Top 4 Ways to Detect Static Yaw Misalignment

One of the most common issues that wind farm operators face is static yaw misalignment. Because this problem is invisible to the turbine controller, wind projects must use specialized methods to identify and address the issue.

Detecting Yaw Misalignment

One significant challenge to measuring yaw misalignment is that its value may vary over time due to wake effects, wind flow characteristics, and even disruptions to the nacelle anemometer. To compensate for these situations, continuous monitoring of the yaw misalignment is ideal. Unfortunately, not all existing solutions can provide continuous estimates, so periodic recalculation of the yaw misalignment is required.

If you're operating under a full-service agreement, your OEM may offer calibration techniques to align the nacelle anemometer to the turbine axis. Keep in mind, however, that not all OEMs provide such services, and even a well-aligned nacelle anemometer could have yaw misalignment.

There are many different ways that you can detect yaw misalignment, ranging from expensive new hardware to utilizing the equipment you already have. What are the standard solutions available, and which one is ideal for your wind project?

Traditional Yaw Misalignment Detection: Hardware Solutions

Traditionally, yaw misalignment detection has relied on hardware solutions: physical devices that you can mount on your turbines or nearby measurement devices. Typical hardware solutions include:

LiDAR

Light detection and ranging (LiDAR) systems can be mounted on top of the nacelle to measure wind direction. LiDAR projects light, which bounces off of particles in the air and reflects back into a receiver. By measuring the reflected light waves, the software can calculate wind speed and relative wind direction. If the LiDAR is in the control loop, it can direct the turbine to turn into the wind.

• Pros: LiDAR allows for continuous monitoring of wind speed and direction. If the LiDAR is in the loop, it can continuously collect data and feed it into the turbine controller, allowing the turbine to adjust to changes in the wind direction. Its accurate and consistent measurements can help mitigate static yaw misalignment. Just a few years ago, LiDAR was undergoing rapid adoption as a new industry standard. However, it is not without its drawbacks.
• Cons: LiDAR allows for continuous monitoring, but only for the turbine it is mounted on. This limitation means that implementing LiDARs to measure static yaw misalignment effectively can get very expensive. To consistently monitor a whole wind farm, every turbine needs its own LiDAR. If you cannot procure LiDAR for every turbine, the modules you do have will need to be moved from time to time between turbines. As a result, you cannot continuously monitor all of your turbines at all times. Any changes in yaw misalignment that occur when the LiDAR is not on that specific turbine will not be identified until the unit returns. To function correctly, the LiDAR unit must be aligned perfectly with the nacelle. If installed incorrectly (even slightly), the unit will give faulty static yaw misalignment recommendations, which will negatively impact your energy production. Finally, LiDARs need to be sent back to the supplier for maintenance periodically. This is an added cost and a disruption to continuous operation.

We find LiDAR to be a good R&D tool, but not suited for mass application.

Spinner Anemometer

It is difficult to measure wind direction with a nacelle anemometer because of its position on the turbine. When mounted on the nacelle, the anemometer sits behind the rotor blades, which distort the wind flow. A newer solution is the spinner anemometer. This anemometer is mounted on the spinner in front of the blades.

• Pros: When mounted on the spinner, the anemometer is able to provide an estimate of wind direction unaffected by the flow of wind around the blades. This means the information provided is much more reliable than a nacelle anemometer can provide. It is also permanently installed on a turbine, which means it can measure wind direction consistently and help with static yaw misalignment detection in real-time.
• Cons: Similar to LiDAR, spinner anemometers must be mounted on every turbine in order to provide a complete picture of your wind farm and to regulate each individual turbine. This is not a cost-effective solution, and there are no short-cuts. While moving an anemometer from turbine to turbine is possible, it’s difficult and expensive to do.

Met Tower

A nearby met tower can be used to calculate wind direction. This approach may seem cost-effective because met towers are traditionally installed when siting wind farms, but the method is outdated and, ultimately, costly.

• Pros: Met towers are installed from the very beginning of a wind project in order to measure wind direction and speed before spending the time and money to install turbines. This provides a small advantage: you likely already have one. Additionally, met towers can provide continuous measurement of wind direction.
• Cons: While met towers might seem like a good option at a glance, their drawbacks far outweigh their benefits. First, the met tower must be properly calibrated to provide accurate information. Since they are typically used as a tool pre-installation, most met towers are not re-calibrated once the farm is active. Using a met tower that hasn't been recently calibrated means using potentially inaccurate data to inform your decisions. Next, the met tower can only be used for a limited number of turbines since it can only provide reasonable wind direction estimates within specific distances. Even when turbines are close enough, wind direction can still be different at the met tower and the turbine. Finally, if other factors used in combination with met towers such as the nacelle direction of the turbine are not calibrated correctly, the measured static yaw misalignment estimate will be incorrect. The result on all counts is a loss in wind plant output and efficiency.

A New Approach: Data-Based Solutions

It’s tempting to turn to a hardware approach for yaw misalignment detection, but these options come with a series of disadvantages. They’re expensive, difficult to install, and—depending on the method you use—unreliable.

In recent years, more data-based approaches have begun to emerge as an answer to these unsatisfactory hardware solutions. Data-based solutions are different because they don’t require new equipment. They use the turbine’s own measurements to estimate and correct yaw misalignment. There are a few distinct advantages:

• Pros: Data-based solutions only require access to your turbines' SCADA data to analyze and monitor the relevant information. To that end, you can continuously monitor the status of yaw misalignment for your wind turbines without the need to move any equipment and scale the turbines being analyzed as needed. The end result is no downtime and no need to procure, install, or service hardware measurement equipment. Furthermore, turbine adjustments can be made quickly and efficiently.
• Cons: Wind is complicated. In order to use existing SCADA data to analyze and detect yaw misalignment, you need to be able to analyze the data combining physics-based models with machine learning techniques. For example, wind measurements taken from existing SCADA data can be affected by the aerodynamics of a turbine's blades. If a comprehensive approach is taken when analyzing the data, these effects can be minimized. For this reason, it’s important to work with the right partner to help you optimize your wind project.

Addressing Your Project's Yaw Misalignment

While there are many options for addressing yaw misalignment, the data-based approaches have consistently demonstrated that they can deliver maximum revenue at minimal cost by estimating and correcting the static yaw misalignment that exists in most turbines.

Want to see the process in action? In a recent engagement with UPC Renewables, WindESCo helped the wind plant boost AEP by 2% by analyzing their existing SCADA data and providing actionable solutions. Download the case study to learn more.