Forecasting for day-ahead offshore maintenance scheduling under uncertainty

Browell, Jethro; Dinwoodie, Iain; McMillan, David

doi:10.1201/9781315374987-171

Cited by 15 publications

(11 citation statements)

References 14 publications

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“…As stated in [11], it is almost impossible to generate a flawless maintenance plan in terms of avoiding production loss, since it is difficult to find a period where the turbine is not producing due to low wind speeds. What can be done in this sense is to schedule the maintenance with an acceptable uncertainty [27,28].…”

Section: Maintenance Plans and Problem Statementmentioning

confidence: 99%

Automated wind turbine maintenance scheduling

Yürüşen

Rowley

Watson

et al. 2020

Reliability Engineering & System Safety

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While many operation and maintenance (O&M) decision support systems (DSS) have been already proposed, a serious research need still exists for wind farm O&M scheduling. O&M planning is a challenging task, as maintenance teams must follow specific procedures when performing their service, which requires working at height in adverse weather conditions. Here, an automated maintenance programming framework is proposed based on real case studies considering available wind speed and wind gust data. The methodology proposed consists on finding the optimal intervention time and the most effective execution order for maintenance tasks and was built on information from regular maintenance visit tasks and a corrective maintenance visit. The objective is to find possible schedules where all work orders can be performed without breaks, and to find out when to start in order to minimise revenue losses (i.e. doing maintenance when there is least wind). For the DSS, routine maintenance tasks are grouped using the findings of an agglomerative nesting analysis. Then, the task execution windows are searched within pre-planned maintenance day.

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Section: Maintenance Plans and Problem Statementmentioning

confidence: 99%

Automated wind turbine maintenance scheduling

Yürüşen

Rowley

Watson

et al. 2020

Reliability Engineering & System Safety

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“…Using grey theory and evaluation diagnosis, Sheu and Kuo 36 construct a forecasting model for the prediction of preventive maintenance timing of various machines. Browell et al 39 develop a probabilistic approach to the short-term scheduling problem based on a cost-loss model for individual maintenance missions and probabilistic forecasts of appropriate access windows. Their analysis shows that this method is able to increase the utilization of possible access windows compared to using deterministic decision rules.…”

Section: Pssc Management Decisions and Modelling Techniquesmentioning

confidence: 99%

Status review and research strategies on product-service supply chain

Elomri

Zhang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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This study draws a holistic view of the supply chain with three service strategies – namely, after-sales service; maintenance, repair and operations; and the product-service system – by analysing over 71 articles in this field. In this investigation, the content analysis is used to scrutinize the research to establish the knowledge, reveal the research opportunities and propose research strategies. The supply chain for product-service system depends more heavily on the implementation of the cooperation, collaboration and integration principles in operation. However, a complete analysis reveals that those crucial principles did not embody in current literature. Current research also failed to discuss the service operation decisions by modelling the intricate relations among them in a broad product-service supply chain context. Thus, the future research directions include developing service demand forecasting models; combining the time-series methods and the causal methods; establishing service resource planning models; addressing the relations between service deliverables, service capacity and the service resource; and strengthening the quantitative evaluation of the product-service supply chain performance with a systemic view. Moreover, by addressing the principles of cooperation, collaboration and integration, the added values of this review are the proposed research strategies for integrated forecasting of product services, integrated product-service resource allocation and scheduling, and systematic performance evaluation of the product-service supply chain. The findings and the proposed research strategies develop an in-depth understanding of product-service supply chain applications and serve as a basis for future research.

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“…The advantage of distributional regression is that, given an appropriate choice of parametric distributions, density forecasts are naturally bounded between zero power and nominal power. A conditional heteroscedastic framework is proposed in [77] in which the error variance is conditional on multiple explanatory variables, while [78] used an adaptive variance model to track the dynamics of very-short-term wind power forecast errors. These methods provide the ability to efficiently model a wide range of error distributions.…”

Section: Parametric and Non-parametric Probabilistic Forecastsmentioning

confidence: 99%

“…Short-term probabilistic forecast of access windows can be derived from deterministic weather forecasts and integrated in decision-making problems under risk (cost-loss model) for offshore wind power plants maintenance [78]. Ensemble forecasts may also be employed as an input to stochastic optimization to generate maintenance schedules [156].…”

Section: Maintenance Scheduling Of Wind Power Plantsmentioning

confidence: 99%

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

Bessa

Möhrlen²,

Fundel

et al. 2017

Energies

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Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding of its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. This paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast's properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. A set of recommendations for standardization and improved training of operators are provided along with examples of best practices.

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Forecasting for day-ahead offshore maintenance scheduling under uncertainty

Cited by 15 publications

References 14 publications

Automated wind turbine maintenance scheduling

Automated wind turbine maintenance scheduling

Status review and research strategies on product-service supply chain

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

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