Wave height forecasting to improve off-shore access and maintenance scheduling

Dinwoodie, Iain; Catterson, Victoria M.; McMillan, David

doi:10.1109/pesmg.2013.6672438

Cited by 11 publications

(11 citation statements)

References 10 publications

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“…The training set was limited to 2 years of data (2006 and 2007), while the test set was from 00:00 1 January 2008 until the end of the data set. An earlier study showed that varying the length of the training set had no significant impact on model accuracy for this data …”

Section: Methodologiesmentioning

confidence: 84%

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An economic impact metric for evaluating wave height forecasters for offshore wind maintenance access

et al. 2015

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This paper demonstrates that wave height forecasters chosen on statistical quality metrics result in sub-optimal decision support for offshore wind farm maintenance. Offshore access is constrained by wave height, but the majority of approaches to evaluating the effectiveness of a wave height forecaster utilize overall accuracy or error rates. This paper introduces a new metric more appropriate to the wind industry, which considers the economic impact of an incorrect forecast above or below critical wave height boundaries. The paper describes a process for constructing a value criteria where the implications between forecasting error and economic consequences are explicated in terms of opportunity costs and realized maintenance costs. A comparison between nine forecasting techniques for modeling and predicting wave heights based on historical data, including an ensemble aggregator, is described demonstrating that the performance ranking of forecasters is sensitive to the evaluation criteria. The results highlight the importance of appropriate metrics for wave height prediction specific to the wind industry, and the limitations of current models that minimize a metric that does not support decision making. With improved ability to forecast weather windows, maintenance scheduling is subject to less uncertainty, hence reducing costs related to vessel dispatch, and lost energy due to downtime

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Section: Methodologiesmentioning

confidence: 84%

“…The ANN is perhaps the most commonly applied intelligent system technique for nonlinear regression problems and has in the past been applied to wave height modeling . The attraction of an ANN is that with a three‐layer network comprised of simple units (neurons), any function can be approximated .…”

Section: Methodologiesmentioning

confidence: 99%

An economic impact metric for evaluating wave height forecasters for offshore wind maintenance access

et al. 2015

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“…and wind speed determine whether it is possible to perform maintenance activities at sea since the vessels access to offshore turbines are limited in by these factors. For example, [8] presents operational wave height limits for various forms of transportation, including helicopters and sea vessels to improve the ability to schedule maintenance, reducing costs related to vessel dispatch and recall due to unexpected wave patterns. Catterson et al (2016), [9], proposed an economic forecasting metric (EFM) which considers the economic impact of an incorrect forecast above or below critical wave height boundaries.…”

Section: Related Work Of Forecasting Using Data-driven Methodsmentioning

confidence: 99%

Data‐driven weather forecasting models performance comparison for improving offshore wind turbine availability and maintenance

Pandit

Kolios

Infield

2020

IET Renewable Power Generation

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Wind energy is an attractive alternative to conventional sources of electricity generation due to its effectively zero carbon emissions. Wind power is highly dependent on wind speed and operations offshore are affected by wave height; these together called turbine weather datasets that are variable and intermittent over various timescales and signify offshore weather conditions. In contrast to onshore wind, offshore wind requires improved forecasting since unfavourable weather prevents repair and maintenance activities. Delayed repair results in increased downtime and reduced wind farm availability and energy yield. This paper proposes two data-driven models for long-term weather conditions forecasting to improve the wind farm availability and support operation and maintenance (O&M) decision-making process. These two data-driven approaches are Long Short-Term Memory Network, abbreviated as LSTM, and Markov chain. A LSTM is an artificial recurrent neural network (RNN), capable of learning long-term dependencies within a sequence of data and is typically used to avoid the long-term dependency problem. While, Markov is another data-driven stochastic model, which assumes that, the future states depend only on the current states, not on the events that occurred before. The readily available weather datasets are obtained from FINO3 database to train and validate the performance of these data-driven models. A performance comparison between these weather forecasted models would be carried out to determine which approach is most accurate and suitable for improving offshore wind turbine availability and support maintenance activities. The full paper outlines the weakness and strength associated with proposed models in relations to offshore wind farms operational activities.

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“…Lost turbine availability is related to the failure of a component, but also depends largely on access to the turbine, and in [125], a computational approach based on probability calculations is developed, allowing estimates to be made on very fast offshore access probabilities and expected delays. Furthermore, Dinwoodie et al [126] performed research on the modeling and prediction of wave height, which is one of the key criteria for access to offshore wind farms, and this was done through data mining.…”

Section: Maintenance Optimization (M_opt)mentioning

confidence: 99%

New Tendencies in Wind Energy Operation and Maintenance

et al. 2021

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Both the reduction in operating and maintenance (O&M) costs and improved reliability have become top priorities in wind turbine maintenance strategies. O&M costs typically account for 20% to 25% of the total levelized cost of electricity (LCOE) of current wind power systems. This paper provides a general review of the state of the art of research conducted on wind farm maintenance in recent years. It shows the new methods and techniques, focusing on trends and future challenges. In addition to this, this work includes a review of the following items: (i) operation and maintenance, (ii) failure rate, (iii) reliability, (iv) condition monitoring, (v) maintenance strategies, (vi) maintenance and life cycle and (vii) maintenance optimization As for offshore wind turbines, it is crucial to limit the maximum faults, since the maintenance of these wind farms is more complex both technically and logistically. Research into wind farm maintenance increased by 87% between 2007 and 2019, with more than 38,000 papers (Scopus) including “wind energy” as the main topic and some keywords related to O&M costs. The LCOE in onshore wind projects has decreased by 45%, while in offshore projects it has decreased by 28%. The O&M costs of onshore wind projects fell 52%, while in the case of offshore projects, they have declined 45%. Thus, the results obtained in this paper suggest that there is a change in research on wind farm operation and maintenance, as in recent years, scientific interest in failure has been increasing, while interest in the various techniques of wind farm maintenance and operation has been decreasing.

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Wave height forecasting to improve off-shore access and maintenance scheduling

Cited by 11 publications

References 10 publications

An economic impact metric for evaluating wave height forecasters for offshore wind maintenance access

An economic impact metric for evaluating wave height forecasters for offshore wind maintenance access

Data‐driven weather forecasting models performance comparison for improving offshore wind turbine availability and maintenance

New Tendencies in Wind Energy Operation and Maintenance

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