从加速和风速SCADA数据中获得风力轮机基础的旋转刚度
Jiazhi Dai1,2, Mario Rotea2,3, Nasser Kehtarnavaz1,2
1Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080, USA.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
本研究介绍了一种具有成本效益的方法,用于使用SCADA数据监测风力轮机基础的健康状况. 它准确地估计了旋转刚度,并检测了损坏,提高了运行安全.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 可再生能源可再生能源是可再生能源.
背景情况:
- 风力轮机基础的健康对于运行安全至关重要.
- 目前评估基础刚性的方法非常昂贵,通常需要专门的传感器.
研究的目的:
- 开发一种具有成本效益的方法来估计风力轮机基础的旋转刚度.
- 为了利用易于获取的SCADA数据 (加速和风速) 进行基础健康监测.
- 为了能够及早检测到基础的恶化.
主要方法:
- 使用卷积神经网络 (CNN) 模型将加速和风速数据映射到时刻和倾斜值.
- 旋转刚度是通过时刻-倾斜平面的线性回归来估计的.
- 一个自编码器生成AI网络被用来创建用于重新训练模型的合成数据.
主要成果:
- 美国有线电视新闻网 (CNN) 模型的硬度估计平均在基准真实值的7%以内.
- 使用合成数据进行重新训练,可以检测度下降,但不能确定它们的确切大小.
- 与传统的基于传感器的方法相比,这种方法可以显著降低成本.
结论:
- 拟议的方法提供了一种可行且经济的方法,用于使用SCADA数据监测风力轮机基础的刚度.
- 这种技术可以作为基础退化早期预警系统,提高风电场的整体安全性和维护.
- 在SCADA数据分析中整合AI为可再生能源部门的预测性维护提供了新的可能性.
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