Combination of Model-based Observer and Support Vector Machines for Fault Detection of Wind Turbines

Support vector machines and a Kalman-like observer are used for fault detection and isolation in a variable speed horizontalaxis wind turbine composed of three blades and a full converter. The support vector approach is data-based and is therefore robust to process knowledge. It is based on structural risk minimization which enhances generalization even with small training data set and it allows for process nonlinearity by using flexible kernels. In this work, a radial basis function is used as the kernel. Different parts of the process are investigated including actuators and sensors faults. With duplicated sensors, sensor faults in blade pitch positions,generator and rotor speeds can be detected. Faults of type stuck measurements can be detected in 2 sampling periods. The detection time of offset/scaled measurements depends on the severity of the fault and on the process dynamics when the fault occurs. The converter torque actuator fault can be detected within 2 sampling periods. Faults in the actuators of the pitch systems represents a higher difficulty for fault detection which is due to the fact that such faults only affect the transitory state(which is very fast) but not the final stationary state. Therefore, two methods are considered and compared for fault detection and isolation of this fault: support vector machines and a Kalman-like observer. Advantages and disadvantages of each method are discussed. On one hand, support vector machines training of transitory states would require a big amount of data in different situations, but the fault detection and isolation results are robust to variations in the input/operating point. On the other hand, the observer is model-based, and therefore does not require training, and it allows identification of the fault level, which is interesting for fault reconfiguration. But the observability of the system is ensured under specific conditions, related to the dynamics of the inputs and outputs. The whole fault detection and isolation scheme is evaluated using a wind turbine benchmark with a real sequence of wind speed.

Experimental investigation of control updating period monitoring in industrial PLC-based fast MPC： Application to the constrained control of a cryogenic refrigerator

在这份报纸，为在模型更新时期的控制的联机改编的一个最近出版的计划的完全的工业确认预兆的控制被建议。服务在确认的工业过程是被用来使涉及粒子加速器或试验性的核反应堆的在上售票员凉下来的一个低温实验法的冰箱。二最近预言特征被验证：它对有时更好的第一个状态使用更少有效(每重复) 优化器如果效率的缺乏是由更新的增加的 over-compensated，控制频率。第二是为一个给定的解答者，它值得监视基于费用功能的联机测量行为更新时期的控制。

A phase-locked loop using ESO-based loop filter for grid-connected converter: performance analysis

An extended state observer(ESO)-based loop filter is designed for the phase-locked loop(PLL)involved in a disturbed grid-connected converter(GeC).This ESO-based design enhances the performances and robustness of the PLL,and,therefore,improves control performances of the disturbed GeCs.Besides,the ESO-based LF can be applied to PLLs with extra filters for abnormal grid conditions.The unbalanced grid is particularly taken into account for the performance analysis.A tuning approach based on the well-designed PI controller is discussed,which results in a fair comparison with conventional PI-type PLLs.The frequency domain properies are quantitatively analysed with respeet to the control stability and the noises rejection.The frequency domain analysis and simulation results suggesti that the performances of the generated ESO-based controllers are comparable to those of the PI control at low frequency,while have better ability to atenuate high-frequency measurement noises.The phase margin decreases slightly,but remains acceptable.Finally,experimental tests are conducted with a hybrid power hardwarein-the-loop benchmark,in which balanced/unbalanced cases are both explored.The obtained results prove the effectiveness of ESO based PLLs when applied to the disturbed GeC.