机器学习的水力发电初创公司的疲劳损害减少
Till Muser1, Ekaterina Krymova2, Alessandro Morabito3
1Swiss Data Science Center, EPFL & ETH Zürich, Andreasstrasse 5, Zurich, Switzerland.
Nature communications
|March 27, 2025
概括
本研究介绍了一种数据驱动的方法,以优化水力发电启动,显著减少轮机疲劳损伤. 这种进步增强了水电运营,并确保了可再生能源转型期间的电网稳定性.
科学领域:
- 可再生能源系统可再生能源系统
- 机械工程 机械工程
- 电力系统的稳定性 电力系统的稳定性
背景情况:
- 水力发电对于全球能源 (17%) 和电网稳定性至关重要,提供必要的辅助服务.
- 对电网服务的需求不断增加,使得水电系统需要适应动态变化和设计之外的条件.
- 液压机器的疲劳损伤,特别是在暂时启动时,是一个重要的操作挑战.
研究的目的:
- 开发一种数据驱动的方法,以确定最佳的过渡性启动轨迹.
- 为了尽量减少水力发电轮机在启动阶段的疲劳损伤.
- 提高水电系统的运行灵活性和安全性.
主要方法:
- 使用了一种机器学习模型,通过从小型模型轮机的实验应力数据进行训练.
- 根据训练模型开发和优化过渡性启动轨迹.
- 通过数值模拟和实验测试验证了优化的轨迹.
主要成果:
- 优化的启动轨迹显然可以减少短暂运行期间的疲劳损伤.
- 数字和实验结果证实了数据驱动方法的有效性.
- 观察到,启动过程中产生的损害显著减少.
结论:
- 数据驱动的方法为水电运营和维护提供了有意义的进步.
- 优化的启动程序有助于在水力发电中安全整合更高的运营灵活性.
- 这种方法通过提高水电的可靠性来支持全球向可再生能源的转变.
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