Nassaj, A., Salehi Dobakhshari, A., Terzija, V. et al. (1 more author) (Accepted: 2025) A Hybrid Gamma-Model for Distribution Feeders Linear Parameter Estimation Using Unsynchronized Terminal Measurements.docx. IEEE Transactions on Power Systems. ISSN 0885-8950 (In Press)
Abstract
Accurate estimation of parameters for Distribution Network Feeders (DNFs) is crucial yet quite challenging, especially with limited synchronized measurements. This letter introduces a novel Hybrid Γ-Model (HGM) that leverages the circuit properties of DNFs to establish a linear relationship between unknown feeder parameters and unsynchronized terminal measurements. By combining two symmetrical Γ-models, the HGM effectively mitigates the inaccuracies and biases of simplified models. This model balances the accuracy of the equivalent П-model with the linearity of the short-line and Γ-models. An effective parameter estimation method is developed based on HGM, operating without requiring synchronized data. This method is applicable to both overhead lines and underground