Novel distributed state estimation method for the AC-DC hybrid microgrid based on the Lagrangian relaxation method

被引:12
|
作者
Ling Ping [1 ]
Kong Xiangrui [2 ]
Fang Chen [1 ]
Yan Zheng [2 ]
机构
[1] State Grid Shanghai Municipal Elect Power Co, Shanghai, Peoples R China
[2] Shanghai Jiao Tong Univ, Dept Elect Engn, Key Lab Control Power Transmis & Convers, Minist Educ, Shanghai, Peoples R China
来源
基金
国家重点研发计划;
关键词
estimation theory; power system control; power system state estimation; state estimation; distributed power generation; AC-DC hybrid microgrid; centralised state estimation algorithms; novel distributed state estimation algorithm; three-stage state estimation model; three-stage state estimation theory; nonlinear state estimation progress; linear state estimation problem; Lagrangian relaxation method; three-stage distributed state estimation method; system state estimation; CLASSIFICATION;
D O I
10.1049/joe.2018.9329
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The AC-DC hybrid microgrid is a credible evolution path for the microgrid. State estimation in complex distribution network is a significant foundation for the safe operation. In AC-DC hybrid microgrid, centralised state estimation algorithms have quite a few deficiencies such as large communication capacity and privacy protection. In response to these obstacles, this paper proposed a novel distributed state estimation algorithm. First, a three-stage state estimation model was established for AC-DC hybrid microgrid, in which the second stage is a nonlinear transform for the linearisation of the nonlinear state estimation progress so that the first and third stage can solve the linear state estimation problem. Furthermore, the Lagrangian relaxation method was ultilised for the distributed problem solution. In the proposed algorithm, AC subsystems and DC subsystems respectively execute state estimation through local information, and the overall consistency of the system state estimation can be eventually achieved according to the transfer iteration of the boundary information. The computational efficiency and convergence of the proposed method was ensured owing to the combination of bilinear theory and Lagrangian relaxation. Simulation results indicate that the proposed method can achieve its goal and be superior to existing method in efficiency.
引用
收藏
页码:4932 / 4936
页数:5
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