循序渐进的滚动差-Z-score和机器学习计数用于风力轮机基础监测
Renjie Li1, Xiangxing Lu1, Jizhang Zhao2,3,4
1Shandong Electric Power Engineering Consulting Institute Corp., Ltd., Jinan, China.
PloS one
|September 5, 2025
概括
这项研究引入了一种新的方法来检测风电场监测中的异常并填补缺少的数据. 它确保了可再生能源基础设施的结构健康评估.
科学领域:
- 工程
- 数据科学
- 可再生能源系统
背景情况:
- 结构健康监测 (SHM) 对工程结构至关重要,尤其是在可再生能源领域.
- 现场数据采集面临设备不稳定性和环境复杂性等挑战,导致数据异常和缺口.
- 风力发电场等结构的准确性能评估主要依赖于准确,完整的监测数据.
研究的目的:
- 解决风电场监测数据中的数据异常和缺口 (接钉拉伸,绳轴力,混凝土拉伸).
- 在具有挑战性的现场环境中开发和验证可靠的异常检测和数据归算方法.
- 提高长期安全评估的结构性监测数据的可靠性和完整性.
主要方法:
- 为有效检测异常提出了一种代滚动差异Z分数方法.
- 开发了一个机器学习归算框架,将线性插入和LightGBM结合起来进行数据重建.
- 在山东省的风电场强化项目中对现实数据进行了实验.
主要成果:
- 代滚动差异Z-score方法证明了强大的异常检测,即使数据损失高达80%.
- 对连续缺失的数据,归算框架实现了0.0214-0.0227的低平均平方误差 (MSE) 和0.14-0.15的根平均平方误差 (RMSE).
- 在连续丢失数据场景中,可靠的数据重建达到了50%的数据损失.
结论:
- 开发的方法为提高风电场监测数据的质量提供了可靠的解决方案.
- 增强数据完整性支持更准确的结构状况评估和安全评估.
- 这项研究有助于可再生能源基础设施的长期结构可靠性.
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