论文标题
通过基于模型的故障诊断和子空间预测重复控制，浮动海上风力涡轮机的快速自适应故障容纳
Fast Adaptive Fault Accommodation in Floating Offshore Wind Turbines via Model-Based Fault Diagnosis and Subspace Predictive Repetitive Control
论文作者
论文摘要
由于漂浮的海上风力涡轮机（FOWTS）在深水中运行，并受到压力巨大的风和波动诱导的负载，因此它们比陆上的同行更容易经历故障和故障。特别是，音高系统可能会遇到螺距执行器卡住（PAS）的故障类型，这将导致完全失去控制权。在本文中，通过整合基于模型的故障诊断（FD）方案和子空间预测重复对照（SPRC）来开发一种新颖的快速和自适应解决方案。 FD的角色是快速检测并隔离失败的音高执行器。基于故障隔离结果，预先调整的自适应SPRC可以在线切换，以代替现有的SPRC，其参数的初始值已离线调整以匹配特定的故障情况。之后，SPRC采用子空间识别来在移动的时间窗口上连续识别风力涡轮机的线性模型，从而制定自适应控制法以减轻PAS诱导的载荷。结果表明，开发的体系结构可以大大减少PAS诱导的叶片载荷。更重要的是，减少PAS引起的负载所需的时间大大缩短，从而避免了在适应时间内对其他组件的进一步损害，并允许继续发电。
As Floating Offshore Wind Turbines (FOWTs) operate in deep waters and are subjected to stressful wind and wave induced loads, they are more prone than onshore counterparts to experience faults and failure. In particular, the pitch system may experience Pitch Actuator Stuck (PAS) type of faults, which will result in a complete loss of control authority. In this paper, a novel fast and adaptive solution is developed by integrating a model-based Fault Diagnosis (FD) scheme and the Subspace Predictive Repetitive Control (SPRC). The FD role is to quickly detect and isolate the failed pitch actuator. Based on the fault isolation results, a pre-tuned adaptive SPRC is switched online in place of the existing one, whose initial values of the parameters has been tuned offline to match the specific faulty case. After that, SPRC employs subspace identification to continuously identify a linear model of the wind turbine over a moving time window, and thereby formulate an adaptive control law to alleviate the PAS-induced loads. Results show that the developed architecture allows to achieve a considerable reduction of the PAS-induced blade loads. More importantly, the time needed to reduce the PAS-induced loads are significantly shortened, thus avoiding further damage to other components during the adaption time and allowing continued power generation.
