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Rich Data for Wind Turbine Power Performance Analysis

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University of Minnesota St. Anthony Falls Laboratory

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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 (http://eolos.umn.edu/facilities/eolos-wind-research-station)  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. Figure 1 illustrates the main turbine geometry and the disposition of the sonic instruments. 
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 (http://mesonet.agron.iastate.edu/ASOS). Solar parameters are calculated based on local sunrise and sunset times from timeanddate.com (https://www.timeanddate.com/sun/usa/minneapolis).

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 (www.napier.ac.uk/) using bespoke Python code. Documentation regarding the interpretation of the columns in the file is provided in the form of an csv spreadsheet. For further details please contact the author by email at b.davison@napier.ac.uk.

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The post-installation verification of wind turbine performance is an essential part of a wind energy project. Data collected from meteorological instruments and from the turbine is analysed to produce an estimate of the annual energy production (AEP) which is compared against expectations. However, turbine warranties can impose very strict data filtering criteria which can lead to high rates of data loss. As a consequence, measurement campaigns may last longer than expected and incur additional costs for the development.
This project aims to investigate the extent of the problem and the potential of alternative data filtering strategies with respect to data loss, AEP estimates and the dispersion of points in the power curve scatter plot. In doing so, it targets a wide range of meteorological parameters with theoretical relationships to wind turbine power production with particular interest in those not accounted for in the current standard. The identification of viable filtering strategies with lower data loss would provide significant benefits to wind energy development projects in terms of greater control over timescales and reduced costs.
Data from a sample of power performance tests is analysed to explore the range and severity of the problem of data loss. It confirms the wide variation in warranty conditions, demonstrates the extent and likelihood of data losses and quantifies the financial implications within the limits of commercial sensitivity. When indirect costs are taken into consideration, the impact of extended measurement campaigns can theoretically reach tens of millions of pounds.
A new, high-fidelity dataset is then compiled so that the effects of alternative filtering strategies can be examined. The dataset covers the whole of 2017 and consists of over 700 parameters of which 74 are selected for investigation here. The eFAST method of global sensitivity analysis is used in combination with correlation analysis to reduce this number to 11 parameters which are then used to define alternative filtering criteria.
Similar AEP estimates are obtained by application of conventional and experimental criteria to the research dataset. In the case of the experimental filters, however, the data loss was 11% compared to 63% data loss with conventional filters. Conventional filters were also shown to increase the dispersion in the power curve scatter plot by over 10%, while dispersion did not increase significantly with the experimental filters.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.description.suggestedcitation[].value
Davison, Brian. (2019). Rich Data for Wind Turbine Power Performance Analysis. Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/1etn-1q17.

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The code repository related to this dataset

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This file in the code repository contains the python methods used to calculate most of the derived quantities:

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Davison, B. The evaluation of data filtering criteria in wind turbine power performance assessment. (Thesis). Edinburgh Napier University. Retrieved from

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Rich Data for Wind Turbine Power Performance Analysis

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Transcriptomic data from RNA Sequencing of canine high grade glioma tumor samples

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University of Minnesota Canine Brain Tumor Program

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There are two data files that contain the raw gene expression data for French bulldogs, boxers, and Boston terriers pre- and post-immunotherapy treatment. One data file contains French bulldog paired RNA sequencing data. These data were used to assess transcriptomic changes in French bulldogs post-treatment compared to pre-treatment. The other data file contains boxer and Boston terrier paired RNA sequencing data. These data were used to assess transcriptomic changes in boxers and Boston terriers post-treatment compared to pre-treatment. Samples consist of pre-treatment samples, which were obtained at the time of surgical resection of high grade gliomas, and post-treatment samples, which were obtained from whole brains collected after death. Immunotherapy treatment consisted of a combination of autologous tumor lysate therapy and an immune checkpoint inhibitor.

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These files contain the raw data for bulk RNA sequencing performed on canine high grade glioma tumor samples. These samples were obtained from three breeds of dogs: French bulldogs, boxers, and Boston terriers. All dogs were enrolled in immunotherapy clinical trials within the University of Minnesota Canine Brain Tumor Program. They were obtained from two different time points relative to treatment: pre-treatment and post-treatment. Treatment consisted of surgical resection and immunotherapy. The purpose of these data are to provide a comprehensive profile of how canine high grade glioma transcriptomes change in response to immunotherapy treatment, and to determine if there are breed-associated changes in differential gene expression.

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University of Minneosta Children's Cancer Research Fund

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Arnold, Susan A; Pluhar, G. Elizabeth; Abrahante Llorens, Juan E. (2024). Transcriptomic data from RNA Sequencing of canine high grade glioma tumor samples. Retrieved from the Data Repository for the University of Minnesota (DRUM), https://hdl.handle.net/11299/267779.

# _embedded.searchResult._embedded.objects[]._embedded.indexableObject.metadata.dc.title[].value
Transcriptomic data from RNA Sequencing of canine high grade glioma tumor samples

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