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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Intelligent Systems with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305325000614 |
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| Summary: | 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. |
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| ISSN: | 2667-3053 |