Use of nacelle lidar data to explore impact of non-linear averaging Chris Slinger, John Medley, Rhys Evans [email protected] +44 1531 650 757 02 September 2014 Background. From the April 2014 PCWG meeting: “Existing correction methods (, RE, TI renorm) do not fully explain observations...” 10 minute mean wind speeds used Erik Tűxen reminded us a more fundamental measure of performance is the relationship between the turbine’s electrical power and the wind’s kinetic power The meeting also noted that averaging non-linear quantities can be misleading Would 10 minute wind speeds based on mean energy be more useful than existing approaches (e.g. mean wind speeds with TI renormalisation) ? 2 Aim of investigation Use high frequency lidar measurement data to investigate these effects – Compare efficiency plots derived from 10 minute averaged wind speeds and those from 10 minute cubed-root-mean-cubed wind speeds – Compare power curves in a similar fashion – Use validation / Round Robin tools to analyse data too – e.g. Power deviations as a function of wind speed and turbulence Also use high frequency lidar data to explore validity of 10 minute normal wind speed distribution assumption – Normal distribution is assumed in the TI renormalisation procedure 3 First dataset to be used Project Cyclops: ZephIR DM on turbine “Project Cyclops: the way forward in power curve measurements ?” Simon Feeney et al, EWEA 2014 New 2 MW Vestas turbine, flat on-shore site in UK Use 1s data from nacelle-mounted ZephIR dual-mode lidar Use 1s data from ground-based ZephIR lidar too Collaborate with RES UK, who will analyse 1s metmast data too 4 ZephIR DM on ground Initial results: normality of data A graphical method of looking at the normality of a distribution is a QQ plot, comparing quantiles from the data to expected Gaussian quantiles. Gaussian data should give a straight line. Some examples are shown here: 5 Normality of data : skewness and kurtosis Skewness and kurtosis are statistics that describe the shape of a probability distribution Skewness measures asymmetry of the distribution Kurtosis measures the how peaked (or how heavy-tailed) the distribution is Kurtosis = -1.56 6 Kurtosis = 0.00 Kurtosis = +13.1 Normality of data : skewness and kurtosis Skewness and kurtosis are statistics that describe the shape of a probability distribution Skewness measures asymmetry of the distribution Kurtosis measures the peakedness (or how heavy-tailed it is) Other Normality tests are available, but a simple kurtosis filter looks easy to implement and may be sufficient – let’s try it and see! 7