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With the fault ride through (FRT) requirement of grid codes, wind generators must stay connected and provide the reactive and active power support during and after the grid faults. The doubly fed induction generators (DFIGs) usually employ an active rotor crowbar to ride-through the faults. The solution works well to protect the DFIG itself, but it has shortcomings in the power support. In the paper, a coordinated FRT control strategy is investigated to improve the power support capability of the DFIGs under the fault conditions. In this strategy, a seamless switch is designed to resume the power control mode after the short-term crowbar interruption. Additional compensation terms are inserted into the converter's control loops to relieve the side-effect of rotor transient current. The coordinated control of dcchopper circuit and rotor-side converters is proposed to keep the dc-link voltage within its acceptable range. Moreover, a novel voltage limiter is designed with consideration of the conflicting effect of rotor transient current, converter's rating constraints and desired power goals. Compared with the conventional crowbar-based strategies, the proposed strategy can fully utilise the DFIG's potential to generate reactive and active power effectively. These performances have been demonstrated through the simulation and experimental tests.

In this study, an autonomous robot navigation system is designed for live working on distribution line. The developed system features a real‐time detection and motion planning system, incorporating a manipulator capable of grasping power components. In order to accurately identify targets, the authors propose an object detection method based on the Larger Scale ‘You Only Look Once’ Version 4 (LS‐YOLOv4) algorithm for detecting the insulators and drop fuses. The LS‐YOLOv4 extracts features of power components by Convolutional Neural Network (CNN), and then performs feature fusion. Then the authors develop a motion planning method based on the Node Control Optimal Rapidly Exploring Random Trees (NC‐RRT*), which can drive the robot to realise the autonomous robot motion planning and obstacle avoidance. On the grasping function, the authors present a reliable Lightweight‐based Convolutional Neural Network (L‐CNN) grasping point detection method. Finally, the authors evaluate fully autonomous robotic system in both simulated and real‐world experiments. The experimental results demonstrate that the proposed system can effectively identify the target and complete the grasping task in an efficient way. Notably, the proposed motion planning method can take into account both planning efficiency and accuracy to manipulation tasks.