This paper proposes an improved direct power control (DPC) strategy of grid-connected wind-turbine-driven doubly fed induction generators (DFIGs) when the grid voltage is unbalanced. The DPC scheme is based on the sliding mode control (SMC) approach, which directly regulates the instantaneous active and reactive powers in the stator stationary reference frame without the requirement of either synchronous coordinate transformation or phase angle tracking of grid voltage. The behavior of DFIGs by the conventional SMC-DPC, which takes no negative-sequence voltage into consideration, is analyzed under unbalanced grid voltage conditions. A novel power compensation method is proposed for the SMC-based DPC during network unbalance to achieve three selective control targets, i.e., obtaining sinusoidal and symmetrical stator current, removing stator interchanging reactive power ripples and canceling stator output active power oscillations, respectively. The active and reactive power compensation components are calculated via a simple method and the proposed three control targets can be achieved, respectively, without the need of extracting negative-sequence stator current components. Experimental results on a 2 kW DFIG prototype are presented to verify the correctness and validity of the proposed control strategy and power compensation method.Index Terms-Direct power control (DPC), doubly fed induction generator (DFIG), power compensation, sliding mode control (SMC), unbalanced grid voltage, wind turbine. NOMENCLATURE U s , V r
Abstract:With the rapid development of wind power generation, the inertial response of wind turbines (WTs) has become a topic of wide concern recently, due to its influence on grid frequency dynamics and stability. This paper proposes and defines the inner potential to summarize and understand the inertia control methods and inertial response of type-3 and type-4 WTs, which is analogous to typical synchronous generators (SGs), to make it more easy to understand by system operators and engineers with a traditional power system background. The dynamics of the defined inner potential of the wind turbine without any inertia control is different from SGs, thus the electromechanical inertia is completely hidden. The rapid power control loop and synchronization control loop are the major reasons that the WT's inertial response is disenabled. On the basis of the defined inner potential's dynamic, the existing inertia control method for WTs are reviewed and summarized as three approaches, i.e., optimizing the power control or synchronization control or both. At last, the main challenges and issues of these inertia controls are attempted to explain and address.
For brain computer interfaces (BCIs) research, the classification of motor imagery brain signals is a major and challenging step. Based on the traditional sparse representation classification, a classification algorithm of electroencephalogram (EEG) based on sparse representation and convolution neural network is proposed by this paper. For the EEG signal, firstly, the features of the signal are obtained through the common spatial pattern (CSP) algorithm, and then the redundant dictionary with sparse representation is constructed based on these features. The sparse representation of the EEG signal is completed and the sparse features can be obtained. Finally, the sparse features are transformed into two dimensional signals, and the convolution neural network is used to complete the classification of EEG signals. Using the dataset downloaded from the website of BCI competition III (dataset IVa), for two types of EEG signals, the experiments show that the recognition accuracy of the method is over 80, and the recognition accuracy is better than that of the traditional SRC algorithm.
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