故障检测的迁移学习及其在风力机SCADA数据中的应用

Q4 Energy
S. Simani, S. Farsoni, P. Castaldi
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引用次数: 0

摘要

风力发电装机容量在全球范围内不断增长。采用风力发电机组远程状态监测,提高了风机的正常运行时间,降低了维护成本。机器学习模型可以检测风力涡轮机中正在发生的损坏。因此,本文证明了在SCADA数据稀缺的情况下,跨涡轮迁移学习可以大大提高故障检测模型的准确性。特别是,它表明,结合来自涡轮机的知识与涡轮机的丰富的数据,可以更早地检测故障比现有技术的方法。训练故障检测模型需要大量的过去和现在的SCADA数据,但这些数据通常不可用或不能代表当前的操作行为。新投产的风电场缺乏以前运行的SCADA数据。由于控制软件更新或硬件更换,旧的涡轮机也可能缺乏代表性的SCADA数据。在这些事件之后,涡轮机的运行行为可能会发生重大变化,因此其SCADA数据不再代表其当前行为。因此,这项工作强调了如何在风力涡轮机之间重用和传递知识,以克服这种数据缺乏,并能够更早地发现风力涡轮机的故障。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Transfer Learning for Fault Detection with Application to Wind Turbine SCADA Data
The installed wind power capacity is growing worldwide. Remote condition monitoring of wind turbines is employed to achieve higher uptimes and lower maintenance costs. Machine learning models can detect developing damages in wind turbines. Therefore, this paper demonstrates that cross–turbine transfer learning can drastically improve the accuracy of fault detection models in turbines with scarce SCADA data. In particular, it shows that combining the knowledge from turbines with scarce and turbines with plentiful data enables earlier detection of faults than prior art methods. Training fault detection models require large amounts of past and present SCADA data but these data are often unavailable or not representative of the current operation behavior. Newly commissioned wind farms lack SCADA data from the previous operation. Due to control software updates or hardware replacements, older turbines may also lack representative SCADA data. After such events, a turbine’s operation behavior can change significantly so its SCADA data no longer represent its current behavior. Therefore, the work highlights how to reuse and transfer knowledge across wind turbines to overcome this lack of data and enable the earlier detection of faults in wind turbines.
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来源期刊
Journal of Nuclear Energy Science and Power Generation Technology
Journal of Nuclear Energy Science and Power Generation Technology Energy-Energy Engineering and Power Technology
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