A Distributed Interval State Estimation Framework of Distribution Networks Based on Multi-source Measurements

被引：0

作者：

Xu J. ^{[1
]}

Wu Z. ^{[2
]}

Zhang T. ^{[1
]}

Mao M. ^{[3
]}

Hu Q. ^{[2
]}

机构：

[1] College of Automation, College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Jiangsu Province, Nanjing

[2] School of Electrical Engineering, Southeast University, Jiangsu Province, Nanjing

[3] State Grid Shanghai Pudong Electric Power Supply Company, Pudong New Area, Shanghai

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2022年 / 42卷 / 24期

基金：

中国国家自然科学基金;

关键词：

distributed generation; distributed optimization; distribution network; multi-source data fusion; state estimation; uncertainty;

D O I：

10.13334/j.0258-8013.pcsee.220395

中图分类号：

学科分类号：

摘要：

Due to stochastic power injections of the renewable energy, the processing of state estimation in the large-scale distribution network needs to consider more uncertainties and complexities, and the compatibility of multiple types of measurements would also affect the state estimation results. The equivalent electrical distance was firstly defined and the unbalanced distribution network was divided into several subareas based on the community discovery algorithm. Then, the interval number of local multi-source measurements consisting of pseudo-measurements and real-time measurements were obtained. A local interval state estimation model for the local distribution network considering the bi-level uncertainty of measurements and line parameters was established, by using interval measurement transformation technology to uniformly convert multi-source interval measurements into system injection current data. Finally, a modified interval optimization method was used to effectively solve the local interval linear state estimation model, and the interaction of boundary state between adjacent sub-areas was completed to output the global interval state results of the distribution network. Case simulations and comparisons have illustrated that the proposed method possesses better performance in estimation accuracy and computational efficiency than these traditional ones, and it can track the influence of the multiple uncertain variables on the state estimation results. © 2022 Chinese Society for Electrical Engineering. All rights reserved.

引用

页码：8888 / 8899

页数：11

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