In order to ensure that wind turbines could be online under the condition of power grid transient fault, the technology of low voltage ride-through (LVRT) for wind turbine has become a research focus of experts and scholars at home and abroad. In current wind turbine control strategy, the PI parameters of the control system keep unchanged when the wind turbine is in the process of LVRT, which affects the performance of the control system. Therefore, the adaptive PI parameter adjustment system based on BP neural network is proposed, which realize the adaptive adjustment of PI parameters in the process of LVRT. The simulation model of the LVRT process was built, the simulation results show that this control algorithm can effectively restrain the current oscillation caused by the voltage sag, shorten the fault recovery time of the system, and has a good dynamic performance. Besides, the adaptability and robustness of the system has been increased, which has improved the low voltage ride-through capability of the system.
Volatile Ovonic threshold switching (OTS) selectors have been regarded as the critical component of highly integrated threedimensional (3D) cross-point array nonvolatile memory systems. However, relatively high leakage current hinders the further reduction of power consumption in the crossbar array. In addition, the threshold voltage drift phenomenon hinders the improvement of device reliability. Utilizing the buffer layer can effectively reduce the interaction between electrodes and the active layer in the cross-point architecture. Here, it manifests that leakage current can be reduced to ∼0.4 nA with a 5 nm thick amorphous carbon layer as a buffer layer in the GeAsSe-based OTS device, where the carbon layer stabilizes the composition of GeAsSe during the electrical switching cycles. It is also found that the carbon layer leads to a lower threshold voltage drift (35.6 mv/dec) and excellent endurance (>10 9 cycles with ∼0.4 nA ON-state current). The conduction mechanism analysis demonstrates that the inhibition of the carbon layer on drift originates from the high barrier height from delocalized states transformed into localized states. This work clearly demonstrates the role of the carbon layer and facilitates future 3D crossbar-storage technology applications.
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