Secondary Control for DC Microgrids with Optimal Sparse Feedback

Author(s):  
Jianzhe Liu ◽  
Xiaonan Lu ◽  
Chen Chen
Author(s):  
Pouya Shafiee ◽  
Yousef Khayat ◽  
Yazdan Batmani ◽  
Qobad Shafiee ◽  
Josep M Guerrero

2018 ◽  
Vol 9 (4) ◽  
pp. 1857-1869 ◽  
Author(s):  
Fanghong Guo ◽  
Qianwen Xu ◽  
Changyun Wen ◽  
Lei Wang ◽  
Peng Wang

2018 ◽  
Vol 7 (2) ◽  
pp. 227-242 ◽  
Author(s):  
Fei GAO ◽  
Ren KANG ◽  
Jun CAO ◽  
Tao YANG

2016 ◽  
Vol 31 (9) ◽  
pp. 6658-6673 ◽  
Author(s):  
Panbao Wang ◽  
Xiaonan Lu ◽  
Xu Yang ◽  
Wei Wang ◽  
Dianguo Xu

Author(s):  
Pouya Shafiee ◽  
Babak Abdolmaleki ◽  
Mojtaba Ahmadi ◽  
Qobad Shafiee

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1992
Author(s):  
Guannan Lou ◽  
Yinqiu Hong ◽  
Shanlin Li

This paper studies the distributed secondary control of DC microgrids (MGs) in the case of asynchronous sampling, including both the stability condition and accurate consensus algorithm. The asynchrony means that the update actions of each distributed generation (DG) based on the local information and information received from neighbors are independent of the actions of others at sampled discrete times, which would cause deviation from the accurate convergence and even lead to instability in the worst case. First, a small-signal model of MG installed with secondary voltage control is established to include the individual sampling periods. A stability criterion based on the periodic continuity of sampling instant offset is thus formulated to reveal a stability mapping of multiple sampling. By quantifying the accuracy deviations caused by the asynchrony, an improved ratio consensus strategy is proposed that allows the deviation to be estimated accurately via an auxiliary signal and compensated with respect to the eventual equilibrium to produce an exact solution. Our approach customizes the stability and accuracy for distributed secondary control considering asynchronous sampling in MG, which has been ignored in most existing literature. The effectiveness of the proposed methodology is verified by simulations.


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