IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i10p8333-d1151548.html
   My bibliography  Save this article

Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research

Author

Listed:
  • Han Peng

    (School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

  • Songyin Li

    (School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

  • Linjian Shangguan

    (School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

  • Yisa Fan

    (School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

  • Hai Zhang

    (School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

Abstract

Power generation from wind farms is growing rapidly around the world. In the past decade, wind energy has played an important role in contributing to sustainable development. However, wind turbines are extremely susceptible to component damage under complex environments and over long-term operational cycles, which directly affects their maintenance, reliability, and operating costs. It is crucial to realize efficient early warning of wind turbine failure to avoid equipment breakdown, to prolong the service life of wind turbines, and to maximize the revenue and efficiency of wind power projects. For this purpose, wind turbines are used as the research object. Firstly, this paper outlines the main components and failure mechanisms of wind turbines and analyzes the causes of equipment failure. Secondly, a brief analysis of the cost of wind power projects based on equipment failure is presented. Thirdly, the current key technologies for intelligent operation and maintenance (O&M) in the wind power industry are discussed, and the key research on decision support systems, fault diagnosis models, and life-cycle costs is presented. Finally, current challenges and future development directions are summarized.

Suggested Citation

  • Han Peng & Songyin Li & Linjian Shangguan & Yisa Fan & Hai Zhang, 2023. "Analysis of Wind Turbine Equipment Failure and Intelligent Operation and Maintenance Research," Sustainability, MDPI, vol. 15(10), pages 1-35, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8333-:d:1151548
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/10/8333/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/10/8333/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bosch, Jonathan & Staffell, Iain & Hawkes, Adam D., 2019. "Global levelised cost of electricity from offshore wind," Energy, Elsevier, vol. 189(C).
    2. Zhou, Shan & Yang, Pu, 2020. "Risk management in distributed wind energy implementing Analytic Hierarchy Process," Renewable Energy, Elsevier, vol. 150(C), pages 616-623.
    3. Wang, Anqi & Qian, Zheng & Pei, Yan & Jing, Bo, 2022. "A de-ambiguous condition monitoring scheme for wind turbines using least squares generative adversarial networks," Renewable Energy, Elsevier, vol. 185(C), pages 267-279.
    4. Valery Okulov & Ivan Kabardin & Dmitry Mukhin & Konstantin Stepanov & Nastasia Okulova, 2021. "Physical De-Icing Techniques for Wind Turbine Blades," Energies, MDPI, vol. 14(20), pages 1-16, October.
    5. Jianjun Qin & Michael Havbro Faber, 2019. "Resilience Informed Integrity Management of Wind Turbine Parks," Energies, MDPI, vol. 12(14), pages 1-19, July.
    6. Giovanni Rinaldi & Philipp R. Thies & Lars Johanning, 2021. "Current Status and Future Trends in the Operation and Maintenance of Offshore Wind Turbines: A Review," Energies, MDPI, vol. 14(9), pages 1-28, April.
