Abstract. Atmospheric conditions have a clear influence on wake effects. Stability classification is usually based on wind speed, turbulence intensity, shear and temperature gradients measured partly at met masts, buoys or LiDARs. The objective of this paper is to find a classification for stability based on wind turbine Supervisory Control and Data Acquisition (SCADA) measurements in order to fit engineering wake models better to the current ambient conditions. Two offshore wind farms with met masts have been used to establish a correlation between met mast stability classification and new aggregated artificial signals. The significance of these new signals on power production is demonstrated for two wind farms with met masts and measurements from a long range LiDAR and validated against data from one further wind farm without a met mast. We found a good correlation between the standard deviation of active power divided by the average power of wind turbines in free flow with the ambient turbulence intensity when the wind turbines were operating in partial load. The proposed signal is very sensitive to increased turbulence due to neighbouring turbines and wind farms even at a distance of more than 38 rotor diameters away. It allows to distinguish between conditions with different magnitude of wake effects.

Abstract. Wind farm underperformance can lead to significant losses in revenues. Efficient detection of wind turbines operating below their expected power output and immediate corrections help maximise asset value. The presented method estimates the environmental conditions from turbine states and uses pre-calculated power matrices from a numeric wake model to predict the expected power output. Deviations between the expected and the measured power output are an indication of underperformance. The confidence of detected underperformance is estimated by detailed analysis of uncertainties of the method. Power normalisation with reference turbines and averaging several measurement devices can reduce uncertainties for estimating the expected power. A demonstration of the method’s ability to detect underperformance in the form of degradation and curtailment is given. Underperformance of 8 % could be detected in a triple wake condition.

Dear Referee, thank you very much for reviewing our paper and sharing your experience. It is of great value for us and we hope to have answered your questions and comments sufficiently. Your comments helped us to identify the sections, where certainly more explanations are needed and it will help to improve the paper. Please finde our answers in the supplement pdf. Sincerely, Niko Mittelmeier and Co-authorsPlease also note the supplement to this comment: http://www.wind-energ-sci-discuss.net/wes-2016-16/wes-2016-16-AC2-supplement.pdf C1

Abstract. Upwind horizontal axis wind turbines need to be aligned with the main wind direction to maximize energy yield.Attempts have been made to improve the yaw alignment with advanced measurement equipment but most of these techniques introduce additional costs and rely on alignment tolerances with the rotor axis or the true north.Turbines that are well aligned after commissioning, may suffer an alignment degradation during their operational lifetime. 10Such changes need to be detected as soon as possible to minimize power losses. The objective of this paper is to propose a three-step methodology to improve turbine alignment and detect changes during operational lifetime with standard nacelle metrology (met) mast instruments (here: two cup anemometer and one wind vane).In step one, a reference turbine and an external undisturbed reference wind signal, e.g. met mast or lidar are used to determine flow corrections for the nacelle wind direction instruments to obtain a turbine alignment with optimal power 15 production. Secondly a nacelle wind speed correction is enabling the application of the previous step without additional external measurement equipment.Step three is a monitoring application and allows to detect alignment changes on the wind direction measurement device by means of a flow equilibrium between the two anemometers behind the rotor.The three steps are demonstrated at two 2MW turbines together with a ground based lidar. A first order multi linear regression model gives sufficient correction of the flow distortion behind the rotor for our purposes and two wind vane 20 alignment changes are detected with an accuracy of ±1.4 ° within three days of operation after the change is introduced.We could show, that standard turbine equipment is able to align a turbine with sufficient accuracy and changes to its alignment can be detected in a reasonable short time which helps to minimize power losses.

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