电力建设2024，Vol.45Issue(10)：78-89,12.DOI:10.12204/j.issn.1000-7229.2024.10.008
基于自适应分区和SFVMD-LSTM伪量测建模的新型配电系统抗差状态估计
Novel Distribution System Robust State Estimation Based on Adaptive Partitioning and SFVMD-LSTM Pseudo-Measurement Modeling
摘要
Abstract
The large number of accesses of distributed resources leads to more and more complex distribution network operation mechanism,as well as multiple types of undesirable data,the expansion of the grid scale and other factors bring new technical challenges to the accurate state estimation of the new distribution system.This paper proposes a new distribution system robust state estimation model based on adaptive partitioning and Spatiotemporal feature variational mode decomposition-Long Short-term Memory(SFVMD-LSTM)pseudo-measurement modeling.On the basis of taking into account the electrical sensitivity of the nodes,considering the distribution characteristics of the poor data,overcoming the shortcomings of the traditional Girvan and Newman(GN)algorithm in adapting to the changes in the quality of the measurement data by improving GN partitioning method,and utilizing the multi-source load data of the nodes in the subregion,the pseudo-measurement modeling method based on the SFVMD-LSTM is proposed,which improves the weighted least square(WLS)estimation.The pseudo-measurement modeling method based on SFVMD-LSTM is proposed to improve the measurement redundancy of WLS estimation,and to solve the problem of low accuracy and insufficient tolerance of traditional state estimation.The estimation accuracy and efficiency of the proposed method are higher than those of the traditional WLS and the Fast decoupling state estimation through example simulation and result comparison analysis.关键词
新型配电系统/不良数据/自适应分区/时空变分模态分解/状态估计Key words
novel distribution system/bad data/adaptive partitioning/spatiotemporal feature variational mode decomposition/state estimation分类
信息技术与安全科学引用本文复制引用
何振武,姜飞,欧阳卫,刘利波,曾子豪,何桂雄..基于自适应分区和SFVMD-LSTM伪量测建模的新型配电系统抗差状态估计[J].电力建设,2024,45(10):78-89,12.基金项目
This work is supported by the National Natural Science Foundation of China(No.52377166). 国家自然科学基金项目(52377166) （No.52377166）
