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Robust fault estimation in wind turbine systems using GA optimisation

机译:基于遗传算法的风电系统鲁棒故障估计

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Wind turbine system is a safety-critical system, which has the demand to improve the operating reliability and reducing the cost caused by the shut-down time and component repairing. As a result, condition monitoring and fault diagnosis have received much attention for wind turbine energy systems. Noticing that environmental disturbances are unavoidable, therefore how to improve the robustness of a fault diagnosis scheme against disturbancesoises has been a key issue in fault diagnosis community. In this investigation, a robust fault estimation approach with the aid of eigenstructure assignment and genetic algorithm (GA) optimization is presented so that the estimation error dynamics has a good robustness against disturbances. A simulation study is carried out for a 5MW wind turbine dynamic model, which has demonstrated the effectiveness of the proposed techniques.
机译:风力涡轮机系统是对安全性至关重要的系统,其需要提高运行可靠性并减少因停机时间和组件维修所引起的成本。结果,状态监测和故障诊断已受到风力涡轮机能源系统的广泛关注。注意到环境干扰是不可避免的,因此如何提高故障诊断方案针对干扰/噪声的鲁棒性已成为故障诊断界的关键问题。在这项研究中,提出了一种基于特征结构分配和遗传算法(GA)优化的鲁棒故障估计方法,从而使估计误差动态特性具有良好的抗干扰鲁棒性。对5MW风力发电机动态模型进行了仿真研究,证明了所提出技术的有效性。

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