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In recent years, considerable advances were made in wind power generation. The growing penetration of wind power makes it necessary for wind turbines to maintain continuous operation during voltage dips, which is stated as the low-voltage ride-through (LVRT) capability. Doubly fed induction generator (DFIG)-based wind turbines (DFIG-WTs), which are widely used in wind power generation, are sensitive to disturbances from the power grid. Therefore, several kinds of protection circuits and control methods are applied to DFIG-WTs for LVRT capability enhancement. This paper gives a comprehensive review and evaluation of the proposed LVRT solutions used in DFIG-WTs, including external retrofit methods and internal control techniques. In addition, future trends of LVRT solutions are also discussed in this paper.

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Electric vehicles (EVs) have rapidly developed in recent years and their penetration has also significantly increased, which, however, brings new challenges to power systems. Due to their stochastic behaviors, the improper charging strategies for EVs may violate the voltage security region. To address this problem, an optimal EV charging strategy in a distribution network is proposed to maximize the profit of the distribution system operators while satisfying all the physical constraints. When dealing with the uncertainties from EVs, a Markov decision process (MDP) model is built to characterize the time series of the uncertainties and then the deep deterministic policy gradient based reinforcement learning technique is utilized to analyze the impact of uncertainties on the charging strategy. Finally, numerical results verify the effectiveness of the proposed method.

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