The emergence of software and science can turn even complex terrain into productive wind farms.
As potential sites with simple, flat terrain are developed, more wind projects are built in or near forests and complex terrain. These sites—associated with harsh flow conditions—are likely to affect wind-turbine performance. Complex terrain can, for example, induce large amounts of turbulence and high loads on wind turbine blades. As a result, there are increasing concerns about under-performing wind farms with higher than expected maintenance costs, performance drops, and warranty claims. Tools and methods for designing efficient wind farms in complex terrain are available, but there is little awareness of wind software within the industry.
The wind industry defines complex flow as that which affects wind-turbine production or safety. Several parameters, such as wind shear, turbulence intensity, and inflow angle, are useful to quantify flow complexity.
Wind shear characterizes the variation of horizontal wind speed with height. In flat, non-complex terrain wind speed increases with height following a logarithmic or power-law profile. Forests and slopes can significantly perturb such a profile. As wind blows over a forest, it loses momentum as trees resist the wind. Above the canopy, wind shear is typically high with a reduced distance for the wind to reach high speeds aloft. Downwind of forests, areas of recirculation can be generated at tree level, implying high wind shear aloft. Wind shear depends on topographic features as well. As wind blows over a hill, wind shear typically decreases with an increasing altitude, and increases downwind of the hill.
Temperature can also significantly affect wind shear. Heating at the ground induces flow motion and diverts wind shear from a smooth log-law profile. In some instances this can result in negative wind shear, that is, decreasing wind speed with height. High or low values of wind shear usually imply increased fatigue loading, reduced power output, and reduced availability. If not properly assessed, high shear can lead to underperformance due to not meeting a turbine’s power curve, higher than expected shut-downs, or even failure.
Turbulence intensity is a measure of wind-speed variation in space and time. Turbulence is generated by trees: the energy of flow converts into motion of leaves and branches within the canopy, which in response perturbs the flow at canopy level. Turbulence is also absorbed by trees. One effect of a canopy is to dissipate larger eddies into smaller ones. These perturbations are generated at canopy level, and transported up and down by the mean flow. This is the reason tree-generated turbulence can be felt kilometers downwind of forests at hub height.
Topography and temperature also affect turbulence intensity. Turbulence is typically low at hill tops and higher downwind, where the flow is more perturbed.
Diurnal and seasonal variations of turbulence can be significant. In some cases, turbulence intensity can double in 12 hours. The effects of high turbulence on wind turbines are similar to the ones previously mentioned for wind shear, expect increased fatigue loading, reduced power output, and reduced availability.
In flat terrain, wind typically reaches turbines perpendicular to the rotor. When the wind blows up a steep slope, it follows the slope close to the ground, typically reaching the rotor perpendicularly and not at an angle. This is the inflow angle. The IEC 61400-11 recommends that values of an inflow angle are within ±8°, which is often not the case when turbines are planned for the vicinity of steep slopes. Values of wind shear outside of the ±8° limits usually imply increased fatigue loading and reduced power output. The application of the IEC standard can lead to sector management, e.g. turbine curtailment or in extreme cases turbine shutdown and, therefore, reduced wind energy yield.
When the wind blows over a hill that is steep enough, a recirculation can be generated downwind of this hill. Trees on the hill increase the chances of recirculation for a given slope. While recirculations combine high inflow angles, turbulence, and wind shear, they are a definite no-go.
Handling complex flow
Steep slopes, forestry, and thermal effects can therefore lead to turbine underproduction and failure. However, wind software can predict these phenomena and mitigate their impact on wind energy yield.
Assessing complex flow
A first step assesses the terrain complexity. Although the use of RIX (Ruggedness Index) values can help assess complex terrain, it is not always readily available to investors or developers and does not take in to account the effect of land cover on flow complexity. In response, Natural Power has developed a complexity assessment applet that is freely available on the company’s website. To provide an estimation of flow complexity, the applet simply requires turbine locations, which can be uploaded as Google Earth place marks, and a simple estimation of forest coverage in the area of interest. The applet then computes a complexity index, taking into account topographic features and approximate land cover. It provides visual results in a summary report that summarizes findings, recommendations, and the next development steps. On-site measurements can also provide an indication of flow complexity. High values of turbulence or wind shear can, for instance, indicate complex flow at a specific location. The amplitude of daily or monthly variations should be accounted for and can indicate the presence of thermal effects.