    7. Paweł Pijarski & Piotr Kacejko & Piotr Miller, 2023. "Advanced Optimisation and Forecasting Methods in Power Engineering—Introduction to the Special Issue," Energies, MDPI, vol. 16(6), pages 1-20, March.
    8. Sunila, Kanerva & Bergaentzlé, Claire & Martin, Bénédicte & Ekroos, Ari, 2019. "A supra-national TSO to enhance offshore wind power development in the Baltic Sea? A legal and regulatory analysis," Energy Policy, Elsevier, vol. 128(C), pages 775-782.
    9. Luz, Thiago & Moura, Pedro, 2019. "100% Renewable energy planning with complementarity and flexibility based on a multi-objective assessment," Applied Energy, Elsevier, vol. 255(C).
    10. Ravi Kumar Pandit & Davide Astolfi & Isidro Durazo Cardenas, 2023. "A Review of Predictive Techniques Used to Support Decision Making for Maintenance Operations of Wind Turbines," Energies, MDPI, vol. 16(4), pages 1-17, February.
    11. Song, Dongran & Fan, Xinyu & Yang, Jian & Liu, Anfeng & Chen, Sifan & Joo, Young Hoon, 2018. "Power extraction efficiency optimization of horizontal-axis wind turbines through optimizing control parameters of yaw control systems using an intelligent method," Applied Energy, Elsevier, vol. 224(C), pages 267-279.
    12. Peng Guo & Jian Fu & XiYun Yang, 2018. "Condition Monitoring and Fault Diagnosis of Wind Turbines Gearbox Bearing Temperature Based on Kolmogorov-Smirnov Test and Convolutional Neural Network Model," Energies, MDPI, vol. 11(9), pages 1-16, August.
    13. Liwei Ju & Peng Li & Qinliang Tan & Zhongfu Tan & GejiriFu De, 2018. "A CVaR-Robust Risk Aversion Scheduling Model for Virtual Power Plants Connected with Wind-Photovoltaic-Hydropower-Energy Storage Systems, Conventional Gas Turbines and Incentive-Based Demand Responses," Energies, MDPI, vol. 11(11), pages 1-28, October.
    14. 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.
    15. 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.
    16. C, O. Mauricio Hernandez & Shadman, Milad & Amiri, Mojtaba Maali & Silva, Corbiniano & Estefen, Segen F. & La Rovere, Emilio, 2021. "Environmental impacts of offshore wind installation, operation and maintenance, and decommissioning activities: A case study of Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    17. Asier González-González & Ismael Etxeberria-Agiriano & Ekaitz Zulueta & Fernando Oterino-Echavarri & Jose Manuel Lopez-Guede, 2014. "Pitch Based Wind Turbine Intelligent Speed Setpoint Adjustment Algorithms," Energies, MDPI, vol. 7(6), pages 1-17, June.
    18. Dai, Xiaoran & Liu, Guo-Ping & Hu, Wenshan, 2023. "An online-learning-enabled self-attention-based model for ultra-short-term wind power forecasting," Energy, Elsevier, vol. 272(C).
    19. Wu, Xiaoni & Hu, Yu & Li, Ye & Yang, Jian & Duan, Lei & Wang, Tongguang & Adcock, Thomas & Jiang, Zhiyu & Gao, Zhen & Lin, Zhiliang & Borthwick, Alistair & Liao, Shijun, 2019. "Foundations of offshore wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 379-393.
    20. Szumilas-Kowalczyk, H. & Pevzner, N. & Giedych, R., 2020. "Long-term visual impacts of aging infrastructure: Challenges of decommissioning wind power infrastructure and a survey of alternative strategies," Renewable Energy, Elsevier, vol. 150(C), pages 550-560.
    21. Lubov Petrichenko & Antans Sauhats & Illia Diahovchenko & Irina Segeda, 2022. "Economic Viability of Energy Communities versus Distributed Prosumers," Sustainability, MDPI, vol. 14(8), pages 1-24, April.
