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 ...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.展开更多
A vertical ship lift under earthquake excitation may suffer from a whipping effect due to the sudden change of building lateral stiffness at the top of the ship lift towers.This paper proposes a roof magnetorheologica...A vertical ship lift under earthquake excitation may suffer from a whipping effect due to the sudden change of building lateral stiffness at the top of the ship lift towers.This paper proposes a roof magnetorheological(MR)intelligent isolation system to prevent the seismic whipping effect on machinery structures.Theoretically,the dynamic models of MR damper and the mechanical model of ship lift was established,the inverse neural network controlling algorithm was proposed and the fundamental semi-active control equation for the Three-Gorges ship lift where the MR intelligent isolation system was installed was deduced.Experimentally,the experimental model of the ship lift was given,the vibrating table experiment of the MR intelligent isolation system controlling the whipping effect was carried out and the results of the inverse neural network control strategy and passive isolation strategy were compared.In practical aspect,the large-scale MR damper(500 kN)and a sliding support with limited stiffness were designed and fabricated.It was proven that the MR intelligent isolation system with proper control strategy can greatly reduce the seismic whipping effect on the top workshop of the ship lift and be simple and effective enough to be applied to real engineering structures.展开更多
文摘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.
基金supported by the National Natural Science Foundation of China(Grant No.50708086).
文摘A vertical ship lift under earthquake excitation may suffer from a whipping effect due to the sudden change of building lateral stiffness at the top of the ship lift towers.This paper proposes a roof magnetorheological(MR)intelligent isolation system to prevent the seismic whipping effect on machinery structures.Theoretically,the dynamic models of MR damper and the mechanical model of ship lift was established,the inverse neural network controlling algorithm was proposed and the fundamental semi-active control equation for the Three-Gorges ship lift where the MR intelligent isolation system was installed was deduced.Experimentally,the experimental model of the ship lift was given,the vibrating table experiment of the MR intelligent isolation system controlling the whipping effect was carried out and the results of the inverse neural network control strategy and passive isolation strategy were compared.In practical aspect,the large-scale MR damper(500 kN)and a sliding support with limited stiffness were designed and fabricated.It was proven that the MR intelligent isolation system with proper control strategy can greatly reduce the seismic whipping effect on the top workshop of the ship lift and be simple and effective enough to be applied to real engineering structures.
