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Wind turbine power curve modelling using artificial neural network

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Cited by:

  1. Siyu Tao & Qingshan Xu & Andrés Feijóo & Stefanie Kuenzel & Neeraj Bokde, 2019. "Integrated Wind Farm Power Curve and Power Curve Distribution Function Considering the Wake Effect and Terrain Gradient," Energies, MDPI, vol. 12(13), pages 1-14, June.
  2. Juan D. Velásquez & Lorena Cadavid & Carlos J. Franco, 2023. "Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances," Energies, MDPI, vol. 16(19), pages 1-45, October.
  3. Davide Astolfi & Francesco Castellani & Andrea Lombardi & Ludovico Terzi, 2021. "Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring," Energies, MDPI, vol. 14(4), pages 1-18, February.
  4. Usama Aziz & Sylvie Charbonnier & Christophe Berenguer & Alexis Lebranchu & Frederic Prevost, 2022. "A Multi-Turbine Approach for Improving Performance of Wind Turbine Power-Based Fault Detection Methods," Energies, MDPI, vol. 15(8), pages 1-21, April.
  5. Qian, Guo-Wei & Ishihara, Takeshi, 2022. "A novel probabilistic power curve model to predict the power production and its uncertainty for a wind farm over complex terrain," Energy, Elsevier, vol. 261(PA).
  6. Gonzalez, Elena & Stephen, Bruce & Infield, David & Melero, Julio J., 2019. "Using high-frequency SCADA data for wind turbine performance monitoring: A sensitivity study," Renewable Energy, Elsevier, vol. 131(C), pages 841-853.
  7. Li, Tenghui & Liu, Xiaolei & Lin, Zi & Morrison, Rory, 2022. "Ensemble offshore Wind Turbine Power Curve modelling – An integration of Isolation Forest, fast Radial Basis Function Neural Network, and metaheuristic algorithm," Energy, Elsevier, vol. 239(PD).
  8. Pengfei Zhang & Zuoxia Xing & Shanshan Guo & Mingyang Chen & Qingqi Zhao, 2022. "A New Wind Turbine Power Performance Assessment Approach: SCADA to Power Model Based with Regression-Kriging," Energies, MDPI, vol. 15(13), pages 1-15, July.
  9. Khaled, Mohamed & Ibrahim, Mostafa M. & Abdel Hamed, Hesham E. & AbdelGwad, Ahmed F., 2019. "Investigation of a small Horizontal–Axis wind turbine performance with and without winglet," Energy, Elsevier, vol. 187(C).
  10. Lin, Zi & Liu, Xiaolei, 2020. "Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network," Energy, Elsevier, vol. 201(C).
  11. Karamichailidou, Despina & Kaloutsa, Vasiliki & Alexandridis, Alex, 2021. "Wind turbine power curve modeling using radial basis function neural networks and tabu search," Renewable Energy, Elsevier, vol. 163(C), pages 2137-2152.
  12. Ciulla, G. & D’Amico, A. & Di Dio, V. & Lo Brano, V., 2019. "Modelling and analysis of real-world wind turbine power curves: Assessing deviations from nominal curve by neural networks," Renewable Energy, Elsevier, vol. 140(C), pages 477-492.
  13. Marčiukaitis, Mantas & Žutautaitė, Inga & Martišauskas, Linas & Jokšas, Benas & Gecevičius, Giedrius & Sfetsos, Athanasios, 2017. "Non-linear regression model for wind turbine power curve," Renewable Energy, Elsevier, vol. 113(C), pages 732-741.
  14. Aziz, Usama & Charbonnier, Sylvie & Bérenguer, Christophe & Lebranchu, Alexis & Prevost, Frederic, 2021. "Critical comparison of power-based wind turbine fault-detection methods using a realistic framework for SCADA data simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
  15. Ruiz de la Hermosa González-Carrato, Raúl, 2018. "Wind farm monitoring using Mahalanobis distance and fuzzy clustering," Renewable Energy, Elsevier, vol. 123(C), pages 526-540.
  16. Shahram Hanifi & Xiaolei Liu & Zi Lin & Saeid Lotfian, 2020. "A Critical Review of Wind Power Forecasting Methods—Past, Present and Future," Energies, MDPI, vol. 13(15), pages 1-24, July.
  17. Cambron, P. & Lepvrier, R. & Masson, C. & Tahan, A. & Pelletier, F., 2016. "Power curve monitoring using weighted moving average control charts," Renewable Energy, Elsevier, vol. 94(C), pages 126-135.
  18. Miguel Á. Rodríguez-López & Emilio Cerdá & Pablo del Rio, 2020. "Modeling Wind-Turbine Power Curves: Effects of Environmental Temperature on Wind Energy Generation," Energies, MDPI, vol. 13(18), pages 1-21, September.
  19. Sabir, Zulqurnain & Wahab, Hafiz Abdul & Umar, Muhammad & Erdoğan, Fevzi, 2019. "Stochastic numerical approach for solving second order nonlinear singular functional differential equation," Applied Mathematics and Computation, Elsevier, vol. 363(C), pages 1-1.
  20. Giannakis, Andreas & Karlis, Athanasios & Karnavas, Yannis L., 2018. "A combined control strategy of a DFIG based on a sensorless power control through modified phase-locked loop and fuzzy logic controllers," Renewable Energy, Elsevier, vol. 121(C), pages 489-501.
  21. Helbing, Georg & Ritter, Matthias, 2018. "Deep Learning for fault detection in wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 189-198.
