If you would like to be considered for the Industry Lead Role, please put the project in the subject line and email your resume to Eric@BoulderInnovationCenter.com.
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Summary
Advanced wind farm control systems coupled with near-term (seconds to minutes) weather prediction can increase the efficiency of wind turbine farms as well as reduce operational cost through reduced operational loads on the turbines. This MAP project will analyze advanced control systems coupled with next generation LIDAR and RADAR weather prediction solutions developed at the University of Colorado in conjunction with NREL and CIRES.

Description of Technology
Life-time maintenance costs of wind turbines contribute significantly to the overall cost of wind energy. Measurements of key properties of wind (e.g. turbulence) in real time could be used to optimize the control of wind turbines both individually, and as part of a greater wind farm system. Controlling wind loading to mitigate turbine damage and avoid shut-downs during high wind conditions, optimizing turbine control for greater efficiency of operation, and determining turbine placement within wind farms to coordinate control for the best overall capture and generation of power would help to significantly reduce the end cost of wind power. Light Detection and Ranging (LIDAR) systems are able to measure the speed of incoming wind before it interacts with a wind turbine rotor. These preview wind measurements can be used in feed-forward and feed-back control systems designed to reduce turbine loads by making a significant impact on the generator torque and blade pitch controls. Generator torque control determines how much torque is extracted from the turbine, while blade pitch control is used to mitigate structural loads in high wind conditions.

The degree to which such preview-based control techniques can reduce loads by reacting to turbulence depends on how accurate the incoming wind can be measured. Professor Pao and her research group have been investigating novel controls for variable-speed wind turbines, which are designed to follow wind-speed variations to maximize aerodynamic efficiency. This allows for the mitigation of the sources of the severe vibration and loading on the turbine blades and support structure.

Commercial, NOAA, and NCAR wind profilers use pulse radar technology which cannot observe the lowest 150 m of the atmosphere. Scott Palo and Christopher Williams are working on a Lower Boundary Layer (LBL) radar which is expected to be capable of estimating wind and turbulence from 30 m to over 1 km AGL with a 30 m vertical resolution and 30 second temporal resolutions enabling turbulence estimates from the spectral characteristics at each altitude. This new radar that they call a LFMCW-LBL radar, will use Linear Frequency Modulated Continuous Wave (LFMCW) radar technology enabling fine vertical resolution close to the ground using three simultaneous radar beams pointing ~20° off-vertical and each beam separated in azimuth by 120°.

Potential Benefits
•Improved wind profiling and accuracy for wind turbulence detection
•Significant reduction in turbine maintenance
•Increase in component lifetime and turbine reliability

Technology Development Status
•Pao and team has developed detailed computer models for feedforward and feedback control systems based on LIDAR detection – some testing have been done at the NREL wind test center
•Palo and team are developing the new LFMCW radar and have strong theoretical work completed and working on prototype

If you would like to be considered for the Industry Lead Role, please put the project in the subject line and email your resume to Eric@BoulderInnovationCenter.com.