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OA北大核心CSTPCD
Novel Distribution System Robust State Estimation Based on Adaptive Partitioning and SFVMD-LSTM Pseudo-Measurement Modeling
分布式资源大量接入使配电网运行机理愈加复杂,多类型不良数据、电网规模扩大等因素给新型配电系统的精准状态估计带来了新的技术挑战.提出一种基于自适应分区和时空变分模态分解-长短期记忆(spatiotemporal feature variational mode decomposition-long short term memory,SFVMD-LSTM)网络伪量测建模的新型配电系统抗差状态估计模型.在计及节点电气灵敏度的基础上,考虑不良数据分布特…查看全部>>
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…查看全部>>
何振武;姜飞;欧阳卫;刘利波;曾子豪;何桂雄
长沙理工大学电网防灾减灾全国重点实验室,长沙市 410114长沙理工大学电网防灾减灾全国重点实验室,长沙市 410114长沙理工大学电网防灾减灾全国重点实验室,长沙市 410114长沙理工大学电网防灾减灾全国重点实验室,长沙市 410114国网湖南综合能源服务有限公司,长沙市 410007中国电力科学研究院有限公司,北京市 100192
动力与电气工程
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
novel distribution systembad dataadaptive partitioningspatiotemporal feature variational mode decompositionstate estimation
《电力建设》 2024 (10)
78-89,12
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)
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