  22. Tugce Demirdelen & Pırıl Tekin & Inayet Ozge Aksu & Firat Ekinci, 2019. "The Prediction Model of Characteristics for Wind Turbines Based on Meteorological Properties Using Neural Network Swarm Intelligence," Sustainability, MDPI, vol. 11(17), pages 1-18, September.
  23. Díaz, Santiago & Carta, José A. & Castañeda, Alberto, 2020. "Influence of the variation of meteorological and operational parameters on estimation of the power output of a wind farm with active power control," Renewable Energy, Elsevier, vol. 159(C), pages 812-826.
  24. Sebastiani, Alessandro & Peña, Alfredo & Troldborg, Niels, 2023. "Numerical evaluation of multivariate power curves for wind turbines in wakes using nacelle lidars," Renewable Energy, Elsevier, vol. 202(C), pages 419-431.
  25. Nielson, Jordan & Bhaganagar, Kiran & Meka, Rajitha & Alaeddini, Adel, 2020. "Using atmospheric inputs for Artificial Neural Networks to improve wind turbine power prediction," Energy, Elsevier, vol. 190(C).
  26. Xu, Keyi & Yan, Jie & Zhang, Hao & Zhang, Haoran & Han, Shuang & Liu, Yongqian, 2021. "Quantile based probabilistic wind turbine power curve model," Applied Energy, Elsevier, vol. 296(C).
  27. Lahouar, A. & Ben Hadj Slama, J., 2017. "Hour-ahead wind power forecast based on random forests," Renewable Energy, Elsevier, vol. 109(C), pages 529-541.
  28. Meng Li & Shuangxin Wang, 2019. "Dynamic Fault Monitoring of Pitch System in Wind Turbines using Selective Ensemble Small-World Neural Networks," Energies, MDPI, vol. 12(17), pages 1-20, August.
  29. Sabir, Zulqurnain & Wahab, Hafiz Abdul & Umar, Muhammad & Sakar, Mehmet Giyas & Raja, Muhammad Asif Zahoor, 2020. "Novel design of Morlet wavelet neural network for solving second order Lane–Emden equation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 172(C), pages 1-14.
  30. Dokur, Emrah & Erdogan, Nuh & Salari, Mahdi Ebrahimi & Karakuzu, Cihan & Murphy, Jimmy, 2022. "Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine," Energy, Elsevier, vol. 248(C).
  31. Ravi Pandit & David Infield, 2018. "Gaussian Process Operational Curves for Wind Turbine Condition Monitoring," Energies, MDPI, vol. 11(7), pages 1-20, June.
  32. Marino Marrocu & Luca Massidda, 2017. "A Simple and Effective Approach for the Prediction of Turbine Power Production From Wind Speed Forecast," Energies, MDPI, vol. 10(12), pages 1-14, November.
  33. Francisco Bilendo & Angela Meyer & Hamed Badihi & Ningyun Lu & Philippe Cambron & Bin Jiang, 2022. "Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review," Energies, MDPI, vol. 16(1), pages 1-38, December.
  34. Yan, Jie & Zhang, Hao & Liu, Yongqian & Han, Shuang & Li, Li, 2019. "Uncertainty estimation for wind energy conversion by probabilistic wind turbine power curve modelling," Applied Energy, Elsevier, vol. 239(C), pages 1356-1370.
  35. Sergio Velázquez Medina & José A. Carta & Ulises Portero Ajenjo, 2019. "Performance Sensitivity of a Wind Farm Power Curve Model to Different Signals of the Input Layer of ANNs: Case Studies in the Canary Islands," Complexity, Hindawi, vol. 2019, pages 1-11, March.
  36. Muhammad Shahzad Nazir & Fahad Alturise & Sami Alshmrany & Hafiz. M. J Nazir & Muhammad Bilal & Ahmad N. Abdalla & P. Sanjeevikumar & Ziad M. Ali, 2020. "Wind Generation Forecasting Methods and Proliferation of Artificial Neural Network: A Review of Five Years Research Trend," Sustainability, MDPI, vol. 12(9), pages 1-27, May.
  37. Han, Shuang & Qiao, Yanhui & Yan, Ping & Yan, Jie & Liu, Yongqian & Li, Li, 2020. "Wind turbine power curve modeling based on interval extreme probability density for the integration of renewable energies and electric vehicles," Renewable Energy, Elsevier, vol. 157(C), pages 190-203.
  38. Davide Astolfi & Francesco Castellani & Ludovico Terzi, 2018. "Wind Turbine Power Curve Upgrades," Energies, MDPI, vol. 11(5), pages 1-17, May.
  39. Rogers, T.J. & Gardner, P. & Dervilis, N. & Worden, K. & Maguire, A.E. & Papatheou, E. & Cross, E.J., 2020. "Probabilistic modelling of wind turbine power curves with application of heteroscedastic Gaussian Process regression," Renewable Energy, Elsevier, vol. 148(C), pages 1124-1136.
  40. Sebastiani, Alessandro & Angelou, Nikolas & Peña, Alfredo, 2024. "Wind turbine power curve modelling under wake conditions using measurements from a spinner-mounted lidar," Applied Energy, Elsevier, vol. 364(C).
  41. Mehrjoo, Mehrdad & Jafari Jozani, Mohammad & Pawlak, Miroslaw, 2020. "Wind turbine power curve modeling for reliable power prediction using monotonic regression," Renewable Energy, Elsevier, vol. 147(P1), pages 214-222.
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