Grid connected wind turbines may produce flicker during continuous operation. This paper presents a simulation model of a MW-level variable speed wind turbine with a doubly fed induction generator developed in the simulation tool of PSCAD/EMTDC. Flicker emission of variable speed wind turbines with doubly fed induction generators is investigated during continuous operation, and the dependence of flicker emission on mean wind speed, wind turbulence intensity, short circuit capacity of grid and grid impedance angle are analyzed. A comparison is done with the fixed speed wind turbine, which leads to a conclusion that the factors mentioned above have different influences on flicker emission compared with that in the case of the fixed speed wind turbine. Flicker mitigation is realized by output reactive power control of the variable speed wind turbine with doubly fed induction generator. Simulation results show the wind turbine output reactive power control provides an effective means for flicker mitigation regardless of mean wind speed, turbulence intensity and short circuit capacity ratio.Index Terms-Doubly fed induction generator, flicker, variable speed wind turbine.

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The fast development of wind power generation brings new requirements for wind turbine integration into the network. After clearance of an external short-circuit fault, gridconnected wind turbines should restore their normal operation without power loss caused by disconnections.This article concentrates on the transient stability of variable speed wind turbines with doubly fed induction generators (DFIGs) at an external short-circuit fault. A simulation model of a MW-level variable speed wind turbine with a DFIG developed in PSCAD/EMTDC is presented and the control and protection schemes are described in detail. The transient process of grid-connected wind turbines with DFIGs at an external shortcircuit fault is analysed, and in critical post-fault situations a measure is proposed for the voltage recovery of DFIG wind turbines after fault clearance. Simulation results demonstrate that in uncritical post-fault situations the control schemes are able to restore the wind turbine's normal operation without disconnections.It is also proved that the proposed measure is effective in re-establishing the voltage at the wind turbine terminal in critical post-fault situations.

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In remote sensing image classification, distance measurements and classification criteria are equally important; and less accuracy of either would affect classification accuracy. Remote sensing image classification was performed by combining support vector machine (SVM) and [Formula: see text]-nearest neighbor (KNN). This was based on the separability of classes using SVM and the spatial and spectral characteristics of remote sensing data. Moreover, a distance formula is proposed as the measure criterion that considers both luminance and direction of the vectors. First, the SVM is trained and the support vectors (SVs) are obtained for each class. In the testing phase, newly tested samples were entered, and average distance between the test samples and SVs for each class are calculated using the distance formula. Finally, decisions are made to classify the test samples into the class with minimal average distance. This procedure is repeated until all test samples are classified. In the combinatorial algorithm, the nearest neighbor classifier is used to classify a testing sample, i.e. the average Euclidean distance between the testing data point to each set of SVs from different categories is calculated and the nearest neighbor classifier identifies the category with minimum distance. The proposed combinatorial algorithm has advantages over the conventional KNN for eliminating the [Formula: see text] parameter selection problem and reducing heavy learning time. A comparison is carried out with SVM, KNN and Spectral Angle Mapper (SAM) classification using ALOS/PALSAR and PSM images, and the effectiveness of the proposed algorithm is demonstrated.

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