RB
R. Bos
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This paper presents a heuristic building block for wind farm layout optimization algorithms. For each pair of wake-interacting turbines, a vector is defined. Its magnitude is proportional to the wind speed deficit of the waked turbine due to the waking turbine. Its direction is chosen from the inter-turbine, downwind, or crosswind directions. These vectors can be combined for all waking or waked turbines and averaged over the wind resource to obtain a vector, a "pseudo-gradient", that can take the role of gradient in classical gradient-following optimization algorithms. A proof-of-concept optimization algorithm demonstrates how such vectors can be used for computationally efficient wind farm layout optimization. Results for various sites, both idealized and realistic, illustrate the types of layout generated by the proof-of-concept algorithm. These results provide a basis for a discussion of the heuristic's strong points-speed, competitive reduction in wake losses, and flexibility-and weak points-partial blindness to the objective and dependence on the starting layout. The computational speed of pseudo-gradient-based optimization is an enabler for analyses that would otherwise be computationally impractical. Pseudo-gradient-based optimization has already been used by industry in the design of large-scale (offshore) wind farms.
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This paper presents a heuristic building block for wind farm layout optimization algorithms. For each pair of wake-interacting turbines, a vector is defined. Its magnitude is proportional to the wind speed deficit of the waked turbine due to the waking turbine. Its direction is chosen from the inter-turbine, downwind, or crosswind directions. These vectors can be combined for all waking or waked turbines and averaged over the wind resource to obtain a vector, a "pseudo-gradient", that can take the role of gradient in classical gradient-following optimization algorithms. A proof-of-concept optimization algorithm demonstrates how such vectors can be used for computationally efficient wind farm layout optimization. Results for various sites, both idealized and realistic, illustrate the types of layout generated by the proof-of-concept algorithm. These results provide a basis for a discussion of the heuristic's strong points-speed, competitive reduction in wake losses, and flexibility-and weak points-partial blindness to the objective and dependence on the starting layout. The computational speed of pseudo-gradient-based optimization is an enabler for analyses that would otherwise be computationally impractical. Pseudo-gradient-based optimization has already been used by industry in the design of large-scale (offshore) wind farms.
An important problem in wind turbine design is the prediction of the 50-year load, as set by the IEC 61400-1 Design Load Case (DLC) 1.1. In most cases, designers work with limited simulation budgets and are forced to use extrapolation schemes to obtain the required return level. That this is no easy task is proven by the many studies dedicated to finding the best distribution and fitting method to capture the extreme load behavior as well as possible. However, the issue that is often overlooked is the effect that the sheer uncertainty around the 50-year load has on a design process. In this paper, we use a collection of 96 years’ worth of extreme loads to perform a large number of hypothetical design problems. The results show that, even with sample sizes
exceeding N D 103 10 min extremes, designs are often falsely rejected or falsely accepted based on an overor underpredicted 50-year load. Therefore, designers are advised to be critical of the outcome of DLC 1.1 and should be prepared to invest in large sample sizes. ...
exceeding N D 103 10 min extremes, designs are often falsely rejected or falsely accepted based on an overor underpredicted 50-year load. Therefore, designers are advised to be critical of the outcome of DLC 1.1 and should be prepared to invest in large sample sizes. ...
An important problem in wind turbine design is the prediction of the 50-year load, as set by the IEC 61400-1 Design Load Case (DLC) 1.1. In most cases, designers work with limited simulation budgets and are forced to use extrapolation schemes to obtain the required return level. That this is no easy task is proven by the many studies dedicated to finding the best distribution and fitting method to capture the extreme load behavior as well as possible. However, the issue that is often overlooked is the effect that the sheer uncertainty around the 50-year load has on a design process. In this paper, we use a collection of 96 years’ worth of extreme loads to perform a large number of hypothetical design problems. The results show that, even with sample sizes
exceeding N D 103 10 min extremes, designs are often falsely rejected or falsely accepted based on an overor underpredicted 50-year load. Therefore, designers are advised to be critical of the outcome of DLC 1.1 and should be prepared to invest in large sample sizes.
exceeding N D 103 10 min extremes, designs are often falsely rejected or falsely accepted based on an overor underpredicted 50-year load. Therefore, designers are advised to be critical of the outcome of DLC 1.1 and should be prepared to invest in large sample sizes.
