User–Feeder Topology Identification in Low-Voltage Residential Power Networks: A Clustering Fusion Approach

Xihao Guo, Chenghao Xu, Zixiang Ming, Bo Meng, Shan Yang, Linna Xu and Yongli Zhu ()
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Xihao Guo: School of System Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
Chenghao Xu: School of System Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
Zixiang Ming: School of System Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
Bo Meng: School of System Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
Shan Yang: School of System Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
Linna Xu: School of System Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
Yongli Zhu: School of System Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China

Energies, 2025, vol. 18, issue 18, 1-26

Abstract: This paper proposes a data-driven framework for user–feeder topology identification in low-voltage residential power networks using ambient (current and voltage) measurements from smart meters. The framework first prepossesses the raw dataset via wavelet-based denoising, principal component analysis-based dimensionality reduction, and deep learning-based temporal feature extraction. In addition, a deep learning-based anomaly detection approach is also applied. Seven clustering algorithms are adopted for user–feeder relationship identification, and then the results are fused via a result-fusion strategy to enhance the identification accuracy further. Experiments on three real-world residential power networks demonstrate that the proposed approach significantly outperforms the results obtained by a single clustering method and the results obtained by simple voting-based fusion. The proposed approach achieves up to 88% identification accuracy in the considered case studies. Ablation studies are also conducted to validate the importance of each module in the proposed framework.

Keywords: user–feeder topology; residential power network; anomaly detection; clustering; result fusion (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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