SCADA data for wind turbine data-driven condition/performance monitoring: A review on state-of-art, challenges and future trends

Simple item page
dc.contributor.authorPandit, Ravi
dc.contributor.authorAstolfi, Davide
dc.contributor.authorHong, Jiarong
dc.contributor.authorInfield, David
dc.contributor.authorSantos, Matilde
dc.date.accessioned2022-09-22T11:47:03Z
dc.date.available2022-09-22T11:47:03Z
dc.date.issued2022-09-19
dc.description.abstractThis paper reviews the recent advancement made in data-driven technologies based on SCADA data for improving wind turbines’ operation and maintenance activities (e.g. condition monitoring, decision support, critical components failure detections) and the challenges associated with them. Machine learning techniques applied to wind turbines’ operation and maintenance (O&M) are reviewed. The data sources, feature engineering and model selection (classification, regression) and validation are all used to categorise these data-driven models. Our findings suggest that (a) most models use 10-minute mean SCADA data, though the use of high-resolution data has shown greater advantages as compared to 10-minute mean value but comes with high computational challenges. (b) Most of SCADA data are confidential and not available in the public domain which slows down technological advancements. (c) These datasets are used for both, the classification and regression of wind turbines but are used in classification extensively. And, (d) most commonly used data-driven models are neural networks, support vector machines, probabilistic models and decision trees and each of these models has its own merits and demerits. We conclude the paper by discussing the potential areas where SCADA data-based data-driven methodologies could be used in future wind energy research.en_UK
dc.identifier.citationPandit R, Astolfi D, Hong J, et al., (2023) SCADA data for wind turbine data-driven condition/performance monitoring: A review on state-of-art, challenges and future trends, Wind Engineering, Volume 47, Issue 2, April 2023, pp. 422–441en_UK
dc.identifier.issn0309-524X
dc.identifier.urihttps://doi.org/10.1177/0309524X221124031
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18464
dc.language.isoenen_UK
dc.publisherSageen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectwind turbinesen_UK
dc.subjectSCADA dataen_UK
dc.subjectcondition monitoringen_UK
dc.subjectperformance monitoringen_UK
dc.titleSCADA data for wind turbine data-driven condition/performance monitoring: A review on state-of-art, challenges and future trendsen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Wind_turbine_data-driven_condition_performance_monitoring-2022.pdf
Size:
1.44 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)