IDEAS home Printed from https://ideas.repec.org/r/eee/appene/v280y2020ics0306261920313519.html
   My bibliography  Save this item

Wind turbine power modelling and optimization using artificial neural network with wind field experimental data

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Yang, Kun & Deng, Xiaowei & Ti, Zilong & Yang, Shanghui & Huang, Senbin & Wang, Yuhang, 2023. "A data-driven layout optimization framework of large-scale wind farms based on machine learning," Renewable Energy, Elsevier, vol. 218(C).
  2. Jong-Beom Seo & Hosaeng Lee & Sang-Jo Han, 2023. "A Design Optimization of Organic Rankine Cycle Turbine Blades with Radial Basis Neural Network," Energies, MDPI, vol. 17(1), pages 1-18, December.
  3. Mohamed Abd-El-Hakeem Mohamed & Hossam Seddik Abbas & Mokhtar Shouran & Salah Kamel, 2022. "A Neuro-Predictive Controller Scheme for Integration of a Basic Wind Energy Generation Unit with an Electrical Power System," Energies, MDPI, vol. 15(16), pages 1-24, August.
  4. Cheng, Biyi & Yao, Yingxue, 2023. "Machine learning based surrogate model to analyze wind tunnel experiment data of Darrieus wind turbines," Energy, Elsevier, vol. 278(PA).
  5. Cheng, Biyi & Du, Jianjun & Yao, Yingxue, 2022. "Machine learning methods to assist structure design and optimization of Dual Darrieus Wind Turbines," Energy, Elsevier, vol. 244(PA).
  6. Amir Abdul Majid, 2023. "A Novel Method of Forecasting Chaotic and Random Wind Speed Regimes Based on Machine Learning with the Evolution and Prediction of Volterra Kernels," Energies, MDPI, vol. 16(12), pages 1-14, June.
  7. Khani, Mohammad Sadegh & Shahsavani, Younes & Mehraein, Mojtaba & Kisi, Ozgur, 2023. "Performance evaluation of the savonius hydrokinetic turbine using soft computing techniques," Renewable Energy, Elsevier, vol. 215(C).
  8. José Genaro González-Hernández & Rubén Salas-Cabrera, 2022. "Duty Cycle-Rotor Angular Speed Reverse Acting Relationship Steady State Analysis Based on a PMSG d–q Transform Modeling," Mathematics, MDPI, vol. 10(5), pages 1-17, February.
  9. Zhu, Dongping & Huang, Xiaogang & Ding, Zhixia & Zhang, Wei, 2024. "Estimation of wind turbine responses with attention-based neural network incorporating environmental uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
  10. He, Ruiyang & Yang, Hongxing & Sun, Haiying & Gao, Xiaoxia, 2021. "A novel three-dimensional wake model based on anisotropic Gaussian distribution for wind turbine wakes," Applied Energy, Elsevier, vol. 296(C).
  11. Jiang, Chiju & Zhang, Weihao & Li, Ya & Li, Lele & Wang, Yufan & Huang, Dongming, 2023. "Multi-scale Pix2Pix network for high-fidelity prediction of adiabatic cooling effectiveness in turbine cascade," Energy, Elsevier, vol. 265(C).
  12. Yeo, Eng Jet & Kennedy, David M. & O'Rourke, Fergal, 2022. "Tidal current turbine blade optimisation with improved blade element momentum theory and a non-dominated sorting genetic algorithm," Energy, Elsevier, vol. 250(C).
  13. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Tan, Yong & Rao, Lei, 2022. "Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning," Renewable Energy, Elsevier, vol. 189(C), pages 90-103.
  14. Xing, Zuoxia & Chen, Mingyang & Cui, Jia & Chen, Zhe & Xu, Jian, 2022. "Detection of magnitude and position of rotor aerodynamic imbalance of wind turbines using Convolutional Neural Network," Renewable Energy, Elsevier, vol. 197(C), pages 1020-1033.
  15. Tavakol Aghaei, Vahid & Ağababaoğlu, Arda & Bawo, Biram & Naseradinmousavi, Peiman & Yıldırım, Sinan & Yeşilyurt, Serhat & Onat, Ahmet, 2023. "Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm," Applied Energy, Elsevier, vol. 341(C).
  16. Shu, Tong & Song, Dongran & Hoon Joo, Young, 2022. "Decentralised optimisation for large offshore wind farms using a sparsified wake directed graph," Applied Energy, Elsevier, vol. 306(PA).
  17. Li, Rui & Zhang, Jincheng & Zhao, Xiaowei, 2022. "Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data," Energy, Elsevier, vol. 258(C).
