Structural defects in the Kelmarsh open SCADA dataset: a provenance audit with file reconstruction and downstream consequences

Abstract. The Kelmarsh wind farm dataset, covering six turbines in the United Kingdom from 2016 to 2024, has been used in several dozen wind energy studies since 2022. The studies we read that use it check its quality by removing implausible values, which cannot reveal errors in how the files are organised or how measurements are defined. We examined all 54 annual turbine files for such errors with nine reproducible checks, fixing our hypotheses in advance, and found four kinds. First, most measurements start partway through the record, in one of twelve steps between 2016 and 2023, so whole-year missing-data rates overstate real gaps by up to 73 percentage points. Second, between November 2017 and April 2018 the main wind speed measurement switches from the average of two sensors to a single sensor, making the turbines appear 1.2 to 9.1 % more productive at a given wind speed; the single sensor alone shows a change of −0.6 to +1.5 %. Third, the 2023 and 2024 files store each year in 81 overlapping segments with measurements only in the first copy of each time step, leaving 97.6 % of rows without wind speed or power values. Keeping the first copy recovers the year; keeping the last retains less than 1 % of the data and overstates the annual capacity factor (output as a share of the maximum possible) by 25 to 53 percentage points. Files tested from a second wind farm by the same owner share this structure. Fourth, the energy, voltage, wind speed and temperature records contain smaller inconsistencies and placeholder values. One pre-specified prediction failed, seven others failed as originally worded and one could not be evaluated as specified; all are reported. We provide corrected files, a table flagging suspect values, and the analysis code.