Wind farms often operate with just one or two met towers as reference sources, towers which were originally used for the contractual power-performance test. Once turbines are installed, wind information from the fixed met towers is used to monitor production estimates and as an aid to operations and maintenance. However, the met tower may only represent a subsector of the wind turbine array. The demand for operational assessment analysis is being addressed using a combination of advanced mobile ground-based remote sensing instruments and advanced-modeling techniques. An advantage of a mobile, remote-sensing instrument is that it can be moved around and within an operating wind-turbine array to provide the project owner-operators access to specific sector wind data that they can use to assess their operational control strategies and predict specific turbine maintenance needs.

As wind-turbine technology improves, existing wind farms must regularly evaluate whether to invest in a manufacturer’s performance enhancements, such as vortex generators, new SCADA (supervisory control and data acquisition) algorithms, and other modifications. By using a remote-sensing system as a mobile met tower, relative power curves can be developed for pre-upgrade and post-upgrade analysis to better understand the benefits of various improvements under varying wind conditions. After installing the upgrades, monitoring the free-stream wind flow provides better information for determining whether the upgrade produced an improvement or it was simply a ‘better’ wind day. Also, if the blades or software are updated, that might change the transfer function of the nacelle anemometer – making upwind free-stream measurement all the more important.

Additionally, wind-farm operators are investigating the use of forward-looking nacelle mounted LiDAR systems for improved yaw correction, blade-pitch-control strategies, and potentially early gust and ramp-down detection. Several of these nacelle-mounted LiDARs use the Doppler-shift measurement techniques used by ground-based models. However, new direct LiDAR detection methods are in test and early results are promising. Several providers offer such systems. Whether it is necessary to install a forward-looking LiDAR on each turbine or only on a few key turbines to provide the necessary guidance is still under investigation because wind direction variation across a project site will likely influence that decision.

Wake losses

Another loss factor and one of the most important steps in wind-project valuation is the estimation of wake effects. These occur when upwind turbines create turbulence that detracts from downwind turbine performance. Wind-project wake losses make a substantial contribution to understanding wind plant under-performance, and in many cases may be the largest contributor to such under-performance.

 

Remote sensing systems can play a role in measuring these effects at operating wind farms, but can also calibrate and test various advanced-mesoscale models used for wake analysis. Studies conducted by Vaisala’s 3TIER Services and WindLogics have shown the skill of the Weather Research and Forecasting (WRF) model in this application.

Several hypotheses explain why the WRF model provides a more accurate estimate of turbine wakes. Foremost, WRF takes into account the variability of the atmospheric vertical-temperature profile, which strongly controls the amount of turbulence available to dissipate turbine wakes. Standard wake models assume neutral atmospheric stability. Neutrally stable flows let wakes dissipate more rapidly than they would in the more strongly stable conditions typical of the nocturnal boundary layer. Although WRF is more computationally expensive, the results of these studies show that using WRF to model wind turbines and nearby existing or planned wind plants is the best method to ensure that the total waking effect has been appropriately captured.

These findings are significant because computer simulations are the only option for evaluating wakes prior to construction. Advanced mesoscale models also offer flexibility for operational optimization because they can quickly provide a modeled wind profile at each turbine with wake losses incorporated, which can be compared with actual production on an ongoing basis – all without the expense of installing, maintaining, or collecting measured data at each turbine.

Working with the grid

Finally, a wind project’s generation must be integrated predictably into the power grid for system reliability and to fulfill its expected financial return. Regional independent system operators (ISOs) thus require wind generation facilities to provide forecasting data, which they use to understand how much power a wind generation facility will be supplying to the grid on a day-ahead and hour-ahead basis.

ISOs also need wind forecasts to predict so-called “ramp events.” This is a period of rapid change in wind-farm production caused by increases or decreases in wind speed over a few hours. Different grid systems operate in their own unique wind regime and therefore, have different definitions of wind ramps. ISOs and wind-farm operators must have operating strategies in place to deal with impacts these events have on generation capacity and system reliability. Energy traders also use wind forecasts to determine the amount of power that will be available at any given time, because this will drive prices up or down.

Accurately forecasting wind-energy production requires measurements and advanced computer modeling. First, measurements of the current wind resource are recorded at and around the wind-farm site. These data along with that from national and global weather service forecasts are combined with high-resolution Numerical Weather Prediction (NWP) models such as WRF. Forecasts are based on these weather models along with knowledge of the capacity of the turbines and their predicted power output in various wind conditions.

NWP models are typically initialized with gridded data sets from the national weather service. By nature, these are at least one hour old by the time they can be accessed, ingested, and processed by the modeling software. Therefore, in many locations, forecasting in the hour-ahead time frame can be improved with onsite wind observations. Analysis of local weather conditions using a network of ground-based remote sensors can assist in turning forecast models to site conditions. These mobile wind-measurement systems offer a new source of data to feed today’s robust microclimate forecasting models. By combining remote-sensing measurement technologies with advanced computer-modeling techniques in forecasting, owner-operators can improve the cost competitiveness of wind power generation compared with traditional fossil-fuel generators in electrical markets around the world.

Looking ahead

The wind industry is deploying more advanced-measurement technology, turbine-performance enhancements, and forecasting systems to optimize wind-power generation for better grid integration. Ultimately, these will improve the value of wind energy in a utility’s generation portfolio. Improvements to remote sensing and advanced computer modeling enhance operational turbine performance. These advancements include new SoDAR and LiDAR instruments developed for studying active wind farm conditions, such as SCADA integrated permanent SoDARs and nacelle-mounted LiDARs. Used either as a complement to met tower data or as a stand-alone tool, remote sensing systems and advanced modeling software can be used throughout the lifecycle of a wind farm. Through innovation, in hardware and software, wind farm developers and financiers can reduce the uncertainty of wind-power generation and make it a more attractive component of modern energy investment portfolios. WPE&D