A U.S.-based renewable energy company has successfully completed field trials that used data from a nacelle-mounted Lidar wind sensor to correct a yaw error and increase total energy production from an errant wind turbine. Eliminating the yaw error let the company improve annual energy production (AEP) from the turbine by 1.8%.
“We decided look closer at this particular turbine because it was not performing to its OEM supplied power curve,” says FirstWind wind resource manager Cegeon Chan.”We looked for a probably cause and found the yaw error.” However, analysis of the SCADA data—including measurements from a nearby met mast and the vane measurement from the wind turbine—was insufficient to determine the correction needed.
“The conventional, turbine-based anemometer is best for measuring cut in and cut-out speed,” says NRG Renewable spokesman Evan Osler. “The met mast is good but few wind farms have upwind met masts and if so, they are often far upwind.” So the company decided to test a Wind Iris on the turbine.
The laser based wind sensor collected wind speed and direction data ahead of the turbine for 30 days. Analysis showed an average yaw error of seven degrees. A correction factor was then applied to the yaw measurement and 15 additional days of measurement using the Wind Iris revealed that the yaw error had been eliminated.
The improvement, calculated with a power-curve estimation model using Wind Iris data, was independently verified by comparing the relative increase in turbine production with nearby turbines that had not been optimized. While project-specific revenue impacts for this case are not available, increasing the AEP of 2MW turbines by 1.8% while selling power for $60/MWh would earn $7,767 more, or from 20 turbines, a $155,340 benefit. WPE