IDEAS home Printed from https://ideas.repec.org/r/eee/renene/v34y2009i6p1487-1493.html
   My bibliography  Save this item

On-line monitoring of power curves

Citations

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


Cited by:

  1. Bi, Ran & Zhou, Chengke & Hepburn, Donald M., 2017. "Detection and classification of faults in pitch-regulated wind turbine generators using normal behaviour models based on performance curves," Renewable Energy, Elsevier, vol. 105(C), pages 674-688.
  2. Papatheou, Evangelos & Dervilis, Nikolaos & Maguire, Andrew E. & Campos, Carles & Antoniadou, Ifigeneia & Worden, Keith, 2017. "Performance monitoring of a wind turbine using extreme function theory," Renewable Energy, Elsevier, vol. 113(C), pages 1490-1502.
  3. Hsu-Hao Yang & Mei-Ling Huang & Shih-Wei Yang, 2015. "Integrating Auto-Associative Neural Networks with Hotelling T 2 Control Charts for Wind Turbine Fault Detection," Energies, MDPI, vol. 8(10), pages 1-16, October.
  4. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
  5. Jurasz, Jakub & Mikulik, Jerzy & Krzywda, Magdalena & Ciapała, Bartłomiej & Janowski, Mirosław, 2018. "Integrating a wind- and solar-powered hybrid to the power system by coupling it with a hydroelectric power station with pumping installation," Energy, Elsevier, vol. 144(C), pages 549-563.
  6. Hui Huang & Qiliang Zhu & Xueling Zhu & Jinhua Zhang, 2023. "An Adaptive, Data-Driven Stacking Ensemble Learning Framework for the Short-Term Forecasting of Renewable Energy Generation," Energies, MDPI, vol. 16(4), pages 1-20, February.
  7. Taslimi-Renani, Ehsan & Modiri-Delshad, Mostafa & Elias, Mohamad Fathi Mohamad & Rahim, Nasrudin Abd., 2016. "Development of an enhanced parametric model for wind turbine power curve," Applied Energy, Elsevier, vol. 177(C), pages 544-552.
  8. 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).
  9. Renken, Volker & Sorg, Michael & Marschner, Volker & Gerdes, Lewin & Gerdes, Gerhard & Fischer, Andreas, 2018. "Geographical comparison between wind power, solar power and demand for the German regions and data filling concepts," Renewable Energy, Elsevier, vol. 126(C), pages 475-484.
  10. Yang, Hsu-Hao & Huang, Mei-Ling & Lai, Chun-Mei & Jin, Jhih-Rong, 2018. "An approach combining data mining and control charts-based model for fault detection in wind turbines," Renewable Energy, Elsevier, vol. 115(C), pages 808-816.
  11. Chen, Jincheng & Wang, Feng & Stelson, Kim A., 2018. "A mathematical approach to minimizing the cost of energy for large utility wind turbines," Applied Energy, Elsevier, vol. 228(C), pages 1413-1422.
  12. Morshedizadeh, Majid & Kordestani, Mojtaba & Carriveau, Rupp & Ting, David S.-K. & Saif, Mehrdad, 2017. "Application of imputation techniques and Adaptive Neuro-Fuzzy Inference System to predict wind turbine power production," Energy, Elsevier, vol. 138(C), pages 394-404.
  13. John Thomas Lyons & Tuhfe Göçmen, 2021. "Applied Machine Learning Techniques for Performance Analysis in Large Wind Farms," Energies, MDPI, vol. 14(13), pages 1-28, June.
  14. Nasery, Praanjal & Aziz Ezzat, Ahmed, 2023. "Yaw-adjusted wind power curve modeling: A local regression approach," Renewable Energy, Elsevier, vol. 202(C), pages 1368-1376.
  15. Wakui, Tetsuya & Yokoyama, Ryohei, 2013. "Wind speed sensorless performance monitoring based on operating behavior for stand-alone vertical axis wind turbine," Renewable Energy, Elsevier, vol. 53(C), pages 49-59.
  16. 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.
  17. Seo, Seokho & Oh, Si-Doek & Kwak, Ho-Young, 2019. "Wind turbine power curve modeling using maximum likelihood estimation method," Renewable Energy, Elsevier, vol. 136(C), pages 1164-1169.
  18. Ye, Feng & Ezzat, Ahmed Aziz, 2024. "Icing detection and prediction for wind turbines using multivariate sensor data and machine learning," Renewable Energy, Elsevier, vol. 231(C).
  19. Lydia, M. & Suresh Kumar, S. & Immanuel Selvakumar, A. & Edwin Prem Kumar, G., 2015. "Wind resource estimation using wind speed and power curve models," Renewable Energy, Elsevier, vol. 83(C), pages 425-434.
  20. Antonio Colmenar-Santos & Severo Campíez-Romero & Lorenzo Alfredo Enríquez-Garcia & Clara Pérez-Molina, 2014. "Simplified Analysis of the Electric Power Losses for On-Shore Wind Farms Considering Weibull Distribution Parameters," Energies, MDPI, vol. 7(11), pages 1-30, October.
  21. 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.
  22. Shahriari, M. & Cervone, G. & Clemente-Harding, L. & Delle Monache, L., 2020. "Using the analog ensemble method as a proxy measurement for wind power predictability," Renewable Energy, Elsevier, vol. 146(C), pages 789-801.
  23. Mingzhe Zou & Sasa Z. Djokic, 2020. "A Review of Approaches for the Detection and Treatment of Outliers in Processing Wind Turbine and Wind Farm Measurements," Energies, MDPI, vol. 13(16), pages 1-30, August.
