Digital twin technology in wind turbine components: A review
The industrial development, the advances in sensor technology and the processing of large amounts of data, have enabled the training and testing of artificial intelligence models that reproduce, with high accuracy, the behavior of some variables of interest. With the consolidation of the big data er...
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Elsevier
2025-06-01
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| Series: | Intelligent Systems with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305325000614 |
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| author | Jersson X. Leon-Medina Diego A. Tibaduiza Núria Parés Francesc Pozo |
| author_facet | Jersson X. Leon-Medina Diego A. Tibaduiza Núria Parés Francesc Pozo |
| author_sort | Jersson X. Leon-Medina |
| collection | DOAJ |
| description | The industrial development, the advances in sensor technology and the processing of large amounts of data, have enabled the training and testing of artificial intelligence models that reproduce, with high accuracy, the behavior of some variables of interest. With the consolidation of the big data era and the proliferation of sensors that can acquire information directly from various components of a wind turbine (WT), a digital twin (DT) allows to close the gap between the physical and the digital worlds. It combines historical data, sensor readings, machine learning and physics-based modeling to replicate the behavior of the physical component accurately. This DT can simulate the performance and behavior of the physical object under different conditions and situations, allowing for predicting failures in WT components and determining their remaining useful life. This review describes the existing literature related to the use of DTs and their developments for WT applications and their components in onshore and offshore applications. This review explores various types of DTs and their approaches, aiming to cover different methods of data processing and concepts related to each DT framework. In addition, it identifies insights from various studies and reviews, particularly focusing on the components of WTs. |
| format | Article |
| id | doaj-art-2f1f00d83c2f4cdea8b6628c0ab5e68c |
| institution | OA Journals |
| issn | 2667-3053 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj-art-2f1f00d83c2f4cdea8b6628c0ab5e68c2025-08-20T02:32:19ZengElsevierIntelligent Systems with Applications2667-30532025-06-012620053510.1016/j.iswa.2025.200535Digital twin technology in wind turbine components: A reviewJersson X. Leon-Medina0Diego A. Tibaduiza1Núria Parés2Francesc Pozo3Control, Data and Artificial Intelligence (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Campus Diagonal-Besòs (CDB), Universitat Politècnica de Catalunya (UPC), Eduard Maristany 16, 08019 Barcelona, Spain; Grupo de Investigación en Energía y Nuevas Tecnologías—GENTE, Escuela de Ingeniería Electromecánica, Universidad Pedagógica y Tecnológica de Colombia, Facultad Seccional Duitama, Carrera 18 con Calle 22, Duitama 150461, Boyacá, ColombiaUniversidad Nacional de Colombia sede Bogotá, Departamento de Ingeniería Eléctrica y Electrónica, Av 45 carrera 30, Bogota, ColombiaLaboratori de Càlcul Numèric (LaCàN), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Campus Diagonal-Besòs (CDB), Universitat Politècnica de Catalunya (UPC), Eduard Maristany 16, 08019 Barcelona, SpainControl, Data and Artificial Intelligence (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Campus Diagonal-Besòs (CDB), Universitat Politècnica de Catalunya (UPC), Eduard Maristany 16, 08019 Barcelona, Spain; Institute of Mathematics (IMTech), Universitat Politècnica de Catalunya (UPC), Pau Gargallo 14, 08028 Barcelona, Spain; Corresponding author at: Control, Data and Artificial Intelligence (CoDAlab), Department of Mathematics, Escola d’Enginyeria de Barcelona Est (EEBE), Campus Diagonal-Besòs (CDB), Universitat Politècnica de Catalunya (UPC), Eduard Maristany 16, 08019 Barcelona, Spain.The industrial development, the advances in sensor technology and the processing of large amounts of data, have enabled the training and testing of artificial intelligence models that reproduce, with high accuracy, the behavior of some variables of interest. With the consolidation of the big data era and the proliferation of sensors that can acquire information directly from various components of a wind turbine (WT), a digital twin (DT) allows to close the gap between the physical and the digital worlds. It combines historical data, sensor readings, machine learning and physics-based modeling to replicate the behavior of the physical component accurately. This DT can simulate the performance and behavior of the physical object under different conditions and situations, allowing for predicting failures in WT components and determining their remaining useful life. This review describes the existing literature related to the use of DTs and their developments for WT applications and their components in onshore and offshore applications. This review explores various types of DTs and their approaches, aiming to cover different methods of data processing and concepts related to each DT framework. In addition, it identifies insights from various studies and reviews, particularly focusing on the components of WTs.http://www.sciencedirect.com/science/article/pii/S2667305325000614digital twin (DT)wind turbine (WT)Remaining useful lifeMachine learningDeep learningCondition monitoring |
| spellingShingle | Jersson X. Leon-Medina Diego A. Tibaduiza Núria Parés Francesc Pozo Digital twin technology in wind turbine components: A review Intelligent Systems with Applications digital twin (DT) wind turbine (WT) Remaining useful life Machine learning Deep learning Condition monitoring |
| title | Digital twin technology in wind turbine components: A review |
| title_full | Digital twin technology in wind turbine components: A review |
| title_fullStr | Digital twin technology in wind turbine components: A review |
| title_full_unstemmed | Digital twin technology in wind turbine components: A review |
| title_short | Digital twin technology in wind turbine components: A review |
| title_sort | digital twin technology in wind turbine components a review |
| topic | digital twin (DT) wind turbine (WT) Remaining useful life Machine learning Deep learning Condition monitoring |
| url | http://www.sciencedirect.com/science/article/pii/S2667305325000614 |
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