Work

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

Year: 2025

Type: article

Abstract: 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 measurement data from distribution networks with high observability. Measurements updated for low-observable distribution networks are supplemented by transferring samples from high-observable distribution networks using sample migration techniques, resulting in a state estimation model suitable for low-observable distribution networks. Test results demonstrate that the proposed algorithm outperforms traditional algorithms in both estimation accuracy and robustness aspects, such as the Weighted Least Squares (WLS) and Weighted Least Absolute Value (WLAV) methods. Furthermore, sample migration enhances the generalization ability of the state estimation model.

Source: Algorithms

DOI: 10.3390/a18030121

Authors: Hao Jiao, Chen Wu, Lei Wei, Jinming Chen, Yang Xu, Manyun Huang

Institutions: State Grid Jiangsu Electric Power (China), Hohai University

Language: English

Topic: Power System Optimization and Stability

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,930