Uncertain accessibility estimation method for offshore wind farm based on multi‐step probabilistic wave forecasting

Zhang, Hao; Yan, Jie; Han, Soonhung; Li, Li; Liu, Yongqian; Infield, David

doi:10.1049/rpg2.12227

Cited by 9 publications

(3 citation statements)

References 34 publications

(58 reference statements)

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“…Refs. [165,166] used a two-stage stochastic programming model and a multi-step probabilistic wave prediction model, respectively, to determine the optimal fleet configuration and the transport path, taking into account the uncertainty of weather conditions and the occurrence of failures. The concept of self-reconfiguration of wind turbine position in Floating Offshore Wind Farms (FOWF) is discussed.…”

Section: Cost Minimizationmentioning

confidence: 99%

Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey

Yang

Jia

et al. 2023

Energies

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Offshore Wind Power Systems (OWPS) offer great energy and environmental advantages, but also pose significant Operation and Maintenance (O&M) challenges. In this survey, we analyze these challenges and propose some optimization strategies and technologies for OWPS comprehensively. The existing literature review mainly focuses on a certain field of offshore wind power O&M, but lacks a comprehensive introduction to offshore wind power. We consider the energy efficiency, reliability, safety, and economy of OWPS from various aspects, such as offshore wind and wave energy utilization, offshore wind turbine components, and wind power operation parameters, and compare them with onshore wind power systems. We suggest that OWPS can benefit from advanced design optimization, digital twin, monitoring and forecasting, fault diagnosis, and other technologies to enhance their O&M performance. This paper aims to provide theoretical guidance and practical reference for the technological innovation and sustainable development of OWPS.

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Section: Cost Minimizationmentioning

confidence: 99%

Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey

Yang

Jia

et al. 2023

Energies

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“…Other examples related to bottom-fixed offshore turbines are shown in [31], where the authors prove that the exponentiated Weibull distribution provides a better model fit to significant wave height data than the translated Weibull distribution, which is more widely used. Based on 7-year observation data of a buoy station, Zhang et al [32] present an uncertain accessibility estimation method based on a multi-step probabilistic wave height forecasting model and Monte Carlo simulation to improve offshore wind farm operation and maintenance. However, as shown, there are only a few examples of wave models oriented to floating wind turbines, and they are mainly focused on how the waves affect the response of the turbine instead of on wave height forecasting.…”

Section: Related Workmentioning

confidence: 99%

Use of State-of-Art Machine Learning Technologies for Forecasting Offshore Wind Speed, Wave and Misalignment to Improve Wind Turbine Performance

Sacie

Santos

López

et al. 2022

JMSE

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One of the most promising solutions that stands out to mitigate climate change is floating offshore wind turbines (FOWTs). Although they are very efficient in producing clean energy, the harsh environmental conditions they are subjected to, mainly strong winds and waves, produce structural fatigue and may cause them to lose efficiency. Thus, it is imperative to develop models to facilitate their deployment while maximizing energy production and ensuring the structure’s safety. This work applies machine learning (ML) techniques to obtain predictive models of the most relevant metocean variables involved. Specifically, wind speed, significant wave height, and the misalignment between wind and waves have been analyzed, pre-processed and modeled based on actual data. Linear regression (LR), support vector machines regression (SVR), Gaussian process regression (GPR) and neural network (NN)-based solutions have been applied and compared. The results show that Nonlinear autoregressive with an exogenous input neural network (NARX) is the best algorithm for both wind speed and misalignment forecasting in the time domain (72% accuracy) and GPR for wave height (90.85% accuracy). In conclusion, these models are vital to deploying and installing FOWTs and making them profitable.

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“…A small fraction of those efforts partially account for revenue losses by incentivizing the grouping of maintenance tasks at periods of low power production (Besnard et al, 2009;Yildirim et al, 2017;Song et al, 2018). A separate line of research focuses on forecasting access-based opportunities (Zhang et al, 2021;Yang et al, 2020;Lubing et al, 2019;Taylor and Jeon, 2018), but largely overlooks those based on economic dependencies and/or those related to revenue losses.…”

Section: The Need For Opportunistic Maintenancementioning

confidence: 99%

STOCHOS: Stochastic Opportunistic Maintenance Scheduling For Offshore Wind Farms

Papadopoulos¹,

Coit²,

Ezzat³

2022

Preprint

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Despite the promising outlook, the numerous economic and environmental benefits of offshore wind energy are still compromised by its high operations and maintenance (O&M) expenditures. On one hand, offshore-specific challenges such as site remoteness, harsh weather, high transportation requirements, and production losses, significantly inflate the total O&M expenses relative to land-based wind farms. On the other hand, the uncertainties in weather conditions, asset degradation, and electricity prices largely constrain the farm operator's ability to determine the time windows at which maintenance is possible, let alone optimal. In response, we propose STOCHOS, short for the stochastic holistic opportunistic scheduler-a maintenance scheduling approach tailored to address the unique challenges and uncertainties in offshore wind farms. Given probabilistic forecasts of key environmental and operational parameters, STOCHOS optimally schedules the offshore maintenance tasks by harnessing the maintenance opportunities that arise due to a combination of favorable weather conditions, on-site maintenance resources, and maximal operating revenues. STOCHOS is formulated as a two-stage stochastic mixed integer linear program, which we solve using a scenariobased rolling horizon algorithm that aligns with the industrial practice. Scenarios are generated using a probabilistic forecasting framework which adequately characterizes the temporal dependence in key input parameters. Evaluated on a real-world case study from the U.S. North Atlantic where several large-scale offshore wind farms are currently being developed, STOCHOS demonstrates considerable improvements relative to prevalent maintenance benchmarks, across several O&M metrics, including total cost, downtime, resource utilization, and maintenance interruptions, attesting to its potential merit towards enabling the economic viability of offshore wind energy.

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Uncertain accessibility estimation method for offshore wind farm based on multi‐step probabilistic wave forecasting

Cited by 9 publications

References 34 publications

Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey

Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey

Use of State-of-Art Machine Learning Technologies for Forecasting Offshore Wind Speed, Wave and Misalignment to Improve Wind Turbine Performance

STOCHOS: Stochastic Opportunistic Maintenance Scheduling For Offshore Wind Farms

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