Vector Magnetograph Observations by the Solar Flare Telescope at Boao

We report that vector magnetograph (VMG) observations of the solar photosphere are being carride out by the Solar Flare Telescope (SOFT) at BOAO. The VMG uses a narrow band Lyot filter (FWHM=0 125A) for Stokes parameter (I, Q, U, V) observations to obtain longitudinal and transverse fields. We have obtained a filter convolved line profile of Fe I 6302 5 for VMG by changing the central wavelength of the Lyot filter, which is consistent with the Sacremento Peak spectral atlas data. Using the line profile, we have determined calibration coefficients of longitudinal and transverse fields by the line slope method. Then we have compared vector fields of AR8422 observed at BOAO with those at Mitaka. The comparison shows that longitudinal fields are very similar to each other, but transverse fields are a little different. Finally, we present Hα and magnetic observations of AR8419 during its flaring activity (M3 1/1B).

A Coarse Alignment Based on the Sliding Fixed-Interval Least Squares Denoising Method

The observation vectors in traditional coarse alignment contain random noise caused by the errors of inertial instruments,which will slow down the convergence rate.To solve the above problem,a real-time noise reduction method,sliding fixed-interval least squares(SFI-LS),is devised to depress the noise in the observation vectors.In this paper,the least square method,improved by a sliding fixed-interval approach,is applied for the real-time noise reduction.In order to achieve a better-performed coarse alignment,the proposed method is utilized to de-noise the random noise in observation vectors.First,the principles of proposed SFI-LS algorithm and coarse alignment are devised.A simulation test and turntable experiment were executed to demonstrate the availability of the designed method.It is indicated that,from the results of the simulation and turntable tests,the designed algorithm can effectively reduce the random noise in observation vectors.Therefore,the proposed method can enhance the performance of coarse alignment availably.

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.

New adaptive vector control methods for induction motors with simpler structure and better performance

This paper deals with the vector control, including both the direct vector control(DVC) and the indirect vector control(Id VC),of induction motors. It is well known that the estimation of rotor flux plays a fundamental role in the DVC and the estimation of rotor resistance is vital in the slip compensation of the Id VC. In these estimations, the precision is significantly affected by the motor resistances. Therefore, online estimation of motor resistances is indispensable in practice.For a fast estimation of motor resistances, it is necessary to slow down the convergence rate of the current estimate. On the other hand, for a fast estimation of the rotor flux, it is necessary to speed up its convergence rate. It is very difficult to realize such a trade-off in convergence rates in a full order observer.In this paper, we propose to decouple the current observer from the flux observer so as to realize independent convergence rates. Then, the resistance estimation algorithm is applied to both DVC and Id VC. In particular, in the application to Id VC the flux observer needs not be used, which leads to a simpler structure. Meanwhile, independent convergence rates of current observer and flux observer yield an improved performance. A superior performance in the torque and flux responses in both cases is verified by numerous simulations.