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Artificial neural networks applications in wind energy systems: a review

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  1. Sun, Peng & Li, Jian & Wang, Caisheng & Lei, Xiao, 2016. "A generalized model for wind turbine anomaly identification based on SCADA data," Applied Energy, Elsevier, vol. 168(C), pages 550-567.
  2. Ram, J.Prasanth & Rajasekar, N. & Miyatake, Masafumi, 2017. "Design and overview of maximum power point tracking techniques in wind and solar photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1138-1159.
  3. 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.
  4. 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.
  5. 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).
  6. Lin, Zhongwei & Chen, Zhenyu & Liu, Jizhen & Wu, Qiuwei, 2019. "Coordinated mechanical loads and power optimization of wind energy conversion systems with variable-weight model predictive control strategy," Applied Energy, Elsevier, vol. 236(C), pages 307-317.
  7. Manzoor Ellahi & Ghulam Abbas & Irfan Khan & Paul Mario Koola & Mashood Nasir & Ali Raza & Umar Farooq, 2019. "Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems: A Review," Energies, MDPI, vol. 12(22), pages 1-30, November.
  8. Harish Kumar Ghritlahre & Purvi Chandrakar & Ashfaque Ahmad, 2021. "A Comprehensive Review on Performance Prediction of Solar Air Heaters Using Artificial Neural Network," Annals of Data Science, Springer, vol. 8(3), pages 405-449, September.
  9. Fathabadi, Hassan, 2016. "Novel high-efficient unified maximum power point tracking controller for hybrid fuel cell/wind systems," Applied Energy, Elsevier, vol. 183(C), pages 1498-1510.
  10. Yang, Zhongshan & Wang, Jian, 2018. "A hybrid forecasting approach applied in wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Energy, Elsevier, vol. 160(C), pages 87-100.
  11. 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.
  12. Bugała, A. & Zaborowicz, M. & Boniecki, P. & Janczak, D. & Koszela, K. & Czekała, W. & Lewicki, A., 2018. "Short-term forecast of generation of electric energy in photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 306-312.
  13. Laura Cornejo-Bueno & Lucas Cuadra & Silvia Jiménez-Fernández & Javier Acevedo-Rodríguez & Luis Prieto & Sancho Salcedo-Sanz, 2017. "Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data," Energies, MDPI, vol. 10(11), pages 1-27, November.
  14. Qunli Wu & Chenyang Peng, 2015. "Wind Power Grid Connected Capacity Prediction Using LSSVM Optimized by the Bat Algorithm," Energies, MDPI, vol. 8(12), pages 1-15, December.
  15. Marugán, Alberto Pliego & Márquez, Fausto Pedro García & Perez, Jesus María Pinar & Ruiz-Hernández, Diego, 2018. "A survey of artificial neural network in wind energy systems," Applied Energy, Elsevier, vol. 228(C), pages 1822-1836.
  16. Raffay Rizwan & Jehangir Arshad & Ahmad Almogren & Mujtaba Hussain Jaffery & Adnan Yousaf & Ayesha Khan & Ateeq Ur Rehman & Muhammad Shafiq, 2021. "Implementation of ANN-Based Embedded Hybrid Power Filter Using HIL-Topology with Real-Time Data Visualization through Node-RED," Energies, MDPI, vol. 14(21), pages 1-33, November.
  17. Assareh, Ehsanolah & Biglari, Mojtaba, 2015. "A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural network and GSA evolutionary algorithm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1023-1037.
  18. Miguel A. Rodríguez-López & Luis M. López-González & Luis M. López-Ochoa & Jesús Las-Heras-Casas, 2018. "Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data," Energies, MDPI, vol. 11(4), pages 1-22, March.
  19. Xianming Dou & Yongguo Yang & Jinhui Luo, 2018. "Estimating Forest Carbon Fluxes Using Machine Learning Techniques Based on Eddy Covariance Measurements," Sustainability, MDPI, vol. 10(1), pages 1-26, January.
  20. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
  21. Rodrigues, Eugénio & Gomes, Álvaro & Gaspar, Adélio Rodrigues & Henggeler Antunes, Carlos, 2018. "Estimation of renewable energy and built environment-related variables using neural networks – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 959-988.
  22. Sudhakar Gantasala & Jean-Claude Luneno & Jan-Olov Aidanpää, 2017. "Investigating How an Artificial Neural Network Model Can Be Used to Detect Added Mass on a Non-Rotating Beam Using Its Natural Frequencies: A Possible Application for Wind Turbine Blade Ice Detection," Energies, MDPI, vol. 10(2), pages 1-21, February.
  23. Paulo Rotela Junior & Eugenio Fischetti & Victor G. Araújo & Rogério S. Peruchi & Giancarlo Aquila & Luiz Célio S. Rocha & Liviam S. Lacerda, 2019. "Wind Power Economic Feasibility under Uncertainty and the Application of ANN in Sensitivity Analysis," Energies, MDPI, vol. 12(12), pages 1-10, June.
  24. Peng Sun & Jian Li & Junsheng Chen & Xiao Lei, 2016. "A Short-Term Outage Model of Wind Turbines with Doubly Fed Induction Generators Based on Supervisory Control and Data Acquisition Data," Energies, MDPI, vol. 9(11), pages 1-21, October.
  25. Abrar Ahmed Chhipa & Vinod Kumar & Raghuveer Raj Joshi & Prasun Chakrabarti & Michal Jasinski & Alessandro Burgio & Zbigniew Leonowicz & Elzbieta Jasinska & Rajkumar Soni & Tulika Chakrabarti, 2021. "Adaptive Neuro-Fuzzy Inference System-Based Maximum Power Tracking Controller for Variable Speed WECS," Energies, MDPI, vol. 14(19), pages 1-19, October.
  26. Kumar, Dipesh & Chatterjee, Kalyan, 2016. "A review of conventional and advanced MPPT algorithms for wind energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 957-970.
  27. Wang, Yifei & Ma, Xiandong & Joyce, Malcolm J., 2016. "Reducing sensor complexity for monitoring wind turbine performance using principal component analysis," Renewable Energy, Elsevier, vol. 97(C), pages 444-456.
  28. Alfredo Arcos Jiménez & Carlos Quiterio Gómez Muñoz & Fausto Pedro García Márquez, 2017. "Machine Learning for Wind Turbine Blades Maintenance Management," Energies, MDPI, vol. 11(1), pages 1-16, December.
  29. Fathabadi, Hassan, 2016. "Maximum mechanical power extraction from wind turbines using novel proposed high accuracy single-sensor-based maximum power point tracking technique," Energy, Elsevier, vol. 113(C), pages 1219-1230.
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