MH
M.A. Heine
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1
A Learning-Based Algorithm for Modeling Blade Degradation in Wind Turbines
By Decoupling Lift and Drag Coefficient for a Physics-Informed Approach
As the world shifts toward sustainable energy, offshore wind power is becoming increasingly important because of its ability to capture stronger and more consistent winds. However, offshore wind turbines operate in harsh environmental conditions, exposing wind turbine blades to loads. These conditions accelerate blade degradation through mechanisms such as leading-edge erosion, which disrupt airflow, increase drag, and decrease lift, ultimately reducing the aerodynamic efficiency and energy output of the turbine. Over time, this degradation alters the aerodynamic characteristics of the turbine, posing
significant challenges to control strategies.
This work addresses this challenge by proposing a physics-informed, learning-based framework for modeling blade degradation in wind turbines. Unlike earlier studies that model degradation as a scalar reduction in performance (typically as a uniform downscaling of the power coefficient CP ), the method developed here decouples the impact on lift and drag coefficients. This approach enables the reconstruction of complex, shape-altering changes in the CP (λ) curve. The result is a generalized degradation framework, parameterized through sensitivity coefficients k1 and k2, that represents the degradation
trajectory in a degradation severity space. These sensitivity coefficients are not static but evolve over time, enabling a dynamic and interpretable representation of aerodynamic degradation.
The framework is implemented into the Wind Speed Estimation - Tip-Speed Ratio (WSE-TSR) control scheme, a closed-loop scheme that is capable of learning. Within this control scheme, the degraded
turbine dynamics are compared with an internal reference model to derive estimation errors, which in turn are used to iteratively update the internal degradation parameters. The result is a correcting controller that learns degradation severity and tracks the evolution of the CP (λ) curve over time.
Initial simulation results, based on modeled degradation paths and a realistic leading-edge erosion case,
revealed limitations in the learning process caused by systematic errors in the control scheme. These errors led to discrepancies between the estimated and actual degraded aerodynamic profiles. However, after identifying and analytically correcting for this model error, the learning algorithm demonstrated significantly improved performance. It was then able to reconstruct accurate degraded CP (λ) curves for moderate and severe degradation scenarios. A discussion is provided on the origin of the learning
inconsistencies and how they could be further investigated. The framework underscores the potential of the framework to support integration into digital twin systems for adaptive control in a degraded state.
By combining physics-based simulation, parameter learning, and a control scheme, this thesis presents a framework that enhances our ability to monitor and interpret degradation in offshore wind turbines. In particular, it provides physics-based insight into how degradation mechanisms, such as leading-edge
erosion, affect aerodynamic performance through changes in lift and drag. This approach contributes to the development of more accurate and adaptive digital twin models that can account for suboptimal aerodynamic performance due to blade degradation. ...
significant challenges to control strategies.
This work addresses this challenge by proposing a physics-informed, learning-based framework for modeling blade degradation in wind turbines. Unlike earlier studies that model degradation as a scalar reduction in performance (typically as a uniform downscaling of the power coefficient CP ), the method developed here decouples the impact on lift and drag coefficients. This approach enables the reconstruction of complex, shape-altering changes in the CP (λ) curve. The result is a generalized degradation framework, parameterized through sensitivity coefficients k1 and k2, that represents the degradation
trajectory in a degradation severity space. These sensitivity coefficients are not static but evolve over time, enabling a dynamic and interpretable representation of aerodynamic degradation.
The framework is implemented into the Wind Speed Estimation - Tip-Speed Ratio (WSE-TSR) control scheme, a closed-loop scheme that is capable of learning. Within this control scheme, the degraded
turbine dynamics are compared with an internal reference model to derive estimation errors, which in turn are used to iteratively update the internal degradation parameters. The result is a correcting controller that learns degradation severity and tracks the evolution of the CP (λ) curve over time.
