The findings matter because Kelmarsh data have supported several dozen wind-energy studies since 2022. Conventional quality control generally addresses questionable values within a record, but it cannot by itself reveal that measurements begin late in a file, that the meaning of a channel changed, or that an entire year has been divided into overlapping blocks. In the audit, these problems remained hidden when researchers merely filtered implausible observations. The authors therefore treated file structure, provenance and measurement definitions as components of data quality rather than assuming that a successfully parsed time series was internally consistent and complete.

The first defect affects the apparent timing of data availability. For most measurements, the record starts partway through the year rather than on January 1. These onset points occur at 12 different steps between 2016 and 2023, indicating a pattern across files rather than a single isolated interruption. If an entire annual file is classified as missing before its signals actually begin, calculated missing-data rates can exceed the true gap by as much as 73 percentage points. The audit produced a signal-onset manifest to document when each measurement becomes available, allowing users to distinguish a genuine absence of observations from the earlier beginning of the stored record.

A second problem emerges in the main wind-speed measurement between November 2017 and April 2018. During that interval, the channel changes from representing the average of two sensors to representing a single sensor. This is a change in what the measurement means, not simply a fluctuation in performance. When the two definitions were compared, the change made the turbines appear between 1.2% and 9.1% more productive at a given wind speed. By contrast, the single-sensor series alone changed by between −0.6% and +1.5%. The contrast shows why a mixed series can create the appearance of improved productivity: the apparent shift is tied to sensor configuration rather than a comparable change within one sensor.

The largest structural error occurs in the 2023 and 2024 files. Instead of storing each year as one continuous sequence, the authors found 81 overlapping segments for each year. Measurements are present only in the first copy of each time step, leaving 97.6% of rows without wind-speed or power values. The result is an array that looks highly incomplete if all rows are retained and subjected to ordinary missing-value filtering. If the first copy of each duplicated time step is kept, the year can be recovered; if the last copy is kept, less than 1% of the data remains. That second outcome produces annual capacity factors—annual output expressed as a share of the maximum possible output—that are overstated by 25 to 53 percentage points.

This pattern was not limited to one year. Files tested from a second wind farm owned by the same operator share the segmented structure, indicating that the issue may reflect the owner’s file-production process rather than an isolated defect in the Kelmarsh release. That observation does not establish the precise cause or prove that every file from the operator is affected, but it broadens the potential scope of the problem. It also demonstrates why provenance information matters: file layout, not only numerical values, can determine whether a dataset represents a complete operating year or a collection of mostly empty overlapping records.

The audit also found smaller inconsistencies and placeholder values in records for energy, voltage, wind speed and temperature. These defects are distinct from the three large structural failures because they occur within otherwise identifiable series rather than changing the coverage or definition of the whole record. The authors provide a table that flags suspect values alongside reconstructed files for 2023 and 2024, a manifest of signal onset dates, and the code and outputs used for the analysis. Their supplied dataset is identified as version 4 of the Kelmarsh SCADA data, while the analysis package is released separately so that the checks and reconstructions can be reproduced.

Not every pre-specified prediction survived the audit. Of the predictions reported in the preprint, one failed, seven failed as originally worded, and one could not be evaluated as specified. The authors state that all outcomes are reported rather than narrowing the discussion to the successful cases. This distinction is important for interpreting the reconstruction: the work is a provenance audit supported by reproducible checks, not a demonstration that a single method can diagnose every possible dataset defect. Its strongest conclusions come from directly documented structures in the files, including the onset dates, sensor-definition switch, overlapping blocks and their measurable effects on coverage and calculated summaries.

For downstream research, the results show that open-data reuse requires checks that go beyond deleting extreme or physically implausible values. Analysts need to verify when each signal starts, determine whether a channel retains the same measurement definition over time, and inspect how timestamps and values are distributed across repeated segments. Deduplication also requires a deliberate rule: choosing the first populated copy recovers the 2023 and 2024 measurements, whereas selecting the final copy produces severe data loss and inflated capacity factors. The audit’s code, corrected files and flag table are intended to support those choices, but the preprint itself remains open for discussion and under journal review, so its findings should be understood as results reported by the authors rather than a settled consensus.

Subject of Research: Provenance defects and downstream effects in the Kelmarsh wind farm SCADA dataset

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

Article References: Park, A., Yin, L., Lee, C., Wong, A., & Choi, Y. (2026). Structural defects in the Kelmarsh open SCADA dataset: a provenance audit with file reconstruction and downstream consequences. https://doi.org/10.5194/wes-2026-166

Image Credits: AI Generated

DOI: 10.5194/wes-2026-166

Keywords: Kelmarsh wind farm, open wind data, SCADA datasets, data provenance, data reconstruction, wind speed sensors, missing data, capacity factor, quality control, reproducible research, Structural, defects