or water bodies. In addition, turbine SCADA and event data are misaligned by design. However, it is now possible to process data and analyse how different streams interact in a matter of minutes, allowing underperformance to be recognised and loss of revenue to be limited. Clir’s platform utilises an advanced data model to structure available data in a way that enables performance analytics to be conducted quickly and accurately at scale.
However, if SCADA data is analysed correctly, it is possible to easily separate out whether a dip in output is a part of the natural variation in resource or if it is a technical issue. Automating this process using advanced digital tools such as machine learning allows owners to fix small but significant issues before the lost production becomes a burden on their balance sheet.
Finding the needle in a haystack
Poor pitch strategies and yaw misalignment are common causes of turbine underperformance. Angling of the blades or yaw away from the wind direction by as little as 4˚ can reduce AEP by up to 1%. If turbine data is put into the right context, owners can quickly identify whether the nacelle is facing ever so slightly away from the wind direction. Clir worked with a wind farm owner who had identified lower than expected performance across its project but was unsure of the exact cause. After onboarding not only data from every turbine on the wind farm, but also data detailing resource conditions and the surrounding environment, Clir found that one of the turbines had much higher output compared to its neighbours when its nacelle was angled 8˚ away from the direction of the wind. From this, the owner was able to confirm that turbines across the project had been misaligned by 8˚ due to a sensor error. Once repaired, the overall output of the wind farm increased.
While major mechanical failures such as a broken gearbox are easily detectible in traditional data analysis, subtle underperformance can often be missed unless the owners dedicate a significant amount of time looking for ‘needles in a haystack.’ If the owner assesses turbine SCADA data alone to try to identify the source of underperformance, smaller faults are often indistinguishable from dips in output due to a low wind resource. These smaller faults tend to reduce annual energy production (AEP) by 1 - 2% on their own, but if they are consistently missed and allowed to accumulate, asset owners can lose out on hundreds of MWh each year.
Spotting the slightest misalignment
Tracing unnecessary derating
Figure 1. Clir’s interface shows that, for this hypothetical scenario, the wind resource has boosted AEP by 20.2%, while turbine underperformance reduced AEP by 4.5%. Underperformance has prevented the wind farm from taking full advantage of unexpectedly favourable weather conditions.
Unreported derating is another cause of underperformance that can be missed and mistaken for low winds by traditional data analysis methods. While turbines should only be derated under conditions previously agreed by the asset owner, the turbine manufacturer, the grid operator, and the permitting authority, there can be times when derating occurs outside of these parameters and without the owner’s knowledge. This can see the turbine run below rated power despite optimal generating conditions, leaving potential revenue on the table. However, if the owner does not analyse turbine data in the context of its environment, the distinction between a drop in winds and a drop in turbine performance can be lost. For example, Clir analysed the data from a turbine with consistently low output, and after placing it in context of peer turbine data and environmental data, was able to pinpoint that despite optimal conditions, at specific time intervals the turbine’s output would plummet by up to 10%. This pattern of underperformance indicated that the turbine was being derated outside of the terms of the derating agreement. Armed with evidence of misapplied derating, owners can renegotiate derating strategies to ensure that the turbine is only curtailed when absolutely necessary.
Rectifying errors in data collection Figure 2. Static yaw error detected from a client’s turbine data. Power production is maximised when the turbine appears to be 8˚ misaligned with the recorded met mast wind direction, i.e. true wind direction.
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Ironically, malfunctioning sensors used for data collection can actually be a cause of underperformance themselves. For example, wind turbines are often programmed to
