Power curve monitoring using weighted moving average control charts


This paper presents a method for detecting small underperformances of wind turbines generator.

The method is validated with simulated underperformances on archived data from an industrial wind farm.

The detection of the underperformances is based on the power curve and uses control charts.

To illustrate the potential of the method, a study case a of wind turbine generator with blade erosion is presented.


A method for the monitoring of a wind turbine generator is proposed, based on its power curve and using control charts. Exponentially Weighted Moving Average (EWMA) and Generally Weighted Moving Average (GWMA) control charts are used to detect underperformances such as blade surface erosion. These variations in production amount to a few percent per year. The reference power curve is modeled using the bin method. A validation bench using simulated shifts on data from an MW-class wind turbine generator is used to assess the performance of the proposed method. Results show great potential, with both the EWMA and GWMA control charts able to detect a 1% per year underperformance inside 300 days of operation, based on simulated data. A short example is also given of an application using data involving a real case of underperformance: this example illustrates both the applicability and potential of this method. In this case, a shift of 3.4% in annual energy production over a period of five years could have been detected in time to plan proper maintenance. The rate of false alarms observed is one for every 667 points, which demonstrate the method’s robustness.


  • Power curve monitoring;
  • Control charts;
  • EWMA;
  • GWMA;
  • Wind energy;
  • Underperformance

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