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A Rapid Method for Modeling a Variable Cycle Engine
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风力轮机数据库用于智能操作和维护策略
Pere Marti-Puig1, Alejandro Blanco-M2, Jordi Cusidó2,3
1Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, 08500, Vic, Catalonia, Spain. pere.marti@uvic.cat.
Scientific data
|February 29, 2024
概括
研究人员现在可以从五个风力轮机访问一个为期三年的监督控制和数据采集 (SCADA) 数据库. 这一数据集有助于开发风能系统的智能运行和维护策略.
科学领域:
- 可再生能源工程可再生能源工程
- 数据科学用于工业应用.
背景情况:
- 智能操作和维护策略对于优化风力轮机性能和寿命至关重要.
- 现有的数据集可能无法捕获高级分析所需的全面,高分辨率数据.
研究的目的:
- 提供一个广泛的三年监督控制和数据采集 (SCADA) 数据库,来自五个Fuhrländer FL2500 2.5MW风力轮机.
- 为研究人员提供一个有价值的资源,用于开发风能系统的智能运行和维护策略.
主要方法:
- 从78个传感器收集了312个类似的变量,分隔5分钟,持续了3年.
- 记录每个传感器的最小,最大,平均值和标准偏差.
- 包括系统,子系统和描述的报警事件.
主要成果:
- 现在可以使用一个全面的SCADA数据库.
- 提供在Matlab,R和Python中下载特定数据子集的功能.
- 一个例子展示了使用变速箱变量和正常性建模检测转子故障的方法.
结论:
- 提出的数据库是风能研究的重要资源.
- 正常性建模方法对风力轮机故障的早期检测有希望.
- 可访问的数据和工具促进了智能风力轮机运行和维护方面的进步.
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