Wind Turbine Underperformance

What is Wind Turbine Underperformance?

Wind turbine performance is a relationship between various factors, including wind speed, turbine condition, sensor data, and weather data. Underperformance occurs when this relationship is suboptimal and degrades power output.

In the past, turbine performance was challenging to identify and costly to solve, but new technologies have granted a new level of insight into underperformance and how to correct it. 

Wind turbine performance data is now more easily obtained through frequent drone inspections, embedded sensors, and third-party weather data. It’s easier to better identify when a turbine is underperforming, understand why, and implement corrective actions.

Common Causes of Wind Turbine Underperformance

What causes wind turbines to underperform? There is a wide variety of possible reasons a turbine’s power output may be less than expected, as well as overarching factors affecting wind turbine performance. Let’s break down some of the core causes of underperformance and how to maintain optimal performance.

The Impact of Icing on Turbines

Icing can be common for turbines in cold climates and is often mistakenly considered a minor issue. However, wind farm performance can be significantly affected by icing on turbine blades, nacelles, and rotors. 

In reality, icing can create up to 40% losses in power generation in Canadian sites and up to 17% in other regions. The power degradation caused by ice on wind farms must be adequately understood in order to be addressed appropriately. 

Wind farm managers can’t control the weather, but miscalculating the effects of ice on performance can result in reduced revenue and spending money on other issues that don’t address the ice problem. Accurate wind turbine power performance testing must include the effects of ice.

Yaw Misalignment and Overall Condition

Each turbine’s condition affects its power generation, which means various components in poor condition can create underperformance. Nacelles, rotors, blades, and yaws are all critical in optimally generating power, and their condition must be monitored and maintained.

Yaw misalignment is a common and hard-to-identify cause of wind turbine underperformance. This issue means that turbines are not properly aligned with wind direction, increasing wear on turbine blades and failing to capture all available wind.

Failing to identify misalignment directly affects power output and reduces the turbine’s lifespan. Adopting the right wind turbine performance monitoring software and hardware goes far in catching yaw misalignment and other condition-related problems to keep turbines operating optimally.

Relying on Traditional Power Curves

Power curves describe the relationship between wind speed and power output and are often provided by the Original Equipment Manufacturer (OEM). These static graphs are a tool aimed at addressing underperformance by predicting how much power a turbine should generate at different wind speeds. 

Curve-based techniques are an incomplete solution to address other causes of underperformance, resulting in chronic underperformance. 

There are several problems with power curves, such as:

• Power curves don’t capture the full context of operating conditions, like air density or hilly terrain, creating a partial picture and incomplete way to address underperformance.
• OEM curves are specific to each turbine model, not accounting for designs or repowering. This makes them hard to follow at the scale of the entire farm, as decisions are made based on various power curves.
• These curves don’t consider the business value of correcting underperformance, making it challenging to earn stakeholder buy-in. 

While power curves were the best options in the past, the latest in wind farm analytics and supporting technologies have made them an inaccurate way to address underperformance, preventing turbines from operating optimally. 

For example, focusing on the wind turbine coefficient of performance, i.e., measuring how efficiently a turbine converts wind energy into electrical power, by using embedded sensors and external data can go far in rapidly identifying performance issues.

Best Practices to Avoid Wind Turbine Underperformance

We’ve explored a few common causes of underperformance and how to address them, but beyond these specific processes, there are overarching best practices to consider to help prevent underperformance. These best practices include:

• • Adopt leading-edge analytics platforms: AI-driven analytics can leverage inspection data to better identify any issues that may be affecting performance. The right platform can also affect wind turbine predictive underperformance and help identify performance issues before they occur, significantly improving output.
  • Frequent and consistent inspections: Inspection data helps identify component-related issues, such as yaw misalignments, and evaluate ice conditions. Autonomous drone inspections consistently capture high-quality data and can be conducted more often than legacy methods. 
  • Accurate weather forecasting: One key source of damage is extreme weather, which can be as significant as a tower collapse or more subtle damage that affects wind performance. Being surprised by excessive winds can cause turbine blades to spin too fast, damaging them and reducing performance in the process. Leveraging all possible weather data to forecast weather helps avoid being caught off guard by extreme weather.

Proactively protecting the condition of turbines prevents underperformance, increases power generation, and maintains reliable revenue. It’s well worth the initial investment in necessary software and hardware to keep wind turbines performing at optimal levels.