Ice protection systems for wind turbines in cold climate: characteristics, comparisons and analysis

Fakorede, Oloufemi; Feger, Zoé; Ibrahim, Hussein; Ilinca, Adrian; Perron, Jean; Masson, Christian

doi:10.1016/j.rser.2016.06.080

Cited by 178 publications

(98 citation statements)

References 17 publications

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“…(a) 3D model of the ultrasonic transducer in wind turbine blade; (b) experimental pneumatic de‐icing system; (c) working principal of rubber inflated boots; (d) ultrasonic de‐icing technique currently being tested and (e) expulsive de‐icing . [Color figure can be viewed at wileyonlinelibrary.com]…”

Section: Active Solutions For Wind Turbine Applicationmentioning

confidence: 99%

Materials, Innovations and Future Research Opportunities on Wind Turbine Blades—Insight Review

Karthikeyan

Anand

Suthakar

et al. 2018

Env Prog and Sustain Energy

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Nowadays, wind turbine blades are manufactured using several materials and methodologies to save cost and to increase their performance. This article gives a brief overview of blade materials and prevailing manufacturing traits to make them more reliable and cost‐efficient. The surface roughness, manufacturing defects, and fluctuating loads in flow fields significantly affect wind turbine power generation. However, these problems can be reduced by using appropriate materials. Therefore, selection of wind turbine materials is extremely important to maintain its performance even in harsh environmental conditions. This article describes an overview of current material solutions for overcoming material defects like delamination, structural distortion, and icing problems in the Polar Regions. It involves composites, smart alloys, protective coatings, etc. © 2018 American Institute of Chemical Engineers Environ Prog, 38:e13046, 2019 Highlights Types of loading, failure mechanisms and current manufacturing methods of wind turbine blades. Innovative blade material approach: Morphing concepts, Glass or Carbon fiber/Nano‐/Bio‐Composites. Active and passive strategies for anti‐icing or de‐icing of wind turbine blades.

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Section: Active Solutions For Wind Turbine Applicationmentioning

confidence: 99%

Materials, Innovations and Future Research Opportunities on Wind Turbine Blades—Insight Review

Karthikeyan

Anand

Suthakar

et al. 2018

Env Prog and Sustain Energy

View full text Add to dashboard Cite

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“…To better harness wind energy, land‐based wind turbines (WTs) are usually deployed at high‐altitude regions where the climate is cold and high humidity. Under this operating environment, however, WTs are vulnerable to blade icing problem which could cause lots of troubles . On one hand, the blade airfoil changes after ice accretion.…”

Section: Introductionmentioning

confidence: 99%

“…Meteorological observing systems collect the meteorological data, then predict the blade icing condition through analyzing the effect of atmosphere changes on WT . For external icing condition monitoring systems, specialized sensors are installed on the WT besides the standard configuration to measure the physical property changes in blades due to ice accretion, such as mass, conductivities, and dielectric constants . However, this method not only increases the WT mechanical complexity, but also induces extra maintenance costs.…”

Section: Introductionmentioning

confidence: 99%

Intelligent wind turbine blade icing detection using supervisory control and data acquisition data and ensemble deep learning

Liu

Cheng

Kong

et al. 2019

Energy Science & Engineering

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Ice accretion on wind turbine blades is one of the major faults affecting the operational safety and power generation efficiency of wind turbines. Current icing detection methods are based on either meteorological observing system or extra condition monitoring system. Compared with current methods, icing detection using the intrinsic supervisory control and data acquisition (SCADA) data of wind turbines has plenty of potential advantages, such as low cost, high stability, and early icing detection ability. However, there have not been deep investigations in this field at present. In this paper, a novel intelligent wind turbine blade icing detection method based on the wind turbine SCADA data is proposed. This method consists of three processes: SCADA data preprocessing, automatic feature extraction, and ensemble icing detection model construction. Specifically, deep autoencoders network is employed to learn multilevel fault features from the complex SCADA data adaptively. And the ensemble technique is utilized to make full use of all the extracted features from different hidden layers of the deep autoencoders network to build the ensemble icing detection model. The effectiveness of the proposed method is validated using the data collected from actual wind farms. The experimental results reveal that the proposed method is able to not only adaptively extract valuable fault features from the complex SCADA data, but also obtains higher detection accuracy and generalization capability compared with conventional machine learning models and individual deep learning model.

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“…The wind turbines built in cold regions, such as high latitude or high altitude, often suffer from heavy icing problem. When the wind turbines, particularly turbine blades, are coated by ice, various problems will occur, such as reduction of power due to disrupted aerodynamics, increased fatigue of components due to mass imbalance, and damage or harm caused by uncontrolled shedding of large ice chunks . Under extreme icing conditions, it may induce complete loss of production.…”

Section: Introductionmentioning

confidence: 99%

“…When the wind turbines, particularly turbine blades, are coated by ice, various problems will occur, such as reduction of power due to disrupted aerodynamics, increased fatigue of components due to mass imbalance, and damage or harm caused by uncontrolled shedding of large ice chunks. [1][2][3][4] Under extreme icing conditions, it may induce complete loss of production. As a result, timely detection of icing even removal of ice on wind turbine blades is an important issue for the efficiency and safety of the wind turbines.…”

Section: Introductionmentioning

confidence: 99%

Ice monitoring of a full-scale wind turbine blade using ultrasonic guided waves under varying temperature conditions

Wang

Zhou

Bao

et al. 2018

Struct Control Health Monit

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Summary The icing of wind turbine blades is a common problem in cold climate area. Ice accretion on wind turbines, particularly turbine blades, can cause a variety of problems. The extreme icing may induce full stop of the turbine system. Thus, ice monitoring is one of the important issues for turbine blade icing solutions. The ice coating on the blade surface can change the primary propagation characteristics of the elastic waves. Therefore, Lamb waves are proposed to monitor the ice‐forming processes of a full‐scale wind turbine blade. Because the varying temperature is a remarkable and inevitably factor during ice monitoring, a principal component analysis (PCA)‐based ice monitoring method is proposed to eliminate the temperature effects and is first demonstrated by a segment of the blade in the laboratory. Subsequently, the proposed method is applied on a full‐scale blade in the frozen tunnel. The PZT wafers array is arranged to enhance the guided wave signals, and the tuning and attenuation characteristics are investigated on the clear surface of blade without ice. Finally, the PCA‐based method is used to identify the ice formation again on both the tip and the middle segments of the full‐scale blade under varying temperature conditions. The results indicate that the proposed ultrasonic guided wave combining PCA‐based method is efficient and sensitive for ice monitoring of the full‐scale wind turbine blade.

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Ice protection systems for wind turbines in cold climate: characteristics, comparisons and analysis

Cited by 178 publications

References 17 publications

Materials, Innovations and Future Research Opportunities on Wind Turbine Blades—Insight Review

Materials, Innovations and Future Research Opportunities on Wind Turbine Blades—Insight Review

Intelligent wind turbine blade icing detection using supervisory control and data acquisition data and ensemble deep learning

Ice monitoring of a full-scale wind turbine blade using ultrasonic guided waves under varying temperature conditions

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