  24. Amer Al-Hinai & Yassine Charabi & Seyed H. Aghay Kaboli, 2021. "Offshore Wind Energy Resource Assessment across the Territory of Oman: A Spatial-Temporal Data Analysis," Sustainability, MDPI, vol. 13(5), pages 1-18, March.
  25. Carlos Adrián Hernández-Meléndez & Luis Alberto Rodríguez-Picón & Iván Juan Carlos Pérez-Olguín & Felipe Adrián Vázquez-Galvez & Jesús Israel Hernández-Hernández & Luis Carlos Méndez-González, 2024. "A Site-Specific Wind Energy Potential Analysis Based on Wind Probability Distributions: A Ciudad Juárez-México Case Study," Sustainability, MDPI, vol. 16(21), pages 1-22, October.
  26. 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.
  27. 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.
  28. 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).
  29. Zhang, Zijun & Kusiak, Andrew & Song, Zhe, 2013. "Scheduling electric power production at a wind farm," European Journal of Operational Research, Elsevier, vol. 224(1), pages 227-238.
  30. Marvuglia, Antonino & Messineo, Antonio, 2012. "Monitoring of wind farms’ power curves using machine learning techniques," Applied Energy, Elsevier, vol. 98(C), pages 574-583.
  31. 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.
  32. Shokrzadeh, Shahab & Jafari Jozani, Mohammad & Bibeau, Eric & Molinski, Tom, 2015. "A statistical algorithm for predicting the energy storage capacity for baseload wind power generation in the future electric grids," Energy, Elsevier, vol. 89(C), pages 793-802.
  33. 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.
  34. 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.
  35. Carlo Bianca, 2022. "On the Modeling of Energy-Multisource Networks by the Thermostatted Kinetic Theory Approach: A Review with Research Perspectives," Energies, MDPI, vol. 15(21), pages 1-22, October.
  36. Pradeep Singh & Krishan Arora & Umesh C. Rathore & Eunmok Yang & Gyanendra Prasad Joshi & Kwang Chul Son, 2023. "Performance Evaluation of Grid-Connected DFIG-Based WECS with Battery Energy Storage System under Wind Alterations Using FOPID Controller for RSC," Mathematics, MDPI, vol. 11(9), pages 1-29, April.
  37. Hu, Yang & Xi, Yunhua & Pan, Chenyang & Li, Gengda & Chen, Baowei, 2020. "Daily condition monitoring of grid-connected wind turbine via high-fidelity power curve and its comprehensive rating," Renewable Energy, Elsevier, vol. 146(C), pages 2095-2111.
  38. 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.
  39. 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.
  40. 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.
  41. Lydia, M. & Kumar, S. Suresh & Selvakumar, A. Immanuel & Prem Kumar, G. Edwin, 2014. "A comprehensive review on wind turbine power curve modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 452-460.
  42. Ouyang, Tinghui & Kusiak, Andrew & He, Yusen, 2017. "Modeling wind-turbine power curve: A data partitioning and mining approach," Renewable Energy, Elsevier, vol. 102(PA), pages 1-8.
  43. Athanasios Zisos & Georgia-Konstantina Sakki & Andreas Efstratiadis, 2023. "Mixing Renewable Energy with Pumped Hydropower Storage: Design Optimization under Uncertainty and Other Challenges," Sustainability, MDPI, vol. 15(18), pages 1-21, September.
  44. Giwhyun Lee & Yu Ding & Marc G. Genton & Le Xie, 2015. "Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 56-67, March.
  45. 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.
  46. Peyman Mazidi & Mian Du & Lina Bertling Tjernberg & Miguel A Sanz Bobi, 2017. "A health condition model for wind turbine monitoring through neural networks and proportional hazard models," Journal of Risk and Reliability, , vol. 231(5), pages 481-494, October.
  47. Tautz-Weinert, Jannis & Yürüşen, Nurseda Y. & Melero, Julio J. & Watson, Simon J., 2019. "Sensitivity study of a wind farm maintenance decision - A performance and revenue analysis," Renewable Energy, Elsevier, vol. 132(C), pages 93-105.
  48. Paula Medina Maçaira & Yasmin Monteiro Cyrillo & Fernando Luiz Cyrino Oliveira & Reinaldo Castro Souza, 2019. "Including Wind Power Generation in Brazil’s Long-Term Optimization Model for Energy Planning," Energies, MDPI, vol. 12(5), pages 1-20, March.
  49. Colak, Ilhami & Sagiroglu, Seref & Yesilbudak, Mehmet, 2012. "Data mining and wind power prediction: A literature review," Renewable Energy, Elsevier, vol. 46(C), pages 241-247.
  50. Meyer, Angela, 2021. "Multi-target normal behaviour models for wind farm condition monitoring," Applied Energy, Elsevier, vol. 300(C).
  51. Kusiak, Andrew & Zheng, Haiyang, 2010. "Optimization of wind turbine energy and power factor with an evolutionary computation algorithm," Energy, Elsevier, vol. 35(3), pages 1324-1332.
  52. Kusiak, Andrew & Li, Wenyan, 2011. "The prediction and diagnosis of wind turbine faults," Renewable Energy, Elsevier, vol. 36(1), pages 16-23.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.