| Issue |
EPJ Web Conf.
Volume 372, 2026
Advanced Power Systems (APS 2026)
|
|
|---|---|---|
| Article Number | 01009 | |
| Number of page(s) | 7 | |
| Section | Intelligent, Digital and Resilient Power Systems | |
| DOI | https://doi.org/10.1051/epjconf/202637201009 | |
| Published online | 11 June 2026 | |
https://doi.org/10.1051/epjconf/202637201009
Voltage monitoring of low-voltage feeders with sparse sensor deployment
1 Óbuda University, Doctoral School of Applied Informatics and Applied Mathematics, Becsi u. 96/b 1034 Budapest, Hungary
2 Óbuda University, KVK Department of Power Systems, Becsi u. 96/b 1034 Budapest, Hungary
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Published online: 11 June 2026
Abstract
Low-voltage (LV) distribution networks increasingly face voltage regulation challenges due to high photovoltaic (PV) penetration and sparse measurement infrastructure. This paper presents a simple, graph-based distribution system state estimation method tailored for radial LV feeders with high resistance- to-reactance ratios. Exploiting the radial topology, the feeder is partitioned into measurement-bounded sections, and section-level net power imbalances are inferred from sparse boundary power-flow measurements. Internal nodal injections are estimated using a uniform allocation rule, and voltage magnitudes are reconstructed through an iterative power flow calculation assuming constant-power loads. The method is validated on a real LV overhead-line feeder in Hungary under a summer daytime scenario with high PV production. Using a full power flow solution as ground truth and assuming ideal sensors, the proposed approach achieves a voltage magnitude root mean square error of approximately 0.46% of nominal voltage. A sensor placement study further shows that branch-level measurements provide the largest improvement in estimation accuracy, with diminishing returns from additional end-of-line sensing.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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