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Data-driven modelling, control, and fault detection of wind turbine systems

机译:数据驱动的风力发电机系统建模,控制和故障检测

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摘要

In this paper, data-driven system modelling, control, and fault detection technique for wind turbine systems are researched. The developed algorithm is to recursively update system parameters using predictor-based system identification (SID) technique. Updated system parameters are used to design subspace predictive controller for wind turbine systems with constraint on pitch angle and generator torque. The usefulness of this application is highlighted through simulation on a benchmark example, 5 MW NREL/Upwind reference turbine. It shows that developed algorithm which streamlines controller design process from identification to fault detection is useful for making wind turbine systems being tolerant to faults.
机译:本文研究了风力涡轮机系统的数据驱动系统建模,控制和故障检测技术。所开发的算法是使用基于预测变量的系统标识(SID)技术来递归更新系统参数。更新的系统参数用于设计受桨距角和发电机转矩约束的风力涡轮机系统的子空间预测控制器。通过在基准示例5 MW NREL /逆风参考涡轮机上进行仿真,突出了此应用程序的实用性。结果表明,所开发的算法可简化从识别到故障检测的控制器设计过程,有助于使风力涡轮机系统具有容错能力。

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