Windborne to Identify Yaw Misalignments Across a Large Fleet of Wind Turbines
According to a technical report by the National Renewable Energy Laboratory (referring to the multi-year Wind Plant Performance Prediction project), modern wind power plants in the United States were underperforming in their expected annual energy output by 3.5%–4.5%.’ [1] There are many potential causes of wind turbine underperformance. Among these are forms of underperformance caused by rotor-disrupted and/or poorly calibrated nacelle-based wind sensors that in turn feed inaccurate wind data to core wind turbine systems.
By Itay Mor, Boaz Peled, Alex Alpert, and Guy Yakir at First Airborne, Israel
To date, wind turbine underperformance has been mostly cost-prohibitive. The budget required to utilise incumbent first-rate wind measurement technologies has been, in most cases, higher than the potential revenue generated from optimising wind turbine energy generation.
Third-party wind measurement devices, such as nacelle-based lidars, can cost 1.5 to 2 times more than the potential value of the recovered MWh produced by a wind turbine with a 2 to 3MW capacity. As a result, it is common for operators to defer to inaction and absorption of consequential production losses.
In 2018, First Airborne, an Israeli technology company, started developing Windborne – a proprietary, industry-verified aerodynamic payload that deploys from a drone at optimal wind measurement points across a wind farm to record unobstructed (non-waked) wind data. A flying wind mast in essence.
The robotic system aims to enhance the economic viability of wind turbine performance testing and annual energy production (AEP) recovery efforts through the cost-effectiveness derived from the device’s mobility and capability to extract wind data without physically interfacing with wind turbines.
Yaw Misalignment Campaign in Texas
Starting in August 2023, First Airborne launched a yaw misalignment detection campaign at a 90-turbine wind farm in Texas.
The operators of the wind farm had concerns that prevalent yaw misalignments across multiple wind turbines on-site were causing significant AEP losses. Preliminary random photography of the wind site displayed concerning evidence of misalignment, especially given that the terrain is flat and not prone to complex wind patterns. Figure 1 illustrates how nacelles throughout the wind farm are clearly facing different directions during rotation (wind speed > 8m/s).
Tasked with the problem, First Airborne set out to deploy Windborne throughout predesignated measurement points across the site in order to swiftly detect potential yaw misalignment.
What is Yaw Misalignment?
Yaw misalignment occurs when the nacelle of a wind turbine is not aligned with the prevalent wind direction.
Misalignment can manifest in both static and dynamic forms. Static yaw misalignment occurs when the signal consistently deviates from the actual wind direction. Dynamic yaw misalignment, on the other hand, happens when the turbine is slow to adjust to changing wind directions.
Why is this Problem Important?
- Power output impact: Misalignment directly hinders a wind turbine’s ability to efficiently face and adapt to changing wind directions, resulting in suboptimal power output.
- Component stress and lifespan reduction: Beyond design limits, misalignment places additional stress on vital components. This heightened load may accelerate wear and tear, potentially shortening the overall lifespan of the wind turbine.
First Airborne’s Yaw Misalignment Detection Process
Step 1: Northing Assessments
Prior to measuring yaw misalignments for any of the wind turbines on-site, a northing assessment process is completed for each turbine. Utilising image capturing drone technology and a reliable reference azimuth, First Airborne takes a set of overhead images for each wind turbine to identify the exact positioning of a turbine’s nacelle within a defined time period.
These overhead images are then compared with SCADA to adjust for the correct nacelle heading versus the SCADA heading signal. For this project, northing assessments were completed for all 90 wind turbine generators (WTGs). After each WTG’s real nacelle heading is made available, the wind resource measurement campaign commences.
Step 2: Wind Data Collection
Wind data is collected across the entire wind farm site. For this specific project, the measurement locations were distilled to 20 measuring points, each point covering 3 to 5 wind turbines, ensuring strong correlations between the Windborne sensor and the tested turbines. For each measurement point, 90,000 high resolution data samples were collected.
Step 3: Analysis
Utilising the proprietary data gathered with Windborne and SCADA data provided by the operator, First Airborne’s automated back-end analytics determined the extent to which certain wind turbines were inaccurately measuring wind direction and, therefore, experiencing persistent yaw misalignment. All misalignment detections fulfilled the industry standard statistical significance requirements.
Campaign Results
According to industry norms, specifically as outlined by Deutsche WindGuard, a misalignment of up to 5 degrees is anticipated.
Out of 90 turbines measured:
- 10 contained misalignments > 6 degrees, 3 of which were misaligned > 8 degrees. This translates into losses ranging approximately between 1.5 and 2% AEP.
- 5 of the wind turbines contained misalignments ranging between 5 and 6 degrees. This translates into losses of ~1% AEP.
- 8 of the wind turbines contained misalignments ranging between 4 and 5 degrees. Although 4 degrees is less than the 5-degree misalignment tolerance threshold, addressing these misalignments can still lead to valuable AEP improvements.
Conclusions
By leveraging Windborne technology, First Airborne identified evidence of yaw misalignment for 25% of the wind turbine fleet within several weeks. Through standard yaw offsetting adjustments, the entire wind farm can recover > 1.5% of lost AEP with financial benefits for years to come.
Further Reading
- Fields, M.J., Optis, M., Perr-Sauer, J., Todd, A. et al. 2021. Wind Plant Performance Prediction Benchmark Phase 1 (Technical Report). Golden, CO, USA: National Renewable Energy Laboratory. https://doi.org/10.2172/1826665
Biography of the Author
Itay Mor is a wind farm developer, builder, operator, and consultant. Throughout his career, Itay has developed over 160MW, and built and operated 50MW. Additionally, Itay is an experienced international business development and strategic partnerships professional. He is currently the VP of business development at First Airborne.
Boaz Peled founded First Airborne in response to operational challenges he faced as an operator of wind farms, specifically the question of efficient and scalable WTG performance testing. Boaz is a wind farm developer and has been active in the wind power industry for over a decade. He has developed green field projects, managed wind farm construction and financing, as well as their technical and commercial operations. Beforehand, Boaz was active as an investment manager in European real estate and, prior to that, in US private equity.
