Keep an eye on Wind Turbine performance and detect anomalies via Baseline Power Curve
The wind turbine power curve contains key information regarding the turbine efficiency and health but the simplified power curve displayed on the SCADA system loses this data information in translation.
“The Power Curve contains the key to how much revenue your assets will generate”
Tarentum Power Curve Analyzer can be used to analyze the data and verify power curves using SCADA data in a generic way with different kinds of conditions like fault, icing and dirt on blades, down rating, pitch malfunctions, and etc. This is one of the ways that we keep track of the performance of a wind turbine.
The Technical Approach to Analyzing Power Curve using SCADA data
We are interested in determining a baseline power curve for each wind turbine that represents the most expected behaviour of that specific turbine. The easiest way would be using a theoretical power curve which has been provided by a wind turbine manufacturer. However, this theoric power curve does not reflect the power curve you observe from SCADA due to conditions such as turbine location and geographic conditions, wind characteristics, air density, and wind measurement uncertainties. To prevent this disparity, first a wind range of the SCADA data is selected, free of faults and maintenance. Then, we filter out data which cause unusual deviations from the expected power curve such as icing, grid failure etc… Filtering these samples in which the wind turbine is not producing any output power are helpful steps to achieve a more generic power curve representing the real behavior of the turbine which we call the baseline power curve. In order to follow the common standards, then, we use the IEC standard binning method for data representation:
As you see in the above figure our Baseline is very close to the theoretical power curve. Consider again, sometimes the baseline may differ little more from the theoretical one.
How to monitor anomalies? Is something wrong?
In order to find odd cases, which is a deviation from the baseline, we use several time intervals like 60 days , 30 days, 15 days and 7 days of data to plot new power curves. The data sets are continuously monitored. If there is a deviation from the baseline power curve the time period is flagged for further inspection.
An example case;
Above, you see some anomalies in the past 60 days period of the new power curve which have been derived using the described methods. Now that we identified the problem, we can look at the raw data on the plot:
Let’s watch exactly what is happening in these periods and when:
Further inspection on the low performance period has indicated that the power curve underperformance has been related to a manual curtailment on the turbine. The issue has been identified as a commercial decision due to market pricing at given hours; service and trading departments have been aligned.
A continuous power curve analysis provides the asset manager with the ability to detect any anomaly on turbine performance and increase the asset revenues by improved performance/shorter reaction times. With the assistance of Tarentum Power Curve Analyzer asset managers will have a never ending power curve verification capability.