    22. M. A. Parvez Mahmud & Nazmul Huda & Shahjadi Hisan Farjana & Candace Lang, 2018. "Environmental Impacts of Solar-Photovoltaic and Solar-Thermal Systems with Life-Cycle Assessment," Energies, MDPI, vol. 11(9), pages 1-21, September.
    23. Pablo Benalcazar & Adam Suski & Jacek Kamiński, 2020. "Optimal Sizing and Scheduling of Hybrid Energy Systems: The Cases of Morona Santiago and the Galapagos Islands," Energies, MDPI, vol. 13(15), pages 1-20, August.
    24. Lin, Zi & Cevasco, Debora & Collu, Maurizio, 2020. "A methodology to develop reduced-order models to support the operation and maintenance of offshore wind turbines," Applied Energy, Elsevier, vol. 259(C).
    25. Xiaowen Song & Zhitai Xing & Yan Jia & Xiaojuan Song & Chang Cai & Yinan Zhang & Zekun Wang & Jicai Guo & Qingan Li, 2022. "Review on the Damage and Fault Diagnosis of Wind Turbine Blades in the Germination Stage," Energies, MDPI, vol. 15(20), pages 1-17, October.
    26. Arcos Jiménez, Alfredo & Zhang, Long & Gómez Muñoz, Carlos Quiterio & García Márquez, Fausto Pedro, 2020. "Maintenance management based on Machine Learning and nonlinear features in wind turbines," Renewable Energy, Elsevier, vol. 146(C), pages 316-328.
    27. Niu, Zhewen & Yu, Zeyuan & Tang, Wenhu & Wu, Qinghua & Reformat, Marek, 2020. "Wind power forecasting using attention-based gated recurrent unit network," Energy, Elsevier, vol. 196(C).
    28. Afef Fekih & Hamed Habibi & Silvio Simani, 2022. "Fault Diagnosis and Fault Tolerant Control of Wind Turbines: An Overview," Energies, MDPI, vol. 15(19), pages 1-21, September.
    29. Aitor Saenz-Aguirre & Ekaitz Zulueta & Unai Fernandez-Gamiz & Javier Lozano & Jose Manuel Lopez-Guede, 2019. "Artificial Neural Network Based Reinforcement Learning for Wind Turbine Yaw Control," Energies, MDPI, vol. 12(3), pages 1-17, January.
    30. Davide Astolfi & Francesco Castellani & Ludovico Terzi, 2018. "Wind Turbine Power Curve Upgrades," Energies, MDPI, vol. 11(5), pages 1-17, May.
    31. Ali M. H. A. Khajah & Simon P. Philbin, 2022. "Techno-Economic Analysis and Modelling of the Feasibility of Wind Energy in Kuwait," Clean Technol., MDPI, vol. 4(1), pages 1-21, January.
    32. Ernesto E. Empig & Ahmet Sivacioğlu & Renato S. Pacaldo & Peter D. Suson & Rabby Q. Lavilles & Maria Rizalia Y. Teves & Maria Cecilia M. Ferolin & Ruben F. Amparado, 2023. "Climate Change, Sustainable Forest Management, ICT Nexus, and the SDG 2030: A Systems Thinking Approach," Sustainability, MDPI, vol. 15(8), pages 1-26, April.
    33. Pinjia Zhang & Delong Lu, 2019. "A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines," Energies, MDPI, vol. 12(14), pages 1-22, July.
    34. Unai Fernandez-Gamiz & Ekaitz Zulueta & Ana Boyano & Igor Ansoategui & Irantzu Uriarte, 2017. "Five Megawatt Wind Turbine Power Output Improvements by Passive Flow Control Devices," Energies, MDPI, vol. 10(6), pages 1-15, May.
    35. Arezoo Ghazanfari, 2023. "An Analysis of Circular Economy Literature at the Macro Level, with a Particular Focus on Energy Markets," Energies, MDPI, vol. 16(4), pages 1-24, February.
    36. Li, Dan & Jiang, Fuxin & Chen, Min & Qian, Tao, 2022. "Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks," Energy, Elsevier, vol. 238(PC).
    37. Rubert, T. & McMillan, D. & Niewczas, P., 2018. "A decision support tool to assist with lifetime extension of wind turbines," Renewable Energy, Elsevier, vol. 120(C), pages 423-433.
    38. Jorge Maldonado-Correa & Sergio Martín-Martínez & Estefanía Artigao & Emilio Gómez-Lázaro, 2020. "Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-21, June.
