Indicator-based Safety and Security Assessment of Offshore Wind Farms

Torres, Frank Sill; Kulev, Nikolai; Skobiej, Bartosz; Meyer, Michael J.; Eichhorn, Oliver; Schäfer-Frey, Jan

doi:10.1109/rws50334.2020.9241287

Cited by 12 publications

(3 citation statements)

References 29 publications

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“…Lutz et al present a digitalization workflow in the wind energy domain, to extract and structure maintenance data as a basis for reliability KPI calculation [2]. Frank et al [14] present a methodology that uses KPIs and key risk indicators to assess the safety and security of offshore wind farms.…”

Section: Kpi Using Knowledge Discoverymentioning

confidence: 99%

KPI Extraction from Maintenance Work Orders—A Comparison of Expert Labeling, Text Classification and AI-Assisted Tagging for Computing Failure Rates of Wind Turbines

Lutz,

Schäfermeier,

Sexton

et al. 2023

Energies

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Maintenance work orders are commonly used to document information about wind turbine operation and maintenance. This includes details about proactive and reactive wind turbine downtimes, such as preventative and corrective maintenance. However, the information contained in maintenance work orders is often unstructured and difficult to analyze, presenting challenges for decision-makers wishing to use it for optimizing operation and maintenance. To address this issue, this work compares three different approaches to calculating reliability key performance indicators from maintenance work orders. The first approach involves manual labeling of the maintenance work orders by domain experts, using the schema defined in an industrial guideline to assign the label accordingly. The second approach involves the development of a model that automatically labels the maintenance work orders using text classification methods. Through this method, we are able to achieve macro average and weighted average F1-scores of 0.75 and 0.85 respectively. The third technique uses an AI-assisted tagging tool to tag and structure the raw maintenance information, together with a novel rule-based approach for extracting relevant maintenance work orders for failure rate calculation. In our experiments, the AI-assisted tool leads to an 88% drop in tagging time in comparison to the other two approaches, while expert labeling and text classification are more accurate in KPI extraction. Overall, our findings make extracting maintenance information from maintenance work orders more efficient, enable the assessment of reliability key performance indicators, and therefore support the optimization of wind turbine operation and maintenance.

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Section: Kpi Using Knowledge Discoverymentioning

confidence: 99%

KPI Extraction from Maintenance Work Orders—A Comparison of Expert Labeling, Text Classification and AI-Assisted Tagging for Computing Failure Rates of Wind Turbines

Lutz,

Schäfermeier,

Sexton

et al. 2023

Energies

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“…In case of OWF maintenance, there are following relevant KPIs: Operational Availability (OA), Mean Time To Recover (MTTR), Mean Time Between Failures (MTBF), Overall Equipment Effectiveness (OEE), and Inoperable Assets (IA) (Torres et al 2020, Parmenter 2007, Baltic and International Maritime Council (BIMCO) (2018, Standards Policy andStrategy Committee 2019, Moran 2019). One can note, that these parameters are interconnected, e.g., IA is directly linked to OA, MTTR, and MTBF.…”

Section: Maintenance Modelmentioning

confidence: 99%

Validation of copula-based weather generator for maintenance model of offshore wind farm

Skobiej¹,

Niemi²

2021

WMU J Marit Affairs

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This article discusses the aspect of modeling weather conditions in marine environment for implementation in the offshore wind farm domain. It is clear that harsh sea weather conditions influence many characteristics of any offshore installation. The accessibility to the infrastructure, maintenance procedures, failure ratios of components, energy provision levels, or utilization of vessels—are the examples of weather-related issues connected to the offshore wind industry. Regarding the growing popularity of digital twin methodology, authors present a novel view to generate weather data with copula-based method. The results obtained are compared to selected historical data and implemented into the maintenance model. The selected indicators of maintenance service are used for usability assessment of proposed copula-based method.

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“…In the first step, the general safety and security goals of a OWF had to be identified. With the help of experts and stakeholders, nine categories were defined, which are further explained in Table 1 [9,12,16,36]. Next, the stakeholder groups had to be identified.…”

Section: The Survey and Analysis Of Stakeholder Goalsmentioning

confidence: 99%

An Expert-Driven Probabilistic Assessment of the Safety and Security of Offshore Wind Farms

et al. 2021

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Offshore wind farms (OWFs) are important infrastructure which provide an alternative and clean means of energy production worldwide. The offshore wind industry has been continuously growing. Over the years, however, it has become evident that OWFs are facing a variety of safety and security challenges. If not addressed, these issues may hinder their progress. Based on these safety and security goals and on a Bayesian network model, this work presents a methodological approach for structuring and organizing expert knowledge and turning it into a probabilistic model to assess the safety and security of OWFs. This graphical probabilistic model allowed us to create a high-level representation of the safety and security state of a generic OWF. By studying the interrelations between the different functions of the model, and by proposing different scenarios, we determined the impacts that a failing function may have on other functions in this complex system. Finally, this model helped us define the performance requirements of such infrastructure, which should be beneficial for optimizing operation and maintenance.

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Indicator-based Safety and Security Assessment of Offshore Wind Farms

Cited by 12 publications

References 29 publications

KPI Extraction from Maintenance Work Orders—A Comparison of Expert Labeling, Text Classification and AI-Assisted Tagging for Computing Failure Rates of Wind Turbines

KPI Extraction from Maintenance Work Orders—A Comparison of Expert Labeling, Text Classification and AI-Assisted Tagging for Computing Failure Rates of Wind Turbines

Validation of copula-based weather generator for maintenance model of offshore wind farm

An Expert-Driven Probabilistic Assessment of the Safety and Security of Offshore Wind Farms

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