan, A.L. Rogers, Wind Energy Explained: Theory, Design and Application, Second edition, 2006.
• [2] W.S.McCulloch,W.Pitts,A logicalcalculus of theideasimmanent in nervous activity, Bulletin of Mathematical Biophysics 5 (1943) 115–133. http://dx.doi.org/10.1007/BF02478259.
• [3] L.A. Dobrzański, T. Tański, Zastosowanie sieci neuronowych do optymalizacji warunków obróbki cieplnej stopów Mg-Al, Mechanika/Mechanics, 2011, pp. 81–86.
• [4] C.M. Bishop, Neural networks and their applications, Review of Scientific Instruments 65 (1994) 1803–1831. http://dx.doi. org/10.1063/1.1144830.
• [5] R. Tadeusiewicz, T. Gąciarz, B. Borowik, B. Leper, Elementary Introduction to Neural Networks Technology With Example Programs, Telekom, Moskwa, 2011.
• [6] A. Pawlak, Analiza pracy dwuwirnikowej elektrowni wiatrowej, Uniwersytet Przyrodniczy we Wrocławiu, 2012.
• [7] A. Flaga, Inżynieria wiatrowa, Arkady, Warszawa, 2008.
• [8] C. Stergiou, D. Siganos, Neural networks, Internet (2014) 1–15. http://www.doc.ic.ac.uk/_nd/surprise_96/journal/vol4/cs11/ report.html.
• [9] R. Tadeusiewicz, Cybernetic modeling and computer simulation of biological systems [In Polish: Modelowanie cybernetyczne i symulacja komputerowa systemów biologicznych], The works of the Commission of Technical Sciences of Polish Academy of Arts and Sciences [In Polish: Prace Komisji Nauk Technicznych PAU], UWND, Kraków, 2009.
• [10] W.S. McCulloch, W. Pitts, A logical calculus of the idea immanent in nervous activity, Bulletin of Mathematical Biophysics 5 (1943) 115–133. http://dx.doi.org/10.1007/BF02478259.
• [11] R. Tadeusiewicz, Neural networks as a tool for modeling of biological systems, Bio-Algorithms and Med-Systems 11 (2015) 135–144.
• [12] M. Antkowiak, Artificial Neural Networks vs. Support Vector Machines for Skin Diseases Recognition, Neural Networks, 2006. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10. 1.1.160.1206&rep=rep1&type=pdf.
• [13] M. Miernik, M. Kołodziej, The possibilities of applying of artificial neural networks in automatic diagnostic system of machine cutting process, Archives of Civil and Mechanical Engineering 6 (2006) 15–22. http://dx.doi.org/10.1016/S1644- 9665(12)60272-6.
• [14] S. Agatonovic-Kustrin, R. Beresford, Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research, Journal of Pharmaceutical and Biomedical Analysis 22 (2000) 717–727. http://dx.doi.org/ 10.1016/S0731-7085(99)00272-1.
• [15] D. Itchhaporia, P.B. Snow, R.J. Almassy, W.J. Oetgen, Artificial neural networks: current status in cardiovascular medicine, Journal of the American College of Cardiology 28 (1996) 515– 521. http://dx.doi.org/10.1016/0735-1097(96)00174-X.
• [16] M.J.Ł. Jeleń, A. Lipiński, J. Detyna, Grading breast cancer malignancy with neural networks, Bio-Algorithms Med- Systems 7 (2011) 47–53.
• [17] R. Tadeusiewicz, J. Śmietański, Pozyskiwanie obrazów medycznych oraz ich przetwarzanie, analiza, automatyczne rozpoznawanie i diagnostyczna interpretacja, WSTN, Kraków, 2011.
• [18] V. Vapnik, A. Vashist, A new learning paradigm: learning using privileged information, Neural Networks 22 (2009) 544– 557. http://dx.doi.org/10.1016/j.neunet.2009.06.042.
• [19] P. Pławiak, R. Tadeusiewicz, Approximation of phenol concentration using novel hybrid computational intelligence methods, International Journal of Applied Mathematics and Computer Science 24 (2014) 165–181. http://dx.doi.org/10.2478/amcs-2014-0013.
• [20] P. Pławiak, W. Maziarz, Comparison of artificial intelligence methods on the example of tea classification based on signals from E-nose sensors, Advanced Signal Processing 1 (2013) 19–32.
• [21] P. Pławiak, W. Maziarz, Classification of tea specimens using novel hybrid artificial intelligence methods, Sensors and Actuators B: Chemical 192 (2014) 117–125. http://dx.doi.org/ 10.1016/j.snb.2013.10.065.
• [22] W. Wodzisławski, S. Krupa, J. Nowicki, R. Bedziński, J. Detyna, The reaction of the pelvis to the implantation of the acetabular component of the hip endoprosthesis – initial tests with the use of computerized tomography, Acta of Bioengineering and Biomechanics 11 (2009) 45–54.
• [23] R. Tadeusiewicz, R. Chaki, N. Chaki, Exploring Neural Networks with C#, CRC Press, Taylor & Francis Group, Boca Raton, 2014.
• [24] M. Chandrasekaran, M. Muralidhar, C.M. Krishna, U.S. Dixit, Application of soft computing techniques in machining performance prediction and optimization: a literature review, International Journal of Advanced Manufacturing Technology 46 (2010) 445–464. http://dx.doi.org/10.1007/s00170-009-2104-x.
• [25] P. Komarnicki, L. Romański, J. Bieniek, M. Dębowski, K. Pałka, Wpływ regulacji pracy wentylatorów na równomierność rozkładu strumienia powietrza w tunelu aerodynamicznym, Inżynieria Rol. 2 (2013) 155–167.
• [26] M. Dębowski, L. Romański, P. Komarnicki, J. Bieniek, Analiza wpływu wybranych profili łopat wirnika na generowaną moc przez mini siłownię wiatrową, Inżynieria Rol. 145 (2013) 25–33.
• [27] R. Tadeusiewicz, Neural network as a tool for medical signals filtering, diagnosis aid, therapy assistance and forecasting improving, in: Springer (Ed.), IFMBE Proc., Berlin, 2009, 1532–1534.
• [28] L. Romański, J. Bieniek, P. Komarnicki, M. Dębowski, J. Detyna, Operational tests of a dual-rotor mini wind turbine, Eksploatacja i Niezawodnosc – Maintenance and Reliability 18 (2016) 201–209.
• [29] J. David, P. Švec, R. Garzinová, S. Kluska-Nawarecka, D. Wilk- Kołodziejczyk, K. Regulski, Heuristic modeling of casting processes under the conditions uncertainty, Archives of Civil and Mechanical Engineering 16 (2016) 179–185. http://dx.doi. org/10.1016/j.acme.2015.10.006.
• [30] R. Tadeusiewicz, Neural networks and other AI systems for medicine [In Polish: Sieci neuronowe i inne systemy sztucznej inteligencji dla medycyny], in: Biomed. Eng. [In Polish Inżynieria Biomed], UWND, Kraków, 2008, pp. 178–187.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)