A Comprehensive Analysis of Wind Turbine Blade Damage

Katsaprakakis, Dimitris Al.; Papadakis, N.; Ntintakis, Ioannis

doi:10.3390/en14185974

Cited by 58 publications

(31 citation statements)

References 93 publications

(106 reference statements)

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“…1. The icing of the blades is a critical problem that affects the capacity of the turbine for power generation as well as its safety [4][5][6]. Some researchers have claimed that the power output of a wind turbine declines by 27% in the case of icing [7].…”

Section: Introductionmentioning

confidence: 99%

Detecting Icing on the Blades of a Wind Turbine Using a Deep Neural Network

Li¹,

Xu²,

Liu³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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The blades of wind turbines located at high latitudes are often covered with ice in late autumn and winter, where this affects their capacity for power generation as well as their safety. Accurately identifying the icing of the blades of wind turbines in remote areas is thus important, and a general model is needed to this end. This paper proposes a universal model based on a Deep Neural Network (DNN) that uses data from the Supervisory Control and Data Acquisition (SCADA) system. Two datasets from SCADA are first preprocessed through undersampling, that is, they are labeled, normalized, and balanced. The features of icing of the blades of a turbine identified in previous studies are then used to extract training data from the training dataset. A middle feature is proposed to show how a given feature is correlated with icing on the blade. Performance indicators for the model, including a reward function, are also designed to assess its predictive accuracy. Finally, the most suitable model is used to predict the testing data, and values of the reward function and the predictive accuracy of the model are calculated. The proposed method can be used to relate continuously transferred features with a binary status of icing of the blades of the turbine by using variables of the middle feature. The results here show that an integrated indicator system is superior to a single indicator of accuracy when evaluating the prediction model.

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Section: Introductionmentioning

confidence: 99%

Detecting Icing on the Blades of a Wind Turbine Using a Deep Neural Network

Li¹,

Xu²,

Liu³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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show abstract

“…In order to produce more energy from wind turbine blades, they are being made larger and currently exceed 200 m in diameter. These large wind turbines are prone to different types of damage [ 1 , 2 ]. The tip velocities of over 100 ms

cause interactions of the blades with rain droplets and other airborne particles, leading to erosion damage over time [ 3 ].…”

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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“…In the field of wind energy, it is essential to increase the yield per turbine by reducing the turn-off times and increasing the rotor diameter. With the rotor blades among the most fatigue-stressed technical components in terms of fatigue life time, load variability and ambient conditions [1,2], there is a need for stronger and more damage-resistant light-weight materials [3]. In the design process of wind turbine rotor blades, fatigue is one of the most important issues [4].…”

Section: Introductionmentioning

confidence: 99%

Taurine-Modified Boehmite Nanoparticles for GFRP Wind Turbine Rotor Blade Fatigue Life Enhancement

2021

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Advanced nanoparticle-reinforced glass fibre composites represent a promising approach to improving the service life of fatigue-loaded structures such as wind turbine rotor blades. However, processing particle-reinforced resins using advanced infusion techniques is problematic due to, for example, higher viscosity as well as filtering effects. In this work, the effects of boehmite nanoparticles on viscosity, static properties and fatigue life are investigated experimentally. Whereas rheological analysis reveals a significant increase of viscosity in the case of pristine boehmite particles, an additional taurine surface modification of the particles can effectively reduce viscosity increase. As regards mechanical properties, significant improvements of both static as well as fatigue properties are found. The addition of 15 wt.% of boehmite particles increases fatigue life by a maximum of 270% compared to the unmodified fibre-reinforced epoxy. Transmitted light-based investigation of the damage mechanisms shows delayed initiation and smaller growth rates for laminates containing boehmite particles. At the same time, the observed mechanisms and their accumulation along the relative cycle number do not change significantly. In addition, by characterising autonomous heating, the so-called Risitano fatigue limit is determined. The results reveal that with increasing particle content there is an increase in the fatigue limit.

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A Comprehensive Analysis of Wind Turbine Blade Damage

Cited by 58 publications

References 93 publications

Detecting Icing on the Blades of a Wind Turbine Using a Deep Neural Network

Detecting Icing on the Blades of a Wind Turbine Using a Deep Neural Network

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

Taurine-Modified Boehmite Nanoparticles for GFRP Wind Turbine Rotor Blade Fatigue Life Enhancement

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