    39. Wim Munters & Johan Meyers, 2018. "Dynamic Strategies for Yaw and Induction Control of Wind Farms Based on Large-Eddy Simulation and Optimization," Energies, MDPI, vol. 11(1), pages 1-32, January.
    40. Yuewei Liu & Shenghui Zhang & Xuejun Chen & Jianzhou Wang, 2018. "Artificial Combined Model Based on Hybrid Nonlinear Neural Network Models and Statistics Linear Models—Research and Application for Wind Speed Forecasting," Sustainability, MDPI, vol. 10(12), pages 1-30, December.
    41. Hameed, Z. & Vatn, J. & Heggset, J., 2011. "Challenges in the reliability and maintainability data collection for offshore wind turbines," Renewable Energy, Elsevier, vol. 36(8), pages 2154-2165.
    42. Kevin Leahy & Colm Gallagher & Peter O’Donovan & Ken Bruton & Dominic T. J. O’Sullivan, 2018. "A Robust Prescriptive Framework and Performance Metric for Diagnosing and Predicting Wind Turbine Faults Based on SCADA and Alarms Data with Case Study," Energies, MDPI, vol. 11(7), pages 1-21, July.
    43. Bhardwaj, U. & Teixeira, A.P. & Soares, C. Guedes, 2019. "Reliability prediction of an offshore wind turbine gearbox," Renewable Energy, Elsevier, vol. 141(C), pages 693-706.
    44. Md Zishan Akhter & Farag Khalifa Omar, 2021. "Review of Flow-Control Devices for Wind-Turbine Performance Enhancement," Energies, MDPI, vol. 14(5), pages 1-35, February.
    45. Beagle, E. & Belmont, E., 2019. "Comparative life cycle assessment of biomass utilization for electricity generation in the European Union and the United States," Energy Policy, Elsevier, vol. 128(C), pages 267-275.
    46. Nabil El Bazi & Mustapha Mabrouki & Oussama Laayati & Nada Ouhabi & Hicham El Hadraoui & Fatima-Ezzahra Hammouch & Ahmed Chebak, 2023. "Generic Multi-Layered Digital-Twin-Framework-Enabled Asset Lifecycle Management for the Sustainable Mining Industry," Sustainability, MDPI, vol. 15(4), pages 1-24, February.
    47. Xueliang Yuan & Leping Chen & Xuerou Sheng & Mengyue Liu & Yue Xu & Yuzhou Tang & Qingsong Wang & Qiao Ma & Jian Zuo, 2021. "Life Cycle Cost of Electricity Production: A Comparative Study of Coal-Fired, Biomass, and Wind Power in China," Energies, MDPI, vol. 14(12), pages 1-15, June.
    48. Iulia Stamatescu & Nicoleta Arghira & Ioana Făgărăşan & Grigore Stamatescu & Sergiu Stelian Iliescu & Vasile Calofir, 2017. "Decision Support System for a Low Voltage Renewable Energy System," Energies, MDPI, vol. 10(1), pages 1-15, January.
    49. Evgeny V. Solomin & Gleb N. Ryavkin, 2023. "Horizontal Axis Wind Turbine Weather Vane Aerodynamic Characteristics: Delayed Detached Eddy Simulation and Experimental Approach," Mathematics, MDPI, vol. 11(8), pages 1-16, April.
    50. Garcia-Teruel, Anna & Rinaldi, Giovanni & Thies, Philipp R. & Johanning, Lars & Jeffrey, Henry, 2022. "Life cycle assessment of floating offshore wind farms: An evaluation of operation and maintenance," Applied Energy, Elsevier, vol. 307(C).
    51. Lledó, Ll. & Torralba, V. & Soret, A. & Ramon, J. & Doblas-Reyes, F.J., 2019. "Seasonal forecasts of wind power generation," Renewable Energy, Elsevier, vol. 143(C), pages 91-100.
    52. Conor McKinnon & Alan Turnbull & Sofia Koukoura & James Carroll & Alasdair McDonald, 2020. "Effect of Time History on Normal Behaviour Modelling Using SCADA Data to Predict Wind Turbine Failures," Energies, MDPI, vol. 13(18), pages 1-19, September.
    53. Malte Jansen & Iain Staffell & Lena Kitzing & Sylvain Quoilin & Edwin Wiggelinkhuizen & Bernard Bulder & Iegor Riepin & Felix Müsgens, 2020. "Offshore wind competitiveness in mature markets without subsidy," Nature Energy, Nature, vol. 5(8), pages 614-622, August.
