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Rich Data for Wind Turbine Power Performance Analysis

Davison, Brian



This dataset was created in order to support comparative analyses concerning the relationships between meteorological parameters and wind turbine power output. It addresses some of the limitations of existing datasets such as those compiled for commercial power performance testing (PPT) according to the international standard (IEC, 2017) and those created by micrometeorological research projects. In particular, it includes

* A wide range of statistical measures for the base parameters
* Alternative measures of wind shear, wind veer and turbulence
* Multiple indicators of atmospheric stability
* Estimates of vertical kinematic fluxes of sensible heat and horizontal momentum
* Solar geometric parameters

The dataset is in tab-delimited format and comprises ten-minute average (TMA) records for the calendar year 2017. Meteorological and turbine SCADA data comes from the Eolos Wind Research Station operated by the University of Minnesota ( and is primarily based on the measurements taken by sonic anemometers. The installation includes a 2.5 MW Clipper Liberty turbine with an associated 130 m met mast which spans the entire vertical diameter of the turbine rotor. The flux calculations rely on the rotation of the coordinate frame which requires knowledge of the local horizontal gradients of pressure and temperature. These are provided by the Automated Surface Observing Systems (ASOS) network of meteorological measurement stations maintained by US government agencies ( Solar parameters are calculated based on local sunrise and sunset times from (

Limitations of the dataset include:

* Incomplete coverage of SCADA parameters
* Wind speed and direction are based solely on sonic measurements
* Flux calculations use a ten-minute averaging period rather than the standard 30 minutes
* Significant turbine curtailment, especially early in the year
* Instrument problems concerning the anemometer at the rotor top-tip height

The dataset was compiled as part of the author’s PhD at Edinburgh Napier University ( using bespoke Python code. Documentation regarding the interpretation of the columns in the file is provided in the form of a csv spreadsheet.

Online Publication Date Jul 31, 2019
Publication Date Jul 31, 2019
Deposit Date Apr 13, 2021
Keywords Wind Turbine; Power performance; SCADA
Public URL
Publisher URL
Type of Data Meteorological and wind turbine data (csv)
Collection Method Scientific instruments including mechanical and sonic anemometers, wind vanes, temperature and humidity sensors, power transducers.
The data was collected using by the University of Minnesota and enriched through combination with data from other sources and through the calculation of a wide range of derived quantities.