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In actual line-commutated converter based high- voltage direct-current (LCC-HVDC) projects, multiple HVDC transmission lines are usually installed on the same tower to save transmission corridors and reduce construction costs. However, electromagnetic coupling effect between adjacent lines can cause commutation failure (CF) in the non-fault poles when a line or pole fails. Hence, this paper deeply investigates the mechanism and the prevention strategy of CFs caused by line coupling effect in single- and double-circuit HVDC systems. The influencing factors of line coupling effect and the coupling characteristics under fault conditions are analyzed first based on the modeling of parallel transmission lines and the traveling wave theory. Then the mechanism of CF caused by line coupling effect is investigated with the consideration of different fault conditions, different line structures, and different system operation modes. Based on the above analyses, prevention strategies for CFs of non-fault poles in single- and double-circuit systems are proposed. Finally, the simulation results verify the correctness of the theoretical analyses and the effectiveness of the proposed control strategies. [ABSTRACT FROM AUTHOR]

With the emergence of the hybrid multi-infeed high voltage direct current (HMIDC) system, there is complex dynamic interaction between the AC/DC system and converter stations, while the interaction mechanism of the HMIDC system is unclear. For this purpose, this paper establishes the HMIDC linearized model based on the motion equation concept. Then, after decoupling and reconstructing the interaction path of the HMIDC system, the damping coefficients are derived. The AC system strength and voltage source converter (VSC) terminal voltage controller parameters are selected to analyze the impacts on damping coefficients, and the interaction mechanism of the HMIDC system is analyzed. In order to solve the potential instability issue, an additional damping controller in the VSC terminal voltage loop is proposed to enhance the voltage stability of HMIDC system, which is realized by adjusting the reactive power/amplitude coupling damping coefficient. Finally, the RT-LAB-based hardware-in-the-loop experiments verify the accuracy of the interaction mechanism analysis of the HMIDC system and the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has exhibited huge potentials for hyperspectral image (HSI) classification due to its powerful nonlinear modeling and end-to-end optimization characteristics. Although the superior performance of DL-based methods has been witnessed, some limitations can still be found. On the one hand, existing DL frameworks usually resorted to first-order statistical features, whereas they rarely considered second-order or higher order statistical features. On the other hand, the optimization of complex hyperparameters (e.g., the layer number and convolutional kernel size) is time-consuming and a very tough task, making the designed DL framework unexplainable. To overcome these challenges, we propose a novel attention-based second-order pooling network (A-SPN). First, a first-order feature operator is designed to model the spectral–spatial information of HSI. Second, an attention-based second-order pooling (A-SOP) operator is designed to model discriminative and representative features. Finally, a fully connected layer with softmax loss is used for classification. The proposed framework can obtain second-order statistical features in an end-to-end manner. In addition, A-SPN is free of complex hyperparameters tuning, making it more explainable and easily equipped for classification tasks. Experimental results based on three common hyperspectral data sets demonstrate that A-SPN outperforms other traditional and state-of-the-art DL-based HSI classification methods in terms of generalization performance with limited training samples, classification accuracy, convergence rate, and computational complexity. [ABSTRACT FROM AUTHOR]

In a line-commutated converter based high-voltage direct-current (LCC-HVDC) system, the inverter control system usually includes constant extinction angle (CEA) controller, constant current (CC) controller, current error controller (CEC), and voltage-dependent current order limiter (VDCOL). However, the interaction influence of multiple controllers may cause repetitive commutation failure (CF). For this purpose, this paper deeply investigates the mechanism and the influencing factors of repetitive CF caused by controller interaction. It is found that the lower limit value of CEA controller can affect the control switching process and thus the probability of repetitive CF. Besides, the minimum gamma measurement (MGM) unit prevents the system from acquiring the real extinction angle in time, which results in the DC current fluctuation during the recovery process. Moreover, the probability of control-induced repetitive CF is much higher when the HVDC inverter is connected to a weak AC system or the AC fault is not serious but not cleared in time. Based on the above analyses, an improved control method is proposed for recovery performance improvement, which is mainly realized by modifying the lower and upper limits of the CEA controller during recovery. Finally, the simulation and experimental results verify the theoretical analyses and the proposed control method. [ABSTRACT FROM AUTHOR]