    54. Li, Weiwei & Qian, Tong & Zhang, Yin & Shen, Yueqing & Wu, Chenghu & Tang, Wenhu, 2023. "Distributionally robust chance-constrained planning for regional integrated electricity–heat systems with data centers considering wind power uncertainty," Applied Energy, Elsevier, vol. 336(C).
    55. Annalisa Santolamazza & Daniele Dadi & Vito Introna, 2021. "A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks," Energies, MDPI, vol. 14(7), pages 1-25, March.
    56. Khazaei, Sahra & Ehsan, Mehdi & Soleymani, Soodabeh & Mohammadnezhad-Shourkaei, Hosein, 2022. "A high-accuracy hybrid method for short-term wind power forecasting," Energy, Elsevier, vol. 238(PC).
    57. Zhang, Yu & Li, Yanting & Zhang, Guangyao, 2020. "Short-term wind power forecasting approach based on Seq2Seq model using NWP data," Energy, Elsevier, vol. 213(C).
    58. Suyan Zhao & Xiaopai Su & Jiahui Li & Guibin Suo & Xiaoxuan Meng, 2023. "Research on Wind Power Project Risk Management Based on Structural Equation and Catastrophe Theory," Sustainability, MDPI, vol. 15(8), pages 1-17, April.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Yanfei Liu & Wentao Wang & Wenjun Wang & Chengbo Yu & Bowen Mao & Dongfang Shang & Yucong Duan, 2023. "Purpose-Driven Evaluation of Operation and Maintenance Efficiency and Safety Based on DIKWP," Sustainability, MDPI, vol. 15(17), pages 1-22, August.
    2. Rogerio Adriano da Fonseca Santiago & Natasha Benjamim Barbosa & Henrique Gomes Mergulhão & Tassio Farias de Carvalho & Alex Alisson Bandeira Santos & Ricardo Cerqueira Medrado & Jose Bione de Melo Fi, 2024. "Data-Driven Models Applied to Predictive and Prescriptive Maintenance of Wind Turbine: A Systematic Review of Approaches Based on Failure Detection, Diagnosis, and Prognosis," Energies, MDPI, vol. 17(5), pages 1-24, February.
    3. Weiwu Feng & Da Yang & Wenxue Du & Qiang Li, 2023. "In Situ Structural Health Monitoring of Full-Scale Wind Turbine Blades in Operation Based on Stereo Digital Image Correlation," Sustainability, MDPI, vol. 15(18), pages 1-17, September.
    4. Xing Su & Xudong Wang & Wanli Xu & Liqian Yuan & Chunhua Xiong & Jinmao Chen, 2024. "Offshore Wind Power: Progress of the Edge Tool, Which Can Promote Sustainable Energy Development," Sustainability, MDPI, vol. 16(17), pages 1-22, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dai, Xiaoran & Liu, Guo-Ping & Hu, Wenshan, 2023. "An online-learning-enabled self-attention-based model for ultra-short-term wind power forecasting," Energy, Elsevier, vol. 272(C).
    2. Aitor Saenz-Aguirre & Ekaitz Zulueta & Unai Fernandez-Gamiz & Javier Lozano & Jose Manuel Lopez-Guede, 2019. "Artificial Neural Network Based Reinforcement Learning for Wind Turbine Yaw Control," Energies, MDPI, vol. 12(3), pages 1-17, January.
    3. Ren, Zhengru & Verma, Amrit Shankar & Li, Ye & Teuwen, Julie J.E. & Jiang, Zhiyu, 2021. "Offshore wind turbine operations and maintenance: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    4. Davide Astolfi & Raymond Byrne & Francesco Castellani, 2020. "Analysis of Wind Turbine Aging through Operation Curves," Energies, MDPI, vol. 13(21), pages 1-21, October.
    5. Mohamed Benbouzid & Tarek Berghout & Nur Sarma & Siniša Djurović & Yueqi Wu & Xiandong Ma, 2021. "Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review," Energies, MDPI, vol. 14(18), pages 1-33, September.
