Enhancing Reliability in Floating Offshore Wind Turbines through Digital Twin Technology: A Comprehensive Review

Abstract

This comprehensive review explores the application and impact of digital twin (DT) technology in bolstering the reliability of Floating Offshore Wind Turbines (FOWTs) and their supporting platforms. Within the burgeoning domain of offshore wind energy, this study contextualises the need for heightened reliability measures in FOWTs and elucidates how DT technology serves as a transformative tool to address these concerns. Analysing the existing scholarly literature, the review encompasses insights into the historical reliability landscape, DT deployment methodologies, and their influence on FOWT structures. Findings underscore the pivotal role of DT technology in enhancing FOWT reliability through real-time monitoring and predictive maintenance strategies, resulting in improved operational efficiency and reduced downtime. Highlighting the significance of DT technology as a potent mechanism for fortifying FOWT reliability, the review emphasises its potential to foster a robust operational framework while acknowledging the necessity for continued research to address technical intricacies and regulatory considerations in its integration within offshore wind energy systems. Challenges and opportunities related to the integration of DT technology in FOWTs are thoroughly analysed, providing valuable insights into the role of DTs in optimising FOWT reliability and performance, thereby offering a foundation for future research and industry implementation.

UBO-EREX: Uncertainty Bayesian-Optimized Extreme Recurrent EXpansion for Degradation Assessment of Wind Turbine Bearings

“…Let x r be the reduced feature matrix, where x is the original feature matrix and v ratio is the desired explained variance ratio. We can express the PCA reduction as follows by first computing the covariance matrix δ as in (4). After that, we perform a Singular Value Decomposition (SVD) σ on the covariance matrix δ, as in (5).…”

“…Wind turbines play a pivotal role in the renewable energy landscape, offering a sustainable solution to power generation [1,2]. However, ensuring the reliable operation of wind turbines is crucial for maximizing energy output and minimizing maintenance costs [3,4]. Among the various components of wind turbines, bearings are particularly susceptible to degradation, which can lead to costly downtimes and safety risks [5,6].…”

“…Data handling and processing: To address non-stationarity in the data, a synchronous resampling technique is employed, aligning vibration data to a consistent reference frame and improving spectral analysis reliability. 4.…”

Immersive Digital Twin under ISO 23247 Applied to Flexible Manufacturing Processes

“…One of the areas where DTs are used is in maintenance, where several investigations have been carried out. In [20][21][22], they present a DT to perform the predictive maintenance of a wind turbine and reliability in floating offshore wind turbines (FOWTs). The architecture presents a distributed DT with IIoT, machine learning (ML), MQTT, and fog computing to offer the better utilization and management of assets through real-time condition monitoring, predictive analysis, and the health management of selected wind turbine components in a wind farm.…”