Assessing offshore wind turbine reliability and availability

Lazakis, Iraklis; Kougioumtzoglou, Maria A.

doi:10.1177/1475090217735413

Cited by 7 publications

(4 citation statements)

References 27 publications

(46 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bayesian networks are frequently combined to improve their performance with ANN [114] or Machine Learning [33,113] on components such as the gearbox or the blades [200,201]; or to detect faults on WT state transitions [75,202,203]. Decision making using Bayesian analysis focuses on applying maintenance strategies that minimize O&M [204] and life-cycle costs [177,205]; assessing risk analysis considering failure modes [206], environmental variables [207] and inspection and repair costs [208]; and estimating information validity [209,210].…”

Section: Methodsmentioning

confidence: 99%

A Comprehensive Review of Artificial Intelligence and Wind Energy

Márquez

Gonzalo

2021

Arch Computat Methods Eng

View full text Add to dashboard Cite

Support of artificial intelligence, renewable energy and sustainability is currently increasing through the main policies of developed countries, e.g., the White Paper of the European Union. Wind energy is one of the most important renewable sources, growing in both onshore and offshore types. This paper studies the most remarkable artificial intelligence techniques employed in wind turbines monitoring systems. The principal techniques are analysed individually and together: Artificial Neural Networks; Fuzzy Logic; Genetic Algorithms; Particle Swarm Optimization; Decision Making Techniques; and Statistical Methods. The main applications for wind turbines maintenance management are also analysed, e.g., economic, farm location, non-destructive testing, environmental conditions, schedules, operator decisions, power production, remaining useful life, etc. Finally, the paper discusses the main findings of the literature in the conclusions.

show abstract

Section: Methodsmentioning

confidence: 99%

A Comprehensive Review of Artificial Intelligence and Wind Energy

Márquez

Gonzalo

2021

Arch Computat Methods Eng

View full text Add to dashboard Cite

show abstract

“…BNs can also be used as a decision-making tool for cost-benefit analysis as well as reliability analysis of repair/replacement actions on different wind turbine components. They can be used either as a standalone tool or in conjunction with other tools such as FTA (see Herp et al, 2018;Lazakis and Kougioumtzoglou, 2019;Reder and Melero, 2018). The application of Bayesian classification methods in wind turbine health state monitoring has also been studied in Song et al (2018).…”

Section: Reliability Assessmentmentioning

confidence: 99%

“…Even though BNs have the capacity to update, on the operations side, there has to be more efficient means of data collection and updating for the BN capacity to be maximised. (Lazakis and Kougioumtzoglou, 2019). The components that have been studied for reliability assessment in the reviewed literature include generator, blade and gearbox.…”

Section: Reliability Assessmentmentioning

confidence: 99%

Bayesian Network Modelling for the Wind Energy Industry: An Overview

Adedipe¹,

Shafiee

Zio³

2020

Reliability Engineering & System Safety

View full text Add to dashboard Cite

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 operating conditions in deep water environments often change very rapidly and, therefore the decision metrics used in different phases of a wind energy project's lifecycle will have to be updated on a very frequent basis, to guarantee higher wind energy system reliability levels. For this reason, there is a crucial need for the wind energy industry to develop advanced computational tools/techniques that are capable of modelling the possible scenarios in (near) real-time and provide a prompt response to any changes in operational/environmental conditions. Bayesian network (BN) is a popular machine learning (ML) method used for system 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, excluding references used in other sections of the text for discussion. 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, knowledge can be transferred to many other sectors.

show abstract

“…Several researchers have made tremendous progress in reliability modeling based on both qualitative and quantitative methods . Lazakis and Kougioumtzoglou applied the HAZID and FMEA, as well as the Bayesian Network in the risk analysis studies for an offshore wind turbine; the probability of failure as well as the annual cost with or without failure was clearly analyzed. The studies of Ozturk et al investigated the reliability of wind turbines considering different climatic conditions, as well as various wind turbine types (geared and direct drives).…”

Section: Literature Reviewmentioning

confidence: 99%

A Markovian reliability approach for offshore wind energy system analysis in harsh environments

Adumene

Okoro

2020

Engineering Reports

View full text Add to dashboard Cite

For an effective monitoring strategy of offshore energy systems in harsh environments, it is vital the system reliability dynamics be fully understood. This article presents the application of a Markovian process for dynamic reliability prediction of an offshore energy system. In the proposed approach, a three‐state Markovian process is developed for the analysis of the subsystem states, which dynamically determines the overall system reliability performance, overcoming the static limitation of fault tree analysis. The multistate subsystems are discretized into normal, degraded, and failed states to demonstrate the performance dynamics of the system for condition monitoring. The proposed approach is tested on a case study and the most critical influencing elements are identified. The application of the methodology efficiently identifies the system vulnerability path so as to assist in system integrity management and to provide a guide to early remedial action against total failure and downtime in offshore energy generation.

show abstract

Assessing offshore wind turbine reliability and availability

Cited by 7 publications

References 27 publications

A Comprehensive Review of Artificial Intelligence and Wind Energy

A Comprehensive Review of Artificial Intelligence and Wind Energy

Bayesian Network Modelling for the Wind Energy Industry: An Overview

A Markovian reliability approach for offshore wind energy system analysis in harsh environments

Contact Info

Product

Resources

About