• HOME
  • About Journal
    • Historical evolution
    • Journal Honors
  • Editorial Board
    • Members of Committee
    • Director of the Committee
    • President and Editor in chief
  • Submission Guide
    • Instructions for Authors
    • Manuscript Processing Flow
    • Model Text
    • Procedures for Submission
  • Academic Influence
  • Open Access
  • Ethics&Policies
    • Publication Ethics Statement
    • Peer Review Process
    • Academic Misconduct Identification and Treatment
    • Advertising and Marketing
    • Correction and Retraction
    • Conflict of Interest
    • Authorship & Copyright
  • Contact Us
  • Chinese
Site search        
文章摘要
基于多周期性MILP模型的新型配电系统拓扑辨识方法
Topology identification method of novel distribution system based on multi-period MILP model
Received:July 13, 2022  Revised:July 16, 2022
DOI:10.19753/j.issn1001-1390.2023.02.017
中文关键词: 拓扑辨识  电流传感器  配电系统  混合整数线性规划  多周期性优化
英文关键词: topology identification, current sensor, distribution system, mixed integer linear programming, multi-period optimization
基金项目:中国南方电网有限公司科技项目(670000KK58200011)
Author NameAffiliationE-mail
XIAO Zhan-hui* Digital Grid Research Institute lxbwhu@126.com 
Zou Wen-jing Digital Grid Research Institute zld2017@126.com 
Tang Liang-yun Digital Grid Research Institute lxbctgu@163.com 
Hits: 1470
Download times: 382
中文摘要:
      在新兴低成本、非接触式的电流传感器的基础上,提出了基于多周期性混合整数线性优化的拓扑辨识方法。基于支路电流绝对值误差建立了拓扑辨识的混合整数非线性优化(MINLP)模型,采用线性化方法将MINLP模型转化为混合整数线性优化(MILP)模型,并通过多周期性测量数据建立多周期性优化模型,从而减小伪测量误差的影响。此外,证明了支路电流传感器优化配置条件,以确保拓扑辨识的准确性。在IEEE-33节点系统的仿真测试结果表明,所提出的拓扑辨识方法拓扑辨识精度高,随多周期性场景的增加,拓扑辨识精度逐渐增加,且受伪测量误差的影响比受支路电流测量误差更大。
英文摘要:
      Based on the emerging low-cost, non-contact current sensors, a topology identification method based on multi-period mixed integer linear optimization was proposed in this paper. Firstly, a mixed integer nonlinear optimization (MINLP) model for topology identification was established based on the absolute value error of branch current. Then, the MINLP model was transformed into a mixed integer linear optimization (MILP) model by linearization method, and a multi-period optimization model was established through multi-period measurement data, so as to eliminate the influence of pseudo measurement error. In addition, the optimal configuration condition of branch current sensors was proved to ensure the accuracy of topology identification. The simulation results of IEEE-33 node system show that the proposed method in this paper has the high accuracy of topology identification. With the increase of multi-period scenarios, the accuracy of topology identification gradually increases, and the influence of pseudo measurement error is greater than that of branch current measurement error.
View Full Text   View/Add Comment  Download reader
Close
  • Home
  • About Journal
    • Historical evolution
    • Journal Honors
  • Editorial Board
    • Members of Committee
    • Director of the Committee
    • President and Editor in chief
  • Submission Guide
    • Instructions for Authors
    • Manuscript Processing Flow
    • Model Text
    • Procedures for Submission
  • Academic Influence
  • Open Access
  • Ethics&Policies
    • Publication Ethics Statement
    • Peer Review Process
    • Academic Misconduct Identification and Treatment
    • Advertising and Marketing
    • Correction and Retraction
    • Conflict of Interest
    • Authorship & Copyright
  • Contact Us
  • 中文页面
Address: No.2000, Chuangxin Road, Songbei District, Harbin, China    Zip code: 150028
E-mail: dcyb@vip.163.com    Telephone: 0451-86611021
© 2012 Electrical Measurement & Instrumentation
黑ICP备11006624号-1
Support:Beijing Qinyun Technology Development Co., Ltd