Wind turbines are the largest rotating machines on the planet, operating in some of the most remote and hostile areas. During a lifespan of several decades, they have to withstand storms, waves, and gusts (and preferably produce electricity in the process). Yet, designers cannot make them too strong. Every additional kilogram of mass has to be manufactured, transported, and installed, and has a price tag that is directly added to the cost of energy. Optimizing structures, however, is very challenging. It requires designers to predict the highest loads on wind turbines, after they have spent decades in a turbulent wind climate. This PhD research focused on extreme wind gusts, which are among the most severe events that a wind turbine can encounter. The concept of a gust is extended from its common perception to a complete mathematical model of three-dimensional velocity fields. These fields have proven to be very valuable to the design process, since they offer a unique insight into the conditions in which high loads are triggered. Moreover, they can be used to efficiently predict the long-term loads with low uncertainty. At the same time, this physical gust model helps to answer an important question for future wind turbines: what happens when rotor blades outgrow the size of the most severe gusts in the atmosphere?
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Wind turbines are the largest rotating machines on the planet, operating in some of the most remote and hostile areas. During a lifespan of several decades, they have to withstand storms, waves, and gusts (and preferably produce electricity in the process). Yet, designers cannot make them too strong. Every additional kilogram of mass has to be manufactured, transported, and installed, and has a price tag that is directly added to the cost of energy. Optimizing structures, however, is very challenging. It requires designers to predict the highest loads on wind turbines, after they have spent decades in a turbulent wind climate. This PhD research focused on extreme wind gusts, which are among the most severe events that a wind turbine can encounter. The concept of a gust is extended from its common perception to a complete mathematical model of three-dimensional velocity fields. These fields have proven to be very valuable to the design process, since they offer a unique insight into the conditions in which high loads are triggered. Moreover, they can be used to efficiently predict the long-term loads with low uncertainty. At the same time, this physical gust model helps to answer an important question for future wind turbines: what happens when rotor blades outgrow the size of the most severe gusts in the atmosphere?
Lidars have gained a lot of popularity in the field of wind energy, partly because of their potential to be used for wind turbine control. By scanning the oncoming wind field, any threats such as gusts can be detected early and high loads can be avoided by taking preventive actions. Unfortunately, lidars suffer from some inherent weaknesses that hinder measuring gusts; e.g., the averaging of high-frequency fluctuations and only measuring along the line of sight). This paper proposes a method to construct a useful signal from a lidar by fitting a homogeneous Gaussian velocity field to a set of scattered measurements. The output signal, an along-wind force, acts as a measure for the damaging potential of an oncoming gust and is shown to agree with the rotor-effective wind speed (a similar control input, but derived directly from the wind turbine’s shaft torque). Low data availability and the disadvantage of not knowing the velocity between the lidar beams is translated into uncertainty and integrated in the output signal. This allows a designer to establish a control strategy based on risk, with the ultimate goal to reduce the extreme loads during operation.
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Lidars have gained a lot of popularity in the field of wind energy, partly because of their potential to be used for wind turbine control. By scanning the oncoming wind field, any threats such as gusts can be detected early and high loads can be avoided by taking preventive actions. Unfortunately, lidars suffer from some inherent weaknesses that hinder measuring gusts; e.g., the averaging of high-frequency fluctuations and only measuring along the line of sight). This paper proposes a method to construct a useful signal from a lidar by fitting a homogeneous Gaussian velocity field to a set of scattered measurements. The output signal, an along-wind force, acts as a measure for the damaging potential of an oncoming gust and is shown to agree with the rotor-effective wind speed (a similar control input, but derived directly from the wind turbine’s shaft torque). Low data availability and the disadvantage of not knowing the velocity between the lidar beams is translated into uncertainty and integrated in the output signal. This allows a designer to establish a control strategy based on risk, with the ultimate goal to reduce the extreme loads during operation.
An important yet difficult task in the design of wind turbines is to assess the extreme load behaviour, most notably finding the 50-year load. Where existing methods often focus on ways to extrapolate from small sample sizes, this paper proposes a different approach. It combines generating constrained gusts in turbulence fields, Delaunay tessellation to assign probabilities and a genetic algorithm to find the desired load in an efficient way. The individual parts of the method are verified and the results are compared to both crude Monte Carlo and importance sampling. We found that using a genetic algorithm is a promising approach to find the 50-year load, with only a small number of load cases (~103) to be evaluated and requiring no user input but an appropriate fitness function.
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An important yet difficult task in the design of wind turbines is to assess the extreme load behaviour, most notably finding the 50-year load. Where existing methods often focus on ways to extrapolate from small sample sizes, this paper proposes a different approach. It combines generating constrained gusts in turbulence fields, Delaunay tessellation to assign probabilities and a genetic algorithm to find the desired load in an efficient way. The individual parts of the method are verified and the results are compared to both crude Monte Carlo and importance sampling. We found that using a genetic algorithm is a promising approach to find the 50-year load, with only a small number of load cases (~103) to be evaluated and requiring no user input but an appropriate fitness function.