Initial simulation results, based on modeled degradation paths and a realistic leading-edge erosion case,
revealed limitations in the learning process caused by systematic errors in the control scheme. These errors led to discrepancies between the estimated and actual degraded aerodynamic profiles. However, after identifying and analytically correcting for this model error, the learning algorithm demonstrated significantly improved performance. It was then able to reconstruct accurate degraded CP (λ) curves for moderate and severe degradation scenarios. A discussion is provided on the origin of the learning
inconsistencies and how they could be further investigated. The framework underscores the potential of the framework to support integration into digital twin systems for adaptive control in a degraded state.
By combining physics-based simulation, parameter learning, and a control scheme, this thesis presents a framework that enhances our ability to monitor and interpret degradation in offshore wind turbines. In particular, it provides physics-based insight into how degradation mechanisms, such as leading-edge
erosion, affect aerodynamic performance through changes in lift and drag. This approach contributes to the development of more accurate and adaptive digital twin models that can account for suboptimal aerodynamic performance due to blade degradation. ...
As the world shifts toward sustainable energy, offshore wind power is becoming increasingly important because of its ability to capture stronger and more consistent winds. However, offshore wind turbines operate in harsh environmental conditions, exposing wind turbine blades to loads. These conditions accelerate blade degradation through mechanisms such as leading-edge erosion, which disrupt airflow, increase drag, and decrease lift, ultimately reducing the aerodynamic efficiency and energy output of the turbine. Over time, this degradation alters the aerodynamic characteristics of the turbine, posing
significant challenges to control strategies.
This work addresses this challenge by proposing a physics-informed, learning-based framework for modeling blade degradation in wind turbines. Unlike earlier studies that model degradation as a scalar reduction in performance (typically as a uniform downscaling of the power coefficient CP ), the method developed here decouples the impact on lift and drag coefficients. This approach enables the reconstruction of complex, shape-altering changes in the CP (λ) curve. The result is a generalized degradation framework, parameterized through sensitivity coefficients k1 and k2, that represents the degradation
trajectory in a degradation severity space. These sensitivity coefficients are not static but evolve over time, enabling a dynamic and interpretable representation of aerodynamic degradation.
The framework is implemented into the Wind Speed Estimation - Tip-Speed Ratio (WSE-TSR) control scheme, a closed-loop scheme that is capable of learning. Within this control scheme, the degraded
turbine dynamics are compared with an internal reference model to derive estimation errors, which in turn are used to iteratively update the internal degradation parameters. The result is a correcting controller that learns degradation severity and tracks the evolution of the CP (λ) curve over time.
Initial simulation results, based on modeled degradation paths and a realistic leading-edge erosion case,
revealed limitations in the learning process caused by systematic errors in the control scheme. These errors led to discrepancies between the estimated and actual degraded aerodynamic profiles. However, after identifying and analytically correcting for this model error, the learning algorithm demonstrated significantly improved performance. It was then able to reconstruct accurate degraded CP (λ) curves for moderate and severe degradation scenarios. A discussion is provided on the origin of the learning
inconsistencies and how they could be further investigated. The framework underscores the potential of the framework to support integration into digital twin systems for adaptive control in a degraded state.
By combining physics-based simulation, parameter learning, and a control scheme, this thesis presents a framework that enhances our ability to monitor and interpret degradation in offshore wind turbines. In particular, it provides physics-based insight into how degradation mechanisms, such as leading-edge
erosion, affect aerodynamic performance through changes in lift and drag. This approach contributes to the development of more accurate and adaptive digital twin models that can account for suboptimal aerodynamic performance due to blade degradation.
significant challenges to control strategies.
This work addresses this challenge by proposing a physics-informed, learning-based framework for modeling blade degradation in wind turbines. Unlike earlier studies that model degradation as a scalar reduction in performance (typically as a uniform downscaling of the power coefficient CP ), the method developed here decouples the impact on lift and drag coefficients. This approach enables the reconstruction of complex, shape-altering changes in the CP (λ) curve. The result is a generalized degradation framework, parameterized through sensitivity coefficients k1 and k2, that represents the degradation
trajectory in a degradation severity space. These sensitivity coefficients are not static but evolve over time, enabling a dynamic and interpretable representation of aerodynamic degradation.
