Kelmarsh Wind Farm Data (SCADA) 核心 · 已核验
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This dataset contains: A kmz file for Kelmarsh wind farm in the UK (for opening in e.g. Google Earth) Static data including turbine coordinates and turbine details (rated power, rotor diameter, hub height, etc.) 10-minute SCADA and events data from the 6 Senvion MM92's at Kelmarsh wind farm, grouped by year from 2016 to mid-2021, which was extracted from our secondary SCADA system (Greenbyte). Note not all signals are available for the entire period Data mappings from primary SCADA to csv signal names Site substation/PMU meter data where available for the same period Site fiscal/grid meter data where available for the same period The dataset has been released by Cubico Sustainable Investments Ltd under a CC-BY-4.0 open data license and is provided as is. However, please provide any feedback you might have on the dataset and format of the data. I'll try and add or link to additional file formats that might be easier to work with (e.g. for use with specific analysis software), and update this dataset periodically (e.g. twice a year), but please prompt me as required. Feel free to use the data according to the license, however, it would be helpful to me if you could let me know where, how and why you are using the data, so that I can highlight this to the business (and renewables industry) and hopefully promote similar data sharing initiatives. I am particularly interested in performance analysis/improvement opportunities, how the dataset can be augmented with other (open) datasets, and sharing more generally within the renewables industry. If you would like to get access to other datasets we may hold (e.g. more recent data, data from our other sites, ~30s resolution data, etc.), please let me know, and, if you have any questions or want to discuss open data and this or other initiatives, please contact me and I will endeavour to help. I would like to thank Cubico's Senior Legal Advisor & Compliance Officer, IT Director, UK Asset Management Team, Executive Committe
本卡描述如含来自官方页的原文片段,其版权归原作者,不在本站 CC-BY 4.0 许可范围(见关于与许可)。
- 落地页
- https://zenodo.org/records/5841834
- 许可证
- CC-BY-4.0 (置信:verified_official)
- 国内可访问性
-
国内直连:直连超时(慢或不稳定,非封锁证据) (2026-07-11 检测)
非直连:可达 (2026-07-11 检测)
「直连超时」表示检测窗口内未完成,系慢或不稳定证据,不构成封锁证据。 - 发布年份
- 2022
- 发布方
- Zenodo
- 设备类型
wind_turbine- PHM 任务
condition_monitoringanomaly_detection
传感器
| sensor_type: otherobserved_property: othermounting_note: SCADA 遥测(风速/功率/温度/转速等 10 分钟统计)+状态与工单记录 |
运行工况
| description: 运行风场连续采集(6×Senvion MM92),含停机/降载事件记录condition_type: otheris_varying: True |
关联论文(35 篇)
仅表示这篇论文与本数据集在文献网络中相邻, 不代表该论文确实使用了本数据集。
- A 3-year database of atmospheric measurements combined with associated operating parameters from a wind farm of 2 MW turbines including rotor geometry 2025 · 首发该数据集
- Kelmarsh wind farm data 2025 · 首发该数据集
- Kelmarsh wind farm data 2022 · 首发该数据集
- Kelmarsh wind farm data 2022 · 首发该数据集
- Kelmarsh wind farm data 2022 · 首发该数据集
- A multi dataset validation model for hybrid feature selection in wind energy maximum power point tracking systems 2026 · 用于方法验证
- A SCADA-Informed Framework Combining Machine Learning and Wind Speed Correction for Yaw Misalignment Detection 2026 · 用于方法验证
- An Ensemble of Neighbor-Informed Models for Wind Turbine Power Prediction with Uncertainty Quantification 2026 · 用于方法验证
- Physics‑informed clustering and ESD‑based outlier detection for wind turbine SCADA data 2026 · 候选引用(未核验)
- JOVET: Jointly optimized multi-variational mode embedded Transformer framework for wind-to-hydrogen LCOH forecasting 2026 · 候选引用(未核验)
- Explainable AI in Wind Turbine Fault Detection 2026 · 候选引用(未核验)
- Learning Degradation Dynamics from Incomplete Trajectories and Failure Statistics 2026 · 候选引用(未核验)
- Fourier Geometric Wind Power Forecasting with Numerical Weather Prediction 2026 · 候选引用(未核验)
- OffWindFM: A Conceptual Roadmap for Foundation Models in Offshore Wind Energy 2026 · 候选引用(未核验)
- Tyan-WP: A Wind Power Foundation Model for Ultra-Short-Term Probabilistic Forecasting 2026 · 候选引用(未核验)
- UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing 2026 · 候选引用(未核验)
- Fourier Geometric Wind Power Forecasting with Numerical Weather Prediction 2026 · 候选引用(未核验)
- Multi-temporal forecasting of wind energy production using artificial intelligence models 2025 · 使用该数据集
- Probability Density Function Control‐Based Deep Ensemble Learning for Wind Energy System Power Forecasting 2025 · 候选引用(未核验)
- Multi-step ahead wind power forecasting based on multi-feature wavelet decomposition and convolution-gated recurrent unit model 2025 · 候选引用(未核验)
仅列前 20 篇(首发/综述优先,按年份倒序);全量见 API。
溯源(9 条)
| 日期: 2026-07-08 |
| 来源链接: https://api.datacite.org/dois/10.5281/zenodo.5841834 日期: 2026-07-08 |
| 日期: 2026-07-08 |
| 日期: 2026-07-08 |
| 日期: 2026-07-10 |
| 日期: 2026-07-10 |
| 日期: 2026-07-11 |
| 日期: 2026-07-12 |
| 日期: 2026-07-25 |