Atmospheric Ice Loading and its Impact on Natural Frequencies of Wind Turbines

Alsabagh, Abdel Salam Y.; Xu, Yigeng; Virk, Muhammad Shakeel; Badran, Omar

doi:10.1260/0309-524x.39.1.83

Cited by 11 publications

(9 citation statements)

References 21 publications

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Section: Aerodynamic Modelmentioning

confidence: 99%

“…Velocity triangles without and with considering blade vibrations are shown in Figure 3. Expressions for the inflow angle and angle of attack (AOA) without considering blade vibrations are given in Equations (9) and (10). Blade vibration velocities change the relative velocity of wind entering the blade section and inflow angle as shown in Figure 3b.…”

Section: Aerodynamic Modelmentioning

confidence: 99%

“…Lorenzo et al [9] investigated the influence of ice mass on the natural frequencies of the National Renewable Energy Laboratory (NREL) 5 MW wind turbine by extracting modal parameters through operational modal analysis. Alsabagh et al [10] considered different ice mass distributions, as defined in the ISO 12494 Standard, on a multi-megawatt wind turbine. They analyzed the influences of ice mass on natural frequencies and dynamic magnification factors (ratio of the dynamic deflection to static deflection), by considering only the mass changes in the blade.…”

Section: Introductionmentioning

confidence: 99%

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Influence of Icing on the Modal Behavior of Wind Turbine Blades

2016

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Wind turbines installed in cold climate sites accumulate ice on their structures. Icing of the rotor blades reduces turbine power output and increases loads, vibrations, noise, and safety risks due to the potential ice throw. Ice accumulation increases the mass distribution of the blade, while changes in the aerofoil shapes affect its aerodynamic behavior. Thus, the structural and aerodynamic changes due to icing affect the modal behavior of wind turbine blades. In this study, aeroelastic equations of the wind turbine blade vibrations are derived to analyze modal behavior of the Tjaereborg 2 MW wind turbine blade with ice. Structural vibrations of the blade are coupled with a Beddoes-Leishman unsteady attached flow aerodynamics model and the resulting aeroelastic equations are analyzed using the finite element method (FEM). A linearly increasing ice mass distribution is considered from the blade root to half-length and thereafter constant ice mass distribution to the blade tip, as defined by Germanischer Lloyd (GL) for the certification of wind turbines. Both structural and aerodynamic properties of the iced blades are evaluated and used to determine their influence on aeroelastic natural frequencies and damping factors. Blade natural frequencies reduce with ice mass and the amount of reduction in frequencies depends on how the ice mass is distributed along the blade length; but the reduction in damping factors depends on the ice shape. The variations in the natural frequencies of the iced blades with wind velocities are negligible; however, the damping factors change with wind velocity and become negative at some wind velocities. This study shows that the aerodynamic changes in the iced blade can cause violent vibrations within the operating wind velocity range of this turbine.

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Section: Aerodynamic Modelmentioning

confidence: 99%

Section: Aerodynamic Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Influence of Icing on the Modal Behavior of Wind Turbine Blades

2016

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“…Etemaddar et al [5] investigated the effects of atmospheric ice accumulation on the aerodynamic performance and structural response of a wind turbine, and they predicted that the relative change in mean value is bigger than the change in standard deviation for most of the response quantities (rotor speed, torque, power, thrust and structural loads) of the iced blade. Alsabagh et al [6] considered different ice mass distributions as defined in the ISO 12494:2001 standard on a multi megawatt wind turbine blade. They analyzed the influences of ice mass on natural frequencies and dynamic magnification factors (ratio of the dynamic deflection to static deflection), by considering only the mass changes in the blade.…”

Section: Introductionmentioning

confidence: 99%

Investigating How an Artificial Neural Network Model Can Be Used to Detect Added Mass on a Non-Rotating Beam Using Its Natural Frequencies: A Possible Application for Wind Turbine Blade Ice Detection