The framework is implemented into the Wind Speed Estimation - Tip-Speed Ratio (WSE-TSR) control scheme, a closed-loop scheme that is capable of learning. Within this control scheme, the degraded
turbine dynamics are compared with an internal reference model to derive estimation errors, which in turn are used to iteratively update the internal degradation parameters. The result is a correcting controller that learns degradation severity and tracks the evolution of the CP (λ) curve over time.
Initial simulation results, based on modeled degradation paths and a realistic leading-edge erosion case,
revealed limitations in the learning process caused by systematic errors in the control scheme. These errors led to discrepancies between the estimated and actual degraded aerodynamic profiles. However, after identifying and analytically correcting for this model error, the learning algorithm demonstrated significantly improved performance. It was then able to reconstruct accurate degraded CP (λ) curves for moderate and severe degradation scenarios. A discussion is provided on the origin of the learning
inconsistencies and how they could be further investigated. The framework underscores the potential of the framework to support integration into digital twin systems for adaptive control in a degraded state.
By combining physics-based simulation, parameter learning, and a control scheme, this thesis presents a framework that enhances our ability to monitor and interpret degradation in offshore wind turbines. In particular, it provides physics-based insight into how degradation mechanisms, such as leading-edge
erosion, affect aerodynamic performance through changes in lift and drag. This approach contributes to the development of more accurate and adaptive digital twin models that can account for suboptimal aerodynamic performance due to blade degradation.
River estuaries are strongly modified by human interventions, such as dredging. For example the Ems river (Germany and Netherlands) has been deepened between in 1965 and 2005. As a result the sediment (sand and dirt) concentration has increased enormously \cite{Ems_data}. The muddiness of this sediment blocks sunlight and decreases the friction of the water with the bed level. The transport of sediment into the river is mainly caused by non-linearity in tidal waves. As a result of the elevation of the bed level and the friction change of the Ems river, the amplitude of the M2 and M4 tide has changed, which was concluded in previous research. In this study the M2 and M4 tide are studied individually, subjected to deepening, widening and friction change. The main research question in this study is '\textit{How does the propagation of the M2 and M4 tide change, subjected to deepening, change in width and change in friction?}' The result to this question can be applied to tidal rivers. To illustrate this, the results are compared to historical data of the Ems river. The sub question in this study is; '\textit{Can historical observations of the amplitude of the M2 and M4 tides in the Ems river over the years be explained with the change in propagation of the M2 and M4 tides, subjected to deepening and friction change?}'.
The questions are answered with help of a model. The model is constructed with the one dimensional water equations, where boundary conditions are used. Next the equations can be used in two cases. In the first case, which is used to answer the main question, the variables width, depth and friction of the river basin are taken to be length independent variables. In the second case, the width, depth and friction of the river basin are taken to be length dependent variables. This second case is used to model the propagation of the M2 and M4 tide for the Ems river. For the first case an analytical and numerical solution exist. From the error, the optimal grid size for the numerical model is obtained, which is taken as $N=100$. For the second case only a numerical solution exists.
With the model it is concluded that widening and an increasing friction cause a damping effect on the propagation of the M2 and M4 tide. Deepening, however, has a different effect on the M2 and M4 tide. It was seen that for certain value change in depth the M2 tide shows an amplification in amplitude, while the M4 tide shows an damping in amplitude for the same value change. The plots obtained in this part of the study can be used to see what is expected to happen to the amplitude of the M2 and M4 tide when a variable is varied in a tidal river basin. The latter is done for the Ems river.
An important observation made with the $x$-dependent model is that the amplification in amplitude from 1965 to 2005 can be explained by the decreased friction, due to increasing muddiness in the Ems river. Secondly, relatively, it was seen that the amplitude of the M4 tide shows a much greater amplification in amplitude at the beginning of the river than the amplitude of the M2 tide. This could be explained by the different change in amplitude of the M2 and M4 tide subjected to deepening, which was concluded from the $x$-independent model as well. However, the difference in M2 and M4 tide between 1965 and 2005 is not due to deepening on its own. Namely, the change in amplitude due to deepening highly depends on the value of the friction, which is different for the years 1965 and 2005. It is concluded that the exact amplitude of the M2 and M4 tide cannot be predicted with the $x$-independent model. However, it is concluded that the results from the $x$-independent model can be used to predict how the M2 and M4 tide will change due to deepening relatively to each other.