    6. Amira Elkodama & Amr Ismaiel & A. Abdellatif & S. Shaaban & Shigeo Yoshida & Mostafa A. Rushdi, 2023. "Control Methods for Horizontal Axis Wind Turbines (HAWT): State-of-the-Art Review," Energies, MDPI, vol. 16(17), pages 1-32, September.
    7. Lv, Sheng-Xiang & Wang, Lin, 2022. "Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization," Applied Energy, Elsevier, vol. 311(C).
    8. Urmeneta, Jon & Izquierdo, Juan & Leturiondo, Urko, 2023. "A methodology for performance assessment at system level—Identification of operating regimes and anomaly detection in wind turbines," Renewable Energy, Elsevier, vol. 205(C), pages 281-292.
    9. Jorge Maldonado-Correa & Sergio Martín-Martínez & Estefanía Artigao & Emilio Gómez-Lázaro, 2020. "Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review," Energies, MDPI, vol. 13(12), pages 1-21, June.
    10. Al-qaness, Mohammed A.A. & Ewees, Ahmed A. & Fan, Hong & Abualigah, Laith & Elaziz, Mohamed Abd, 2022. "Boosted ANFIS model using augmented marine predator algorithm with mutation operators for wind power forecasting," Applied Energy, Elsevier, vol. 314(C).
    11. Xiaodong Wang & Zhaoliang Ye & Shun Kang & Hui Hu, 2019. "Investigations on the Unsteady Aerodynamic Characteristics of a Horizontal-Axis Wind Turbine during Dynamic Yaw Processes," Energies, MDPI, vol. 12(16), pages 1-23, August.
    12. Davide Astolfi & Francesco Castellani, 2019. "Wind Turbine Power Curve Upgrades: Part II," Energies, MDPI, vol. 12(8), pages 1-20, April.
    13. Davide Astolfi & Francesco Castellani & Matteo Becchetti & Andrea Lombardi & Ludovico Terzi, 2020. "Wind Turbine Systematic Yaw Error: Operation Data Analysis Techniques for Detecting It and Assessing Its Performance Impact," Energies, MDPI, vol. 13(9), pages 1-17, May.
    14. Cheng Yang & Jun Jia & Ke He & Liang Xue & Chao Jiang & Shuangyu Liu & Bochao Zhao & Ming Wu & Haoyang Cui, 2023. "Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey," Energies, MDPI, vol. 16(14), pages 1-39, July.
    15. Mahmoud G. Hemeida & Ashraf M. Hemeida & Tomonobu Senjyu & Dina Osheba, 2022. "Renewable Energy Resources Technologies and Life Cycle Assessment: Review," Energies, MDPI, vol. 15(24), pages 1-36, December.
    16. Rebecca J. Barthelmie & Kaitlyn E. Dantuono & Emma J. Renner & Frederick L. Letson & Sara C. Pryor, 2021. "Extreme Wind and Waves in U.S. East Coast Offshore Wind Energy Lease Areas," Energies, MDPI, vol. 14(4), pages 1-25, February.
    17. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2024. "Wind power forecasting: A temporal domain generalization approach incorporating hybrid model and adversarial relationship-based training," Applied Energy, Elsevier, vol. 355(C).
    18. Camila Correa-Jullian & Sergio Cofre-Martel & Gabriel San Martin & Enrique Lopez Droguett & Gustavo de Novaes Pires Leite & Alexandre Costa, 2022. "Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection," Energies, MDPI, vol. 15(8), pages 1-29, April.
    19. Wang, Anqi & Pei, Yan & Qian, Zheng & Zareipour, Hamidreza & Jing, Bo & An, Jiayi, 2022. "A two-stage anomaly decomposition scheme based on multi-variable correlation extraction for wind turbine fault detection and identification," Applied Energy, Elsevier, vol. 321(C).
    20. Wang, Xiaodi & Hao, Yan & Yang, Wendong, 2024. "Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy," Energy, Elsevier, vol. 297(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8333-:d:1151548. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.