Bayesian network modelling for the wind energy industry: an overview

Simple item page
dc.contributor.authorAdedipe, Tosin
dc.contributor.authorShafiee, Mahmood
dc.contributor.authorZio, Enrico
dc.date.accessioned2020-09-24T09:07:00Z
dc.date.available2020-09-24T09:07:00Z
dc.date.issued2020-05-29
dc.description.abstractWind energy farms are moving into deeper and more remote waters to benefit from availability of more space for the installation of wind turbines as well as higher wind speed for the production of electricity. Wind farm asset managers must ensure availability of adequate power supply as well as reliability of wind turbines throughout their lifetime. The environmental conditions in deep waters often change very rapidly, and therefore the performance metrics used in different life cycle phases of a wind energy project will need to be updated on a frequent basis so as to ensure that the wind energy systems operate at the highest reliability. For this reason, there is a crucial need for the wind energy industry to adopt advanced computational tools/techniques that are capable of modelling the risk scenarios in near real-time as well as providing a prompt response to any emergency situation. Bayesian network (BN) is a popular probabilistic method that can be used for system reliability modelling and decision-making under uncertainty. This paper provides a systematic review and evaluation of existing research on the use of BN models in the wind energy sector. To conduct this literature review, all relevant databases from inception to date were searched, and a total of 70 sources (including journal publications, conference proceedings, PhD dissertations, industry reports, best practice documents and software user guides) which met the inclusion criteria were identified. Our review findings reveal that the applications of BNs in the wind energy industry are quite diverse, ranging from wind power and weather forecasting to risk management, fault diagnosis and prognosis, structural analysis, reliability assessment, and maintenance planning and updating. Furthermore, a number of case studies are presented to illustrate the applicability of BNs in practice. Although the paper details information applicable to the wind energy industry, the knowledge gained can be transferred to many other sectors.en_UK
dc.identifier.citationAdedipe T, Shafiee M, Zio E. (2020) Bayesian network modelling for the wind energy industry: an overview. Reliability Engineering and System Safety, Volume 202, October 2020, Article number 107053en_UK
dc.identifier.issn0951-8320
dc.identifier.urihttps://doi.org/10.1016/j.ress.2020.107053
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/15843
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectWind energyen_UK
dc.subjectBayesian network (BN)en_UK
dc.subjectReliabilityen_UK
dc.subjectProbabilistic methodsen_UK
dc.subjectOperation and maintenance (O&M)en_UK
dc.subjectFault diagnosis and prognosisen_UK
dc.subjectStructural analysisen_UK
dc.subjectRisk assessmenten_UK
dc.titleBayesian network modelling for the wind energy industry: an overviewen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Bayesian_Network_Modelling-Wind_Energy-2020.pdf
Size:
866.05 KB
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 (SWEE)