Exploring static yaw misalignment detection and Greensolver’s role in the process
Can data analysis replace short-range nacelle-based LIDAR on wind turbines?
The economic landscape of the renewables sector has undergone significant changes, prompting a sharper focus on performance improvements. Over the past decade, higher interest rates, supply chain constraints, volatile commodity prices, and inflation in equipment and raw material costs have squeezed the profitability of wind projects. In response, the industry is turning to innovative technologies to enhance energy efficiency.
Amid these financial challenges, the wind energy sector has reaped substantial benefits from advancements in data computing and analysis technologies. These innovations have led to remarkable performance improvements, particularly in operational efficiency.
Traditionally, performance analysis has relied on 10-minute averaged data from SCADA systems – a reasonable choice given its balance between precision and data volume. Wind turbines are equipped with a multitude of sensors, generating vast amounts of data that can be overwhelming to store and analyze.
Most Original Equipment Manufacturers offer the possibility to communicate with OPC servers, giving access to 1sec data. This granularity unlocks the potential for deeper insights into power dynamics, continuous monitoring, and more responsive maintenance, forecasting, and specific performance enhancements.
The relevance of Real-Time Data Analysis (RDA) becomes even more apparent when it can substitute for performance checks that traditionally require capital intensive hardware, such as LIDARs. To be clear, LIDARs are far from obsolete; they remain highly relevant in many applications. However, for certain tasks, RDA offers a quicker and more cost-effective alternative.
A Concrete Application: Static Yaw Misalignment Detection
Static Yaw misalignment refers to a persistent deviation between the turbine’s rotor orientation and the actual wind direction. In essence, the turbine isn’t facing directly into the wind, which is essential for optimal performance. This misalignment can stem from various causes, including incorrect installation or calibration of the wind vane sensor, software issues in the turbine controller, mechanical problems with the yaw system, or gradual sensor calibration drift over time.
The primary consequence of Static Yaw misalignment is reduced power generation. This occurs because the effective swept area of the rotor is decreased, capturing less wind energy. Moreover, the blades’ angle of attack becomes suboptimal, reducing aerodynamic efficiency. The relationship between the misalignment angle (θ) and power loss follows a cos²(θ) function. For instance, a 10° misalignment can lead to an approximately 2.6% loss in Annual Energy Production (AEP).
In addition to power loss, Static Yaw misalignment also results in uneven loads on turbine components, particularly the blades and drivetrain. This imbalance increases fatigue, potentially shortens the turbine’s lifespan, and drives up maintenance costs over time.
Historically, wind farm operators have detected Static Yaw misalignment by installing hardware such as spinner sensors, additional wind anemometers, and short-range nacelle-based LIDARs. However, with the advent of Real-Time Data Analysis, Greensolver offers a cutting-edge alternative.
Greensolver has teamed up with two leading data analytics specialists carrying different software approaches, to conduct a groundbreaking assessment campaign on wind assets. This collaboration provided an excellent opportunity to test the effectiveness of Static Yaw misalignment detection using:
- Real-time data analysis on the one hand: with a data granularity of 1sec, maximum power points are identified when the turbine realigns to the wind, highlighting the static yaw misalignment by averaging over a substantial period (3month minimum)
- A patented Digital-twin model approach, based on algorithms that cross 10min data from SCADA, long term wind databases (ERA/MERRA2) as well as satellite imaging to determine the Static Yaw misalignment.
Those software empirical studies have demonstrated similar accuracy that hardware approaches such as LIDAR or nose cone anemometer in identifying Static Yaw misalignment.
Why does Software approach Outshines LIDAR Campaigns
When it comes to detecting Static Yaw misalignment, Real-time Data Analysis (RDA) offers several advantages over traditional LIDAR campaigns (LC):
- Effortless Data Access: RDA only requires remote access to Real-Time SCADA data via an OPC Server connection, enabling the analysis to be performed off-site, reducing on-site visits and associated logistics.
- Unmatched Scalability: RDA allows for simultaneous detection across all turbines, whereas each LIDAR system can only monitor one turbine at a time. In large wind farms, this could mean deploying multiple LIDAR systems, driving up costs.
- Non-Intrusive Approach: Unlike LIDAR, RDA doesn’t require any on-site equipment installation, avoiding common challenges such as:
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- Production downtime during hardware setup (typically half a day per turbine).
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- Financial costs linked to deploying specialized technicians.
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- Potential hardware failures leading to operational and safety risks (e.g., broken lenses, loss of communication).
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- OEM concerns about drilling through nacelle fibers to install LIDAR support structures.
- Permanent Monitoring: RDA facilitates continuous tracking of Yaw alignment post-correction, enabling more straightforward adjustments when necessary.
A Comprehensive Solution for Wind Farm Owners
At Greensolver, we’ve honed our expertise in helping wind farm owners assess and correct potential Static Yaw misalignments through RDA/software technology. These campaigns can be conducted during both operational phases and commissioning with minimal CAPEX investment, while significantly boosting performance. Our experience has also highlighted which technologies are more susceptible to these issues and the most effective strategies for involving OEMs and O&M providers in the correction process.
Written by Clément Iafrate, Team Leader Asset Management Wind
