The wake effect in wind energy: a key influence on wind farm performance

In the field of wind energy, a phenomenon of great relevance is known as the wake effect in wind farms. This phenomenon occurs due to the interaction between wind turbines, which affects the performance and energy efficiency of the wind farm. This disturbed airflow can lead to a decrease in energy production and increased load on the turbine components. In this article, we will explore in detail how the wake effect influences wind energy production, examining how turbulence and the strategic arrangement of wind turbines can mitigate its detrimental effects.

What is the wake effect in wind energy?

The wake effect in wind energy occurs when the wind flow is influenced by the presence of a wind turbine, which impacts nearby turbines. When the wind encounters a wind turbine, it creates a zone of low pressure behind it, resulting in slower and less energetic wind for the subsequent turbines. This situation reduces the efficiency and performance of downstream turbines, known as the wake effect.

Optimizing the arrangement of wind turbines is crucial to maximize the utilization of available space in a wind farm. On one hand, it is necessary to maintain an appropriate spacing between turbines to avoid the influence of wake shadows and generated turbulence, which can reduce the overall production of the wind farm. On the other hand, the turbines should be positioned close enough to optimize the available area and minimize the costs of the internal medium-voltage grid of the wind farm, as well as energy losses in the grid.

The design of wind farms poses a challenge due to the phenomenon of multiple wakes. Calculating the wake effect is a significant issue in wind farms and requires accurate modeling to minimize power losses caused by the wake effect both at close and far distances.

In this video, we will explore how Siemens Gamesa Renewable Energy is maximizing wind energy production through wake optimization. We will delve into their innovative approach to strategically placing wind turbines and utilizing cutting-edge technology, enabling them to generate more energy efficiently and profitably. With the use of NVIDIA Modulus and Omniverse, designers in the wind energy industry have the ability to merge traditional methods with physics-based super-resolution artificial intelligence models. This allows for the generation of high-resolution simulation data at significantly faster rates, resulting in the creation of more precise and detailed engineering wake models.

Impact of the wake effect on the performance of wind farms

The impact of the wake effect on a wind farm is significant and can have several consequences on the efficiency and overall performance of the system. Below are some of the main impacts:

a) Decrease in Energy Production:

The wake effect causes a reduction in the speed and energy of the wind reaching the downstream wind turbines due to the upstream wind turbines. This diminishes the energy production of the downstream wind turbines as they receive a less powerful wind flow. Moreover, the effect can be cumulative and detrimental to the wind turbines in the last row of the distribution within the wind farm.

b) Loss of Efficiency:

Due to the decrease in wind speed, wind turbines affected by the wake effect operate at suboptimal speeds. This results in a reduction in energy conversion efficiency, as they fail to fully harness the available wind potential. Efficiency (ɳ) can be defined as:

Where is the measured power output of turbine , is la power output for free stream conditions, and is the number of turbines.

c) Uneven Wear of Wind Turbines:

The wake effect can also lead to uneven wear on the turbines within the wind farm. The downstream wind turbines experience fluctuating wind loads and turbulence, meaning that the wind speed is not evenly distributed across the entire blade sweep area. This can result in increased mechanical wear and shorten the lifespan of the wind turbines.

d) Limitations in Wind Turbine Placement:

The wake effect imposes restrictions on the optimal placement of wind turbines within a wind farm. It is necessary to strike a balance between maximizing energy production and minimizing the impact of wake effect. This involves considering the distance and configuration of the turbines to avoid interference between the generated wakes. Moreover, it heavily depends on the geography of the installation site, as a particular location on the terrain may be optimal for wind resource, but not suitable for tower installation.

Criteria for Wind Turbine Distribution

Wind turbines are arranged in one or multiple alignments perpendicular to the most energetic component of the wind, as indicated by the energy wind rose. The following suggestions are commonly used as criteria for spacing between turbines:

• For the spacing between turbines within the same alignment, a distance ranging from 2 to 3 times the rotor diameter (D) is typically established.

• For the spacing between alignments, a distance of 6 to 8 times the rotor diameter (D) is applied.

The spacing between turbines is determined based on the “wind shadow” they may create, which depends on the prevailing wind direction.

In Figure 3, the distribution of 48 wind turbines in a 120 MW wind farm is shown with a row orientation of 25 degrees relative to the reference direction.

Conclusion

Wake effect in wind energy is a crucial factor that influences the performance of wind farms. Its impact results in decreased energy production, efficiency loss, and uneven wear on turbines. To maximize production and minimize negative effects, optimizing the strategic placement of wind turbines is essential. By combining traditional methods with high-resolution artificial intelligence models, such as those offered by NVIDIA Modulus and Omniverse, faster and more accurate simulation data can be generated, enhancing the ability to mitigate wake effect and maximize efficiency in wind energy generation. Proper turbine distribution, considering appropriate spacing and configuration, is crucial for optimizing wind farm performance and minimizing interference among wake patterns.