A probabilistic rainfall model to estimate the leading-edge lifetime of wind turbine blade coating system

Verma, Amrit Shankar; Jiang, Zhiyu; Caboni, Marco; Verhoef, Hans; Mijle‐Meijer, Harald; Castro, Saullo G.P.; Teuwen, Julie J.E.

doi:10.1016/j.renene.2021.06.122

Cited by 23 publications

(15 citation statements)

References 38 publications

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“…The damage of turbine blades is caused by heavy rainfall and strong winds in combination and therefore, the coastal stations with high annual mean wind speeds are the most prone to damage of the turbine blades. This is in line with results from the Netherlands (Verma et al, 2021b). 375 https://doi.org/10.5194/wes-2022-59 Preprint.…”

supporting

confidence: 89%

“…Previous blade lifetime analyses are based on rain gauge observations (Bech et al, accepted;Hasager et al, 2020;Law and Koutsos, 2020;Skrzypiński et al, 2020;Verma et al, 2021a), disdrometer observations (Tilg et al, 2022;Verma et al, 2021b), weather radar observations (Letson et al, 2020), re-analysis data (Prieto and Karlsson, 2021), and mesoscale weather model outputs 55 (Eisenberg et al, 2018;Visbech et al, in review).…”

Section: Introductionmentioning

confidence: 99%

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Lifetime prediction of turbine blades using global precipitation products from satellites

Badger

Zuo²,

Hannesdóttir³

et al. 2022

Preprint

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Abstract. The growing size of wind turbines leads to extremely high tip speeds when the blades are rotating. The blades are prone to leading edge erosion when raindrops hit the blades at such high speeds and blade damage will eventually affect the power production until repair or replacement of the blade is performed. Since these actions come with a high cost, it is relevant to estimate the blade lifetime for a given wind farm site prior to wind farm construction. Modelling tools for blade lifetime prediction require input time series of rainfall intensities and wind speeds in addition to a turbine-specific tip speed curve. In this paper, we investigate the suitability of satellite-based precipitation data from the Global Precipitation Measurement (GPM) Mission in the context of blade lifetime prediction. We first evaluate satellite-based rainfall intensities from the Integrated Multi-Satellite Retrievals for GPM (IMERG) final product against in situ observations at 18 weather stations located in Germany, Denmark, and Portugal. We then use the satellite and in situ rainfall intensities as input to a model for blade lifetime prediction together with the wind speeds measured at the stations. We find that blade lifetimes estimated with rainfall intensities from satellites and in situ observations are in good agreement despite the very different nature of the observation methods and the fact that IMERG products have a 30 minute temporal resolution whereas in situ stations deliver 10 minute accumulated rainfall intensities. Our results indicate that the wind speed has a large impact on the estimated blade lifetimes. Inland stations show significantly longer blade lifetimes than coastal stations, which are more exposed to high mean wind speeds. One station located in mountainous terrain shows large differences between rainfall intensities and blade lifetimes based on satellite and in situ observations. IMERG rainfall products are known to have a limited accuracy in mountainous terrain. Our analyses also confirm that IMERG overestimates light rainfall and underestimates heavy rainfall. Given that networks of in situ stations have large gaps over the oceans, there is a potential for utilizing rainfall products from satellites to estimate and map blade lifetimes. This is useful as more wind power is installed offshore including floating installations very far from the coast.

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supporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Lifetime prediction of turbine blades using global precipitation products from satellites

Badger

Zuo²,

Hannesdóttir³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…A solar-based cell is generally a p-n junction developed as a thin layer of a semiconductor. The electromagnetic radiation of solar energy can be straightforwardly changed over power through photovoltaic impact (Mohammad et al, 2020;Mousavi et al, 2020;Nyeche and Diemuodeke, 2020;Verma et al, 2021). The fundamental equation that characterizes the V-I characteristics of a solar cell can be derived from the equivalent circuit by applying Kirchhoff's current law:…”

Section: Modeling Of the Pv Power Generation Systemmentioning

confidence: 99%

Performance Enhancement of an Economically Operated DC Microgrid With a Neural Network–Based Tri-Port Converter for Rural Electrification

Sitharthan

Madurakavi²,

Raglend³

et al. 2022

Front. Energy Res.

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The DC Microgrid sounds familiar in recent days for its independent grid operation and energizing small communities without relying on the central grid. The sudden change in energy demand in the microgrid can negatively impact its performance and operation. Energy management is the only optimal solution to the energy production of microgrids. This article -discusses an economically operated DC microgrid for rural electrification with a tri-port converter based on the radial basis function neural network (RBFNN)-based intelligent control strategy to provide enhanced performance to the microgrid. The advantage of the proposed system is that it provides optimal energy management solutions during dynamic loading conditions and enhances the operation of the microgrid. The outstanding aspect of the proposed system is that it boosts the conversion operation and effectively manages the battery energy storage system to supply energy to the domestic loads and supply power to the grid during excess power generation. In the assessment, the rural regions of Tamilnadu and Andhra Pradesh, India, have been considered to enhance the microgrid setup. The performance evaluation of the proposed system has been conducted and validated using an experimental setup. The assessment also discusses the economic and environmental analysis in using the proposed system. The results support the performance and efficiency of the proposed model.

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“…In order to enable site-specific analysis for rain erosion performance and to generate rain droplet distribution fields, meteorological data are required [ 19 ]. The meteorological data are used to predict erosion behavior based on the Springer model.…”

Section: Introductionmentioning

confidence: 99%

The Springer Model for Lifetime Prediction of Wind Turbine Blade Leading Edge Protection Systems: A Review and Sensitivity Study

2022

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The wind energy sector is growing rapidly. Wind turbines are increasing in size, leading to higher tip velocities. The leading edges of the blades interact with rain droplets, causing erosion damage over time. In order to mitigate the erosion, coating materials are required to protect the blades. To predict the fatigue lifetime of coated substrates, the Springer model is often used. The current work summarizes the research performed using this model in the wind energy sector and studies the sensitivity of the model to its input parameters. It is shown that the Springer model highly depends on the Poisson ratio, the strength values of the coating and the empirically fitted a2 constant. The assumptions made in the Springer model are not physically representative, and we reasoned that more modern methods are required to accurately predict coating lifetimes. The proposed framework is split into three parts—(1) a contact pressure model, (2) a coating stress model and (3) a fatigue strength model—which overall is sufficient to capture the underlying physics during rain erosion of wind turbine blades. Possible improvements to each of the individual aspects of the framework are proposed.

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A probabilistic rainfall model to estimate the leading-edge lifetime of wind turbine blade coating system

Cited by 23 publications

References 38 publications

Lifetime prediction of turbine blades using global precipitation products from satellites

Lifetime prediction of turbine blades using global precipitation products from satellites

Performance Enhancement of an Economically Operated DC Microgrid With a Neural Network–Based Tri-Port Converter for Rural Electrification

The Springer Model for Lifetime Prediction of Wind Turbine Blade Leading Edge Protection Systems: A Review and Sensitivity Study

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