This release includes a new demonstration on the use of FLASC using SCADA data collected from a wake steering test at a commercial wind farm. The data comes from the SMARTEOLE Wind Farm Control open data set, which comprises a field campaign carried out on a seven-turbine wind farm at Sole du Moulin Vieux, France. The new examples use real data sets to show the how to use FLASC methods with physical SCADA data as well as to demonstrate the recreation of the analysis of the original paper. The examples also illustrate the structure and filtering of raw data and provide a matched FLORIS model. The steps are based on a workflow developed by Bart Doekemeijer in a separate FLASC Cookiecutter template repo.
The new examples are available within FLASC in the directory: examples_smarteole.
Improvements to the methods FLASC uses to energy ratio class. Now, FLASC includes methods to compute gain in energy ratio. Additionally, users can employ block and conventional bootstrapping to calculate uncertainity bounds. Finally, the team improved energy ratio visualizations so that they now illustrate wind speed content by wind direction bin and allow users to plot results on a polar axis. These improvements are again thanks to contributions from Bart Doekemeijer.
Improvements in data filtering. The FLASC preprocessing data filtering methods have been significantly improved in v1.3 because of contributions from Bart Doekemeijer. The method to call each individual filtering step has been made more consistent, and visualization of the effect of each filtering step is now included.
Detection of impacting turbines and methods to filter based on neighboring turbines. FLASC now includes methods to determine which turbines impact each other and enables users to filter results based on these relationships to control for the impact of faulted or curtailed turbines on downstream turbines in analyses.
Feature or improvement description
This release includes a new demonstration on the use of FLASC using SCADA data collected from a wake steering test at a commercial wind farm. The data comes from the SMARTEOLE Wind Farm Control open data set, which comprises a field campaign carried out on a seven-turbine wind farm at Sole du Moulin Vieux, France. The new examples use real data sets to show the how to use FLASC methods with physical SCADA data as well as to demonstrate the recreation of the analysis of the original paper. The examples also illustrate the structure and filtering of raw data and provide a matched FLORIS model. The steps are based on a workflow developed by Bart Doekemeijer in a separate FLASC Cookiecutter template repo. The new examples are available within FLASC in the directory: examples_smarteole. Improvements to the methods FLASC uses to energy ratio class. Now, FLASC includes methods to compute gain in energy ratio. Additionally, users can employ block and conventional bootstrapping to calculate uncertainity bounds. Finally, the team improved energy ratio visualizations so that they now illustrate wind speed content by wind direction bin and allow users to plot results on a polar axis. These improvements are again thanks to contributions from Bart Doekemeijer. Improvements in data filtering. The FLASC preprocessing data filtering methods have been significantly improved in v1.3 because of contributions from Bart Doekemeijer. The method to call each individual filtering step has been made more consistent, and visualization of the effect of each filtering step is now included. Detection of impacting turbines and methods to filter based on neighboring turbines. FLASC now includes methods to determine which turbines impact each other and enables users to filter results based on these relationships to control for the impact of faulted or curtailed turbines on downstream turbines in analyses.