Data - driven Bayesian networks for reliability of supply from renewable sources

Ciobanu, Alexandra; Munteanu, Florin; Nemes, Ciprian; Astanei, Dragoș

doi:10.1109/optim.2017.7974952

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…There are a number of BN software packages available on the market. Some of these packages include Netica, Microsoft MSBN, BayesiaLab, Hugin, WinBUGS, OpenBayes, AgenaRisk and Bayesfusion (Ashrafi et al, 2015; Ciobanu et al, 2017; Li and Shi, 2010; Li et al, 2015; Su and Fu, 2014). Adedipe et al (2020) reviewed BN software and presented their applications in the wind energy sector.…”

Section: The Proposed Modelmentioning

confidence: 99%

A Bayesian network model for the probabilistic safety assessment of offshore wind decommissioning

Shafiee

Adedipe

2022

Wind Engineering

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With increasing the number of wind turbines approaching the end of their service life, it has become crucial for businesses to understand and assess safety and security issues related to the decommissioning phase of wind farm asset lifecycle. This paper aims to develop, for the first time, a Bayesian Network (BN) model for the safety assessment of offshore wind farm decommissioning operations. The most critical safety incidents are identified and their corresponding risk-influencing factors (RIF) are determined. The impacts of human errors as well as procedural and mechanical/electrical failures on the safety and efficiency of decommissioning operations are thoroughly analysed. The findings of the study revealed that the most critical RIFs during offshore wind decommissioning operations include: visibility, crew fatigue, number of personnel per operation, proper safety procedures, crane integrity, number of lifts available in the wind farm, inspection frequency, as well as equipment design.

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Section: The Proposed Modelmentioning

confidence: 99%

A Bayesian network model for the probabilistic safety assessment of offshore wind decommissioning

Shafiee

Adedipe

2022

Wind Engineering

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“…HUGIN develops a causal probabilistic network in such a way to support updating with new information for creating improved posterior probability distribution results (Andersen et al, 1989). In the context of wind energy industry, this software tool was used in Ciobanu et al (2017).…”

Section: Huginmentioning

confidence: 99%

“…In Otero-Casal et al (2019), the authors used a hybrid filter (Kalman-Bayesian) for improved wind forecasting, useful for reliable wind production forecasts. Other papers on this subject area include: Ciobanu et al (2017); Yang et al (2017); Afshari-Igder et al (2018); and Wang et al (2019a).…”

Section: Wind Power Generation Forecastingmentioning

confidence: 99%

Bayesian Network Modelling for the Wind Energy Industry: An Overview

Adedipe¹,

Shafiee

Zio³

2020

Reliability Engineering & System Safety

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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.

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