2017

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Structures vibrate with their natural frequencies when disturbed from their equilibrium position. These frequencies reduce when an additional mass accumulates on their structures, like ice accumulation on wind turbines installed in cold climate sites. The added mass has two features: the location and quantity of mass. Natural frequencies of the structure reduce differently depending on these two features of the added mass. In this work, a technique based on an artificial neural network (ANN) model is proposed to identify added mass by training the neural network with a dataset of natural frequencies of the structure calculated using different quantities of the added mass at different locations on the structure. The proposed method is demonstrated on a non-rotating beam model fixed at one end. The length of the beam is divided into three zones in which different added masses are considered, and its natural frequencies are calculated using a finite element model of the beam. ANN is trained with this dataset of natural frequencies of the beam as an input and corresponding added masses used in the calculations as an output. ANN approximates the non-linear relationship between these inputs and outputs. An experimental setup of the cantilever beam is fabricated, and experimental modal analysis is carried out considering a few added masses on the beam. The frequencies estimated in the experiments are given as an input to the trained ANN model, and the identified masses are compared against the actual masses used in the experiments. These masses are identified with an error that varies with the location and the quantity of added mass. The reason for these errors can be attributed to the unaccounted stiffness variation in the beam model due to the added mass while generating the dataset for training the neural network. Therefore, the added masses are roughly estimated. At the end of the paper, an application of the current technique for detecting ice mass on a wind turbine blade is studied. A neural network model is designed and trained with a dataset of natural frequencies calculated using the finite element model of the blade considering different ice masses. The trained network model is tested to identify ice masses in four test cases that considers random mass distributions along the blade. The neural network model is able to roughly estimate ice masses, and the error reduces with increasing ice mass on the blade.

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“…Lorenzo et al (2013) investigated the influence of ice mass on the natural frequencies of 10 NREL 5 MW turbine by extracting model parameters through operational modal analysis. Alsabagh et al (2015) considered different ice mass distributions as defined in ISO 12494:2001ISO 12494: (2001 standard on a multi megawatt wind turbine blade. They analyzed the influences of ice mass on natural frequencies and dynamic magnification factors, by considering only the mass changes in the blade.…”

mentioning

confidence: 99%

Detection of ice mass based on the natural frequencies of wind turbine blade

Gantasala

Luneno

Aidanpää

2016

Preprint

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Abstract. Wind turbines installed in the cold climate regions accumulate ice on the blades affecting their aeroelastic behavior and turbine power output. It is essential to detect icing in the early stages to start the deicing systems so that the losses due to icing can be minimized. The increase in mass distribution of the blade due to icing reduces its natural frequencies and how much these frequencies reduce depends on the amount of ice mass and their location on the blade. Ice detection systems like BLADEControl (Bosch Rexroth) and fos4blade IceDetection (fos4X) systems detect ice based on the 10 deviations in blade natural frequencies, but cannot identify the location and amount of ice mass. In this work, the authors propose a method to detect average ice mass accumulated along three zones defined along the blade based on its natural frequencies using Artificial Neural Networks (ANN). Different ice masses are added on a wind turbine blade and their natural frequencies are simulated using a finite element model of the blade vibrations. ANN is trained with the natural frequencies of the iced blade as inputs and corresponding ice mass distributions used in the three zones as outputs. ANN 15 approximates the non-linear function between inputs and outputs in the training process. After training with a large data set of possible ice mass distributions, ANN model can be used to predict ice mass distributions in the three zones for any set of natural frequencies (input to ANN) of the iced blade. NREL 5 MW wind turbine blade is considered in this study to demonstrate the proposed method. Various cases of ice mass distributions are tested by the trained ANN model and the predicted ice mass distributions are compared against actual ice mass distribution values. ANN model is able to predict ice 20 mass distributions exactly if they are similar to the ice mass distributions used in the training data, otherwise the ice masses are predicted with an error. Overall, the proposed method is able to approximately detect average ice mass accumulated along the blade which is not possible before.

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Atmospheric Ice Loading and its Impact on Natural Frequencies of Wind Turbines

Cited by 11 publications

References 21 publications

Influence of Icing on the Modal Behavior of Wind Turbine Blades

Influence of Icing on the Modal Behavior of Wind Turbine Blades

Investigating How an Artificial Neural Network Model Can Be Used to Detect Added Mass on a Non-Rotating Beam Using Its Natural Frequencies: A Possible Application for Wind Turbine Blade Ice Detection

Detection of ice mass based on the natural frequencies of wind turbine blade

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