The results of this study are strongly influenced by the assumptions made to derive the model. Before the one dimensional, $x$-independent model was compared to the observations for the Ems river between 1965 and 2005 a few decisions had to be made regarding $x$-dependent to $x$-independent variables. This process needs to be further researched, before applying the x-independent model to other tidal rivers, which is left for further research. ...
The questions are answered with help of a model. The model is constructed with the one dimensional water equations, where boundary conditions are used. Next the equations can be used in two cases. In the first case, which is used to answer the main question, the variables width, depth and friction of the river basin are taken to be length independent variables. In the second case, the width, depth and friction of the river basin are taken to be length dependent variables. This second case is used to model the propagation of the M2 and M4 tide for the Ems river. For the first case an analytical and numerical solution exist. From the error, the optimal grid size for the numerical model is obtained, which is taken as $N=100$. For the second case only a numerical solution exists.
With the model it is concluded that widening and an increasing friction cause a damping effect on the propagation of the M2 and M4 tide. Deepening, however, has a different effect on the M2 and M4 tide. It was seen that for certain value change in depth the M2 tide shows an amplification in amplitude, while the M4 tide shows an damping in amplitude for the same value change. The plots obtained in this part of the study can be used to see what is expected to happen to the amplitude of the M2 and M4 tide when a variable is varied in a tidal river basin. The latter is done for the Ems river.
An important observation made with the $x$-dependent model is that the amplification in amplitude from 1965 to 2005 can be explained by the decreased friction, due to increasing muddiness in the Ems river. Secondly, relatively, it was seen that the amplitude of the M4 tide shows a much greater amplification in amplitude at the beginning of the river than the amplitude of the M2 tide. This could be explained by the different change in amplitude of the M2 and M4 tide subjected to deepening, which was concluded from the $x$-independent model as well. However, the difference in M2 and M4 tide between 1965 and 2005 is not due to deepening on its own. Namely, the change in amplitude due to deepening highly depends on the value of the friction, which is different for the years 1965 and 2005. It is concluded that the exact amplitude of the M2 and M4 tide cannot be predicted with the $x$-independent model. However, it is concluded that the results from the $x$-independent model can be used to predict how the M2 and M4 tide will change due to deepening relatively to each other.
The results of this study are strongly influenced by the assumptions made to derive the model. Before the one dimensional, $x$-independent model was compared to the observations for the Ems river between 1965 and 2005 a few decisions had to be made regarding $x$-dependent to $x$-independent variables. This process needs to be further researched, before applying the x-independent model to other tidal rivers, which is left for further research. ...
River estuaries are strongly modified by human interventions, such as dredging. For example the Ems river (Germany and Netherlands) has been deepened between in 1965 and 2005. As a result the sediment (sand and dirt) concentration has increased enormously \cite{Ems_data}. The muddiness of this sediment blocks sunlight and decreases the friction of the water with the bed level. The transport of sediment into the river is mainly caused by non-linearity in tidal waves. As a result of the elevation of the bed level and the friction change of the Ems river, the amplitude of the M2 and M4 tide has changed, which was concluded in previous research. In this study the M2 and M4 tide are studied individually, subjected to deepening, widening and friction change. The main research question in this study is '\textit{How does the propagation of the M2 and M4 tide change, subjected to deepening, change in width and change in friction?}' The result to this question can be applied to tidal rivers. To illustrate this, the results are compared to historical data of the Ems river. The sub question in this study is; '\textit{Can historical observations of the amplitude of the M2 and M4 tides in the Ems river over the years be explained with the change in propagation of the M2 and M4 tides, subjected to deepening and friction change?}'.
The questions are answered with help of a model. The model is constructed with the one dimensional water equations, where boundary conditions are used. Next the equations can be used in two cases. In the first case, which is used to answer the main question, the variables width, depth and friction of the river basin are taken to be length independent variables. In the second case, the width, depth and friction of the river basin are taken to be length dependent variables. This second case is used to model the propagation of the M2 and M4 tide for the Ems river. For the first case an analytical and numerical solution exist. From the error, the optimal grid size for the numerical model is obtained, which is taken as $N=100$. For the second case only a numerical solution exists.