Complex terrain is often characterized by large variations in wind flow over short distances. Therefore, in such terrain, a measurement mast located at the center of the site is usually not representative of all turbine locations. Wind flow uncertainty can be minimized by increasing the number of masts, but this approach quickly becomes expensive.
Portable wind sensors, such as lidars or sodars, can assess flow conditions at several locations. However, wind speeds measured by these devices can be biased in case of complex flow. In such instances, computer modeling can convert lidar or sodar measurements to cup-anemometer equivalents.
Unless measurements are scheduled at every planned turbine location, computer modeling the flow is ultimately required to map it over the area of interest.
Performing relevant simulations. If complex flow is not expected on site, linear modeling (WAsP-like) of the flow is usually relevant. As the name suggests, linear models solve linearised, i.e. simplified, versions of the Navier-Stokes equations, which makes them quick and easy to use. Linear models do a good job predicting wind speed over a site provided the terrain is not too complex.
As terrain complexity increases, the assumptions embedded within linear models break down. It is advised to solve the full 3D Navier-Stokes equations for an accurate picture of the flow. CFD (Computational Fluid Dynamics) codes do this.
CFD models have several advantages over linear versions:
• They embed more physics, including a turbulence model, which allows predicting its intensity at each point of the flow field.
• They usually include an advanced canopy model, which means forests are actually modeled as 3D bodies that extract momentum from the flow, absorbing and generating turbulence. This allows a more accurate description of the flow field in the vicinity of forests than simply using a value of roughness at the ground, such as with linear models.
• They are 3D which, among other things, implies they allow modeling of inflow angles and mapping areas of recirculation.
Thermal effects are believed to influence the flow-field. Some recent CFD codes, such as the VENTOS/M code currently in testing, can couple with mesoscale models that are used over larger distances than CFD models cover, and consider thermally-driven flow. The output of these larger models can feed CFD models, provided they simulate thermally-driven flows. Such codes generate a time-series of the flow at any location of interest. This is similar to what measurement masts could measure there, and include diurnal and seasonal variations of flow variables, such as wind shear, turbulence intensity, wind speed, and inflow angle.
These advanced models, however, have drawbacks. First, they imply the use of numerous parameters to which modeled results can be sensitive. This is not necessarily the case for linear models. On top of appropriate input data, these models require proper calibration using measured data before they become representative of reality. This implies a dedicated user with knowledge and experience of measurements and a specific code. Secondly, they require the handling and analysis of numerous variables, which implies a critical mind-set and a good knowledge of fluid mechanics. Thirdly, they imply costly computations, which are more easily run on computer clusters and entail time and investment.
Once CFD computations have been successfully carried out, results must be translated into useful recommendations. At each planned turbine location, key variables to assess include values of turbulence, wind shear, inflow angle, and wind speed. The value can be compared to standard limits for choosing an appropriate turbine and locations.
The reasons for improper values (topography, forestry) can often be identified, which allow implementations and appropriate mitigation measures where possible. Managing tree height can often adjust wind shear within acceptable bounds. For more representative assessments, the impact of tree growth and felling on turbine suitability and wind energy yield can be computed throughout the entire wind-farm lifetime. If topography is the culprit, wind-sector management or turbine layout redesign are usually more appropriate, although topographic management has been seen on specific sites such as quarries. Wind software also allows generating maps showing areas where turbine operation is not recommended, and then coupled with wind speed maps to optimize turbine layouts.
It is preferable to make this information available at the first stages of a wind project because it results in more efficient wind farms, where production is maximized and machine fatigue is minimized. Unfortunately, CFD computations are frequently performed after building a wind farm and detecting underperformance. CFD is then used to better understand the reasons for underperformance, and mitigation actions are mainly limited to wind sector management, forest management, and turbine relocation.
Such information on site conditions is nevertheless valuable to turbine manufacturers and investors who require reliable assessment of site conditions for making appropriate decisions and for the most safe and efficient wind farms possible. WPE
By: Claude Abiven/Senior Technical Manager/Natural Power/www.naturalpower.com
Filed Under: Projects, Software