  18. Purohit, Shantanu & Ng, E.Y.K. & Syed Ahmed Kabir, Ijaz Fazil, 2022. "Evaluation of three potential machine learning algorithms for predicting the velocity and turbulence intensity of a wind turbine wake," Renewable Energy, Elsevier, vol. 184(C), pages 405-420.
  19. Long, Huan & Xu, Shaohui & Gu, Wei, 2022. "An abnormal wind turbine data cleaning algorithm based on color space conversion and image feature detection," Applied Energy, Elsevier, vol. 311(C).
  20. Raúl R. Delgado-Currín & Williams R. Calderón-Muñoz & J. C. Elicer-Cortés, 2024. "Artificial Neural Network Model for Estimating the Pelton Turbine Shaft Power of a Micro-Hydropower Plant under Different Operating Conditions," Energies, MDPI, vol. 17(14), pages 1-14, July.
  21. Du, Qiuwan & Yang, Like & Li, Liangliang & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Aerodynamic design and optimization of blade end wall profile of turbomachinery based on series convolutional neural network," Energy, Elsevier, vol. 244(PA).
  22. Wang, Chuyao & Ji, Jie & Yu, Bendong & Xu, Lijie & Wang, Qiliang & Tian, Xinyi, 2022. "Investigation on the operation strategy of a hybrid BIPV/T façade in plateau areas: An adaptive regulation method based on artificial neural network," Energy, Elsevier, vol. 239(PA).
  23. Xiaoxun, Zhu & Zixu, Xu & Yu, Wang & Xiaoxia, Gao & Xinyu, Hang & Hongkun, Lu & Ruizhang, Liu & Yao, Chen & Huaxin, Liu, 2023. "Research on wind speed behavior prediction method based on multi-feature and multi-scale integrated learning," Energy, Elsevier, vol. 263(PA).
  24. Du, Qiuwan & Li, Yunzhu & Yang, Like & Liu, Tianyuan & Zhang, Di & Xie, Yonghui, 2022. "Performance prediction and design optimization of turbine blade profile with deep learning method," Energy, Elsevier, vol. 254(PA).
  25. Zou, Runmin & Yang, Jiaxin & Wang, Yun & Liu, Fang & Essaaidi, Mohamed & Srinivasan, Dipti, 2021. "Wind turbine power curve modeling using an asymmetric error characteristic-based loss function and a hybrid intelligent optimizer," Applied Energy, Elsevier, vol. 304(C).
  26. Zhang, Juntao & Cheng, Chuntian & Yu, Shen, 2024. "Recognizing the mapping relationship between wind power output and meteorological information at a province level by coupling GIS and CNN technologies," Applied Energy, Elsevier, vol. 360(C).
  27. Sun, Haiying & Yang, Hongxing & Gao, Xiaoxia, 2023. "Investigation into wind turbine wake effect on complex terrain," Energy, Elsevier, vol. 269(C).
  28. James Roetzer & Xingjie Li & John Hall, 2024. "Review of Data-Driven Models in Wind Energy: Demonstration of Blade Twist Optimization Based on Aerodynamic Loads," Energies, MDPI, vol. 17(16), pages 1-20, August.
  29. Amiri, Mojtaba Maali & Shadman, Milad & Estefen, Segen F., 2024. "A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
  30. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2022. "In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
  31. Wang, Longyan & Luo, Wei & Xu, Jian & Xie, Junhang & Luo, Zhaohui & Tan, Andy C.C., 2022. "Comparative study of decentralized instantaneous and wind-interval-based controls for in-line two scale wind turbines," Renewable Energy, Elsevier, vol. 189(C), pages 1218-1233.
  32. Dong, Zhikun & Chen, Yaoran & Zhou, Dai & Su, Jie & Han, Zhaolong & Cao, Yong & Bao, Yan & Zhao, Feng & Wang, Rui & Zhao, Yongsheng & Xu, Yuwang, 2022. "The mean wake model and its novel characteristic parameter of H-rotor VAWTs based on random forest method," Energy, Elsevier, vol. 239(PE).
  33. Xinghua Tao & Nan Mo & Jianbo Qin & Xiaozhe Yang & Linfei Yin & Likun Hu, 2023. "Parallel Multi-Layer Monte Carlo Optimization Algorithm for Doubly Fed Induction Generator Controller Parameters Optimization," Energies, MDPI, vol. 16(19), pages 1-20, October.
  34. Imre Delgado & Muhammad Fahim, 2020. "Wind Turbine Data Analysis and LSTM-Based Prediction in SCADA System," Energies, MDPI, vol. 14(1), pages 1-21, December.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.