论文标题
深度学习方法来预测风力涡轮机的故障
A Deep Learning Approach Towards Prediction of Faults in Wind Turbines
论文作者
论文摘要
随着传统能源来源的上涨，世界正在朝着包括风能在内的可持续能源迈进。风力涡轮机由几个电气和机械组件组成，并经历了大量不规则载荷，使它们的操作行为有时不一致。操作和维护（O＆M）是监视涡轮机行为不一致的关键因素，以预测和防止在不久的将来可能发生的任何初期故障。在过去的十年中，机器学习已应用于风能领域，以分析，诊断和预测风力涡轮机故障。特别是，我们遵循将涡轮机的性能建模为功率曲线的想法，其中任何偏离曲线的功率输出都可以看作是性能错误。现有的使用此想法的工作使用了涡轮机的监督控制与采集系统（SCADA）系统的数据，以使用回归技术过滤和分析故障和警报数据。与以前的工作相反，我们探讨了如何仅从开放访问气象数据中应用深度学习来预测故障预测。
With the rising costs of conventional sources of energy, the world is moving towards sustainable energy sources including wind energy. Wind turbines consist of several electrical and mechanical components and experience an enormous amount of irregular loads, making their operational behaviour at times inconsistent. Operations and Maintenance (O&M) is a key factor in monitoring such inconsistent behaviour of the turbines in order to predict and prevent any incipient faults which may occur in the near future. Machine learning has been applied to the domain of wind energy over the last decade for analysing, diagnosing and predicting wind turbine faults. In particular, we follow the idea of modelling a turbine's performance as a power curve where any power outputs that fall off the curve can be seen as performance errors. Existing work using this idea has used data from a turbine's Supervisory Control & Acquisition (SCADA) system to filter and analyse fault & alarm data using regression techniques. In contrast to previous work, we explore how deep learning can be applied to fault prediction from open access meteorological data only.