With the model it is concluded that widening and an increasing friction cause a damping effect on the propagation of the M2 and M4 tide. Deepening, however, has a different effect on the M2 and M4 tide. It was seen that for certain value change in depth the M2 tide shows an amplification in amplitude, while the M4 tide shows an damping in amplitude for the same value change. The plots obtained in this part of the study can be used to see what is expected to happen to the amplitude of the M2 and M4 tide when a variable is varied in a tidal river basin. The latter is done for the Ems river.
An important observation made with the $x$-dependent model is that the amplification in amplitude from 1965 to 2005 can be explained by the decreased friction, due to increasing muddiness in the Ems river. Secondly, relatively, it was seen that the amplitude of the M4 tide shows a much greater amplification in amplitude at the beginning of the river than the amplitude of the M2 tide. This could be explained by the different change in amplitude of the M2 and M4 tide subjected to deepening, which was concluded from the $x$-independent model as well. However, the difference in M2 and M4 tide between 1965 and 2005 is not due to deepening on its own. Namely, the change in amplitude due to deepening highly depends on the value of the friction, which is different for the years 1965 and 2005. It is concluded that the exact amplitude of the M2 and M4 tide cannot be predicted with the $x$-independent model. However, it is concluded that the results from the $x$-independent model can be used to predict how the M2 and M4 tide will change due to deepening relatively to each other.
The results of this study are strongly influenced by the assumptions made to derive the model. Before the one dimensional, $x$-independent model was compared to the observations for the Ems river between 1965 and 2005 a few decisions had to be made regarding $x$-dependent to $x$-independent variables. This process needs to be further researched, before applying the x-independent model to other tidal rivers, which is left for further research.
The questions are answered with help of a model. The model is constructed with the one dimensional water equations, where boundary conditions are used. Next the equations can be used in two cases. In the first case, which is used to answer the main question, the variables width, depth and friction of the river basin are taken to be length independent variables. In the second case, the width, depth and friction of the river basin are taken to be length dependent variables. This second case is used to model the propagation of the M2 and M4 tide for the Ems river. For the first case an analytical and numerical solution exist. From the error, the optimal grid size for the numerical model is obtained, which is taken as $N=100$. For the second case only a numerical solution exists.
With the model it is concluded that widening and an increasing friction cause a damping effect on the propagation of the M2 and M4 tide. Deepening, however, has a different effect on the M2 and M4 tide. It was seen that for certain value change in depth the M2 tide shows an amplification in amplitude, while the M4 tide shows an damping in amplitude for the same value change. The plots obtained in this part of the study can be used to see what is expected to happen to the amplitude of the M2 and M4 tide when a variable is varied in a tidal river basin. The latter is done for the Ems river.
An important observation made with the $x$-dependent model is that the amplification in amplitude from 1965 to 2005 can be explained by the decreased friction, due to increasing muddiness in the Ems river. Secondly, relatively, it was seen that the amplitude of the M4 tide shows a much greater amplification in amplitude at the beginning of the river than the amplitude of the M2 tide. This could be explained by the different change in amplitude of the M2 and M4 tide subjected to deepening, which was concluded from the $x$-independent model as well. However, the difference in M2 and M4 tide between 1965 and 2005 is not due to deepening on its own. Namely, the change in amplitude due to deepening highly depends on the value of the friction, which is different for the years 1965 and 2005. It is concluded that the exact amplitude of the M2 and M4 tide cannot be predicted with the $x$-independent model. However, it is concluded that the results from the $x$-independent model can be used to predict how the M2 and M4 tide will change due to deepening relatively to each other.
The results of this study are strongly influenced by the assumptions made to derive the model. Before the one dimensional, $x$-independent model was compared to the observations for the Ems river between 1965 and 2005 a few decisions had to be made regarding $x$-dependent to $x$-independent variables. This process needs to be further researched, before applying the x-independent model to other tidal rivers, which is left for further research.