A Data-Driven State Estimation Based on Sample Migration for Low-Observable Distribution Networks

A Data-Driven State Estimation Based on Sample Migration for Low-Observable Distribution Networks

This paper proposes a data-driven state estimation based on sample migration for low-observable distribution networks, addressing the challenge of traditional state estimators being unsuitable for distribution networks with low observability. The state estimation model is trained using historical me...

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Bibliographic Details
Main Authors: Hao Jiao, Chen Wu, Lei Wei, Jinming Chen, Yang Xu, Manyun Huang
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Algorithms
Subjects:
low-observable distribution networks
artificial intelligence
state estimation
sample migration
Online Access:https://www.mdpi.com/1999-4893/18/3/121
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