Separate-Structure UDE-Based Current Resonant Control Strategy on $LCL$-Type Grid-Tied Inverters With Weighted Average Current Method for Improved Injected Current Quality and Robustness

2020 ◽  
Vol 35 (12) ◽  
pp. 13641-13651
Author(s):  
Yongkang Xiong ◽  
Yongqiang Ye ◽  
Yongfeng Cao ◽  
Yuheng Wu
2021 ◽  
Vol 7 ◽  
pp. 292-299
Author(s):  
Hanquan Zhang ◽  
Hongyu Zhu ◽  
Dongdong Zhang ◽  
Haisen Zhao ◽  
Yanli Zhang ◽  
...  

Author(s):  
Shengqing Li ◽  
Xinluo Li ◽  
Xiaobao Lee

AbstractIn the weak current network environment, the existence of power network impedance will reduce the current control stability margin of LCL grid connected inverter, the stability margin will gradually deteriorate as the inductive component of the power network impedance increases, leading to system instability. In this paper, an improved weighted average current control strategy is proposed, which is based on the active damping control of the voltage feedforward of the charged network. In the new control strategy, with the zero-pole cancellation method, a new weighting coefficient related only to the parameters of LCL filter is obtained, which reduces the order of the system, suppresses the influence of power network impedance, and improves the stabilization of the system, getting better inverter output current quality. Then the influence of power network impedance on the control system of grid connected inverter is analyzed, and the feed-forward control of grid voltage is improved to improve the stability of inverter in weak current network. The Bode diagram analysis and MATLAB simulation results proved that the proposed control strategy is feasible.


2017 ◽  
Vol 40 (9) ◽  
pp. 2813-2820
Author(s):  
Heng Zhang ◽  
Wei Xing Zheng

This paper investigates the problem of sensor power control for the scenario of remote state estimation. Most existing works mainly focus on designing sensor power scheduling schemes to minimize average estimation errors or terminal estimation errors when the sensor’s transmission capability is restrained by the energy budget. By contrast with these objectives, we aim to balance the cost of sensor power and the quality of remote estimation in this work. Specifically, we are interested in the problem that minimizes the expected weighted average sum of the remote state estimation errors and the sensor’s transmission power costs in an infinite time horizon. A Markov decision process framework is adopted to present the structure of the optimal power control strategy. However, it is not possible to find an analytical expression of the optimal solution. Thus, we further present an approximation solution and then derive a suboptimal sensor power control strategy. Finally, a simulation example is provided to show the effectiveness of our designed sensor control strategy.


Sign in / Sign up

Export Citation Format

Share Document