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Torque Prediction Based Performance Analysis of Small-Scale Wind Turbines Using Data Driven Modelling Methods

Year 2021, , 260 - 269, 31.03.2021

Muhammed Serdar Kaleli Ali Rıza Kaleli , Cahit Yalçıner

https://doi.org/10.18185/erzifbed.758924

Abstract

Bu çalışmada, rüzgar türbinlerinin rotor torku, tasarlanan küçük ölçekli Savonius ve dört yaprak rotor için toplanan gerçek zamanlı verilere dayanan makine öğrenme yaklaşımı kullanılarak tahmin edilmiştir. Uç hız oranı (TSR), makine öğrenimi modelleme tekniğinde doğrusal regresyon (LR), destek vektör makinesi (SVM) regresyonu ve Gauss işlemi (GP) regresyon yöntemlerinde ana giriş parametresi olarak seçilmiştir. Bu modellerin hiperparametreleri ızgara arama yöntemi ile tanımlanmıştır. RMSE, R2, MSE ve MAE, modellerin deneysel verilere tahimin performansını değerlendirmek için kullanılmıştır. Rotor tork modelleme sonuçları, rüzgar türbinlerinin verimliliğinin modellerin yüksek tahmin doğruluğu ile en üst düzeye çıkarılabileceğini göstermiştir. Öte yandan, Savonius tipi rüzgar türbininin torkunun dört yapraklı türbinden daha yüksek olduğu gözlemlenmiştir.

Keywords

Savonius turbine four leaves turbine machine learning techniques

Thanks

This study was produced from part of the first author’s mater thesis “Comparison Of Wind Energy Production Species On Micro Models” was produced which accepted by Graduate School of Natural and Applied Sciences, Çanakkale Onsekiz Mart University in 2018.

References

  • Al-Shamisi, M. H., Assi, A. H., & Hejase, H. A. N. (2013). Artificial neural networks for predicting global solar radiation in Al Ain City - UAE. International Journal of Green Energy, 10(5), 443–456. https://doi.org/10.1080/15435075.2011.641187
  • Fujisawa, N., & Gotoh, F. (1994). Experimental study on the aerodynamic performance of a Savonius rotor.
  • Gasch, R., & Twele, J. (2012). Wind power plants: Fundamentals, design, construction and operation, second edition. In Wind Power Plants: Fundamentals, Design, Construction and Operation, Second Edition. https://doi.org/10.1007/978-3-642-22938-1
  • GWEC (Global Wind Energy Council). (2017). Global Wind Report 2016. In Wind energy technology.
  • Hafner, M., & Isermann, R. (2003). Multiobjective optimization of feedforward control maps in engine management systems towards low consumption and low emissions. Transactions of the Institute of Measurement and Control, 25(1), 57–74. https://doi.org/10.1191/0142331203tm074oa
  • Hayashi, T., Li, Y., & Hara, Y. (2005). Wind tunnel tests on a different phase three-stage Savonius rotor. JSME International Journal, Series B: Fluids and Thermal Engineering, 48(1), 9–16. https://doi.org/10.1299/jsmeb.48.9
  • Kalogirou, S. A. (2000, December 1). Artificial neural networks in renewable energy systems applications: A review. Renewable and Sustainable Energy Reviews, Vol. 5, pp. 373–401. https://doi.org/10.1016/S1364-0321(01)00006-5
  • Kawamura T., Hayashi T., & Miyashita, K. (2001). Application of the Domain Decomposition Method to the Flow around the Savonius Rotor. Proc. of the 12th International Conference on Domain Decomposition Methods, 393–400.
  • McWilliam, M., & Johnson, D. A. (2008). Velocity measurement of flow around model vertical axis wind turbines. International Journal of Green Energy, 5(1–2), 55–68. https://doi.org/10.1080/15435070701845691
  • Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach, Global Edition. Pearson Education Limited.
  • Sargolzaei, J., & Kianifar, A. (2009). Modeling and simulation of wind turbine Savonius rotors using artificial neural networks for estimation of the power ratio and torque. Simulation Modelling Practice and Theory, 17(7), 1290–1298. https://doi.org/10.1016/j.simpat.2009.05.003
  • Shah, N., Zhao, P., Delvescovo, D., & Ge, H. (2019). Prediction of autoignition and flame properties for multicomponent fuels using machine learning techniques. SAE Technical Papers, 2019-April(April). https://doi.org/10.4271/2019-01-1049
  • Wenehenubun, F., Saputra, A., & Sutanto, H. (2015). An experimental study on the performance of Savonius wind turbines related with the number of blades. Energy Procedia, 68, 297–304. https://doi.org/10.1016/j.egypro.2015.03.259

Veriye Dayalı Modelleme Yöntemleri Kullanarak Küçük Ölçekli Rüzgar Türbinlerinin Tork Tahmin Tabanlı Performans Analizi

Year 2021, , 260 - 269, 31.03.2021

Muhammed Serdar Kaleli Ali Rıza Kaleli , Cahit Yalçıner

https://doi.org/10.18185/erzifbed.758924

Abstract

Bu çalışmada, rüzgar türbinlerinin rotor torku, tasarlanan küçük ölçekli Savonius ve dört yaprak rotor için toplanan gerçek zamanlı verilere dayanan makine öğrenme yaklaşımı kullanılarak tahmin edilmiştir. Uç hız oranı (TSR), makine öğrenimi modelleme tekniğinde doğrusal regresyon (LR), destek vektör makinesi (SVM) regresyonu ve Gauss işlemi (GP) regresyon yöntemlerinde ana giriş parametresi olarak seçilmiştir. Bu modellerin hiperparametreleri ızgara arama yöntemi ile tanımlanmıştır. RMSE, R2, MSE ve MAE, modellerin deneysel verilere tahimin performansını değerlendirmek için kullanılmıştır. Rotor tork modelleme sonuçları, rüzgar türbinlerinin verimliliğinin modellerin yüksek tahmin doğruluğu ile en üst düzeye çıkarılabileceğini göstermiştir. Öte yandan, Savonius tipi rüzgar türbininin torkunun dört yapraklı türbinden daha yüksek olduğu gözlemlenmiştir.

Keywords

Savonius türbini Dört Yapraklı Türbin

References

  • Al-Shamisi, M. H., Assi, A. H., & Hejase, H. A. N. (2013). Artificial neural networks for predicting global solar radiation in Al Ain City - UAE. International Journal of Green Energy, 10(5), 443–456. https://doi.org/10.1080/15435075.2011.641187
  • Fujisawa, N., & Gotoh, F. (1994). Experimental study on the aerodynamic performance of a Savonius rotor.
  • Gasch, R., & Twele, J. (2012). Wind power plants: Fundamentals, design, construction and operation, second edition. In Wind Power Plants: Fundamentals, Design, Construction and Operation, Second Edition. https://doi.org/10.1007/978-3-642-22938-1
  • GWEC (Global Wind Energy Council). (2017). Global Wind Report 2016. In Wind energy technology.
  • Hafner, M., & Isermann, R. (2003). Multiobjective optimization of feedforward control maps in engine management systems towards low consumption and low emissions. Transactions of the Institute of Measurement and Control, 25(1), 57–74. https://doi.org/10.1191/0142331203tm074oa
  • Hayashi, T., Li, Y., & Hara, Y. (2005). Wind tunnel tests on a different phase three-stage Savonius rotor. JSME International Journal, Series B: Fluids and Thermal Engineering, 48(1), 9–16. https://doi.org/10.1299/jsmeb.48.9
  • Kalogirou, S. A. (2000, December 1). Artificial neural networks in renewable energy systems applications: A review. Renewable and Sustainable Energy Reviews, Vol. 5, pp. 373–401. https://doi.org/10.1016/S1364-0321(01)00006-5
  • Kawamura T., Hayashi T., & Miyashita, K. (2001). Application of the Domain Decomposition Method to the Flow around the Savonius Rotor. Proc. of the 12th International Conference on Domain Decomposition Methods, 393–400.
  • McWilliam, M., & Johnson, D. A. (2008). Velocity measurement of flow around model vertical axis wind turbines. International Journal of Green Energy, 5(1–2), 55–68. https://doi.org/10.1080/15435070701845691
  • Russell, S., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach, Global Edition. Pearson Education Limited.
  • Sargolzaei, J., & Kianifar, A. (2009). Modeling and simulation of wind turbine Savonius rotors using artificial neural networks for estimation of the power ratio and torque. Simulation Modelling Practice and Theory, 17(7), 1290–1298. https://doi.org/10.1016/j.simpat.2009.05.003
  • Shah, N., Zhao, P., Delvescovo, D., & Ge, H. (2019). Prediction of autoignition and flame properties for multicomponent fuels using machine learning techniques. SAE Technical Papers, 2019-April(April). https://doi.org/10.4271/2019-01-1049
  • Wenehenubun, F., Saputra, A., & Sutanto, H. (2015). An experimental study on the performance of Savonius wind turbines related with the number of blades. Energy Procedia, 68, 297–304. https://doi.org/10.1016/j.egypro.2015.03.259
There are 13 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Makaleler
Authors

Muhammed Serdar Kaleli This is me 0000-0001-7771-2995

Ali Rıza Kaleli 0000-0002-3234-5922

Cahit Yalçıner 0000-0003-0470-303X

Publication Date March 31, 2021
Published in Issue Year 2021

Cite

APA Kaleli, M. S., Kaleli, A. R., & Yalçıner, C. (2021). Torque Prediction Based Performance Analysis of Small-Scale Wind Turbines Using Data Driven Modelling Methods. Erzincan University Journal of Science and Technology, 14(1), 260-269. https://doi.org/10.18185/erzifbed.758924