Open Access

ARTICLE

Fault Distance Estimation Method for DC Distribution Networks Based on Sparse Measurement of High-Frequency Electrical Quantities

He Wang, Shiqiang Li^*, Yiqi Liu, Jing Bian

Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education (Northeast Electric Power University), Jilin, 132012, China

* Corresponding Author: Shiqiang Li. Email:

Energy Engineering 2025, 122(11), 4497-4521. https://doi.org/10.32604/ee.2025.065244

Received 07 March 2025; Accepted 26 May 2025; Issue published 27 October 2025

Abstract

With the evolution of DC distribution networks from traditional radial topologies to more complex multi-branch structures, the number of measurement points supporting synchronous communication remains relatively limited. This poses challenges for conventional fault distance estimation methods, which are often tailored to simple topologies and are thus difficult to apply to large-scale, multi-node DC networks. To address this, a fault distance estimation method based on sparse measurement of high-frequency electrical quantities is proposed in this paper. First, a preliminary fault line identification model based on compressed sensing is constructed to effectively narrow the fault search range and improve localization efficiency. Then, leveraging the high-frequency impedance characteristics and the voltage-current relationship of electrical quantities, a fault distance estimation approach based on high-frequency measurements from both ends of a line is designed. This enables accurate distance estimation even when the measurement devices are not directly placed at both ends of the faulted line, overcoming the dependence on specific sensor placement inherent in traditional methods. Finally, to further enhance accuracy, an optimization model based on minimizing the high-frequency voltage error at the fault point is introduced to reduce estimation error. Simulation results demonstrate that the proposed method achieves a fault distance estimation error of less than 1% under normal conditions, and maintains good performance even under adverse scenarios.

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Keywords

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