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Bayesian Network Modelling for the Wind Energy Industry: An Overview

Adedipe, Tosin, Shafiee, Mahmood, Zio, Enrico (2020) Bayesian Network Modelling for the Wind Energy Industry: An Overview. Reliability Engineering and System Safety, 202 . ISSN 0951-8320. (doi:10.1016/j.ress.2020.107053) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:81891)

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https://dx.doi.org/10.1016/j.ress.2020.107053

Abstract

Wind 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.

Item Type: Article
DOI/Identification number: 10.1016/j.ress.2020.107053
Uncontrolled keywords: Wind energy; Bayesian network (BN); Reliability; Probabilistic methods; Operation and maintenance (O&M); Fault diagnosis and prognosis; Structural analysis; Risk assessment
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA165 Engineering instruments, meters etc. Industrial instrumentation
T Technology > TA Engineering (General). Civil engineering (General) > TA401 Materials engineering and construction
T Technology > TJ Mechanical engineering and machinery
V Naval Science > VM Naval architecture. Shipbuilding. Marine engineering
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Depositing User: Mahmood Shafiee
Date Deposited: 27 Jun 2020 14:51 UTC
Last Modified: 29 Jun 2020 11:09 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/81891 (The current URI for this page, for reference purposes)
Shafiee, Mahmood: https://orcid.org/0000-0002-6122-5719
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