Abstract
Low-voltage distribution networks often suffer from incomplete or outdated network records, making it challenging to obtain the topology and line parameters under actual operating conditions. To address this issue, a joint identification method is proposed based on the propagation of load transient characteristics. First, the principle of load characteristic propagation is elaborated, and the concept of coupling impedance is introduced. Second, a set of linear regression equations is established based on the changes in current and voltage of the terminal measurements before and after load switching, and then these equations are solved using the least squares method to form the coupling impedance matrix. Finally, a node recursive algorithm is applied to iteratively derive the network topology and calculate line parameters in a bottom-up manner. Case studies show that by effectively extracting the information contained in the transient measurement dataset, the proposed method can correctly identify the network topology and accurately estimate line parameters even under three-phase imbalanced conditions, providing high practical value for engineering applications.
Recommended Citation
Y. Zhang et al., "Topology and Parameter Joint Identification in Imbalanced Low-Voltage Distribution Networks based on Load Characteristic Propagation," IEEE Transactions on Smart Grid, Institute of Electrical and Electronics Engineers, Jan 2025.
The definitive version is available at https://doi.org/10.1109/TSG.2025.3632927
Department(s)
Electrical and Computer Engineering
Publication Status
Early Access
Keywords and Phrases
hidden nodes; line parameters calculation; load transient characteristic; Low-voltage distribution network; three-phase imbalance; topology identification
International Standard Serial Number (ISSN)
1949-3061; 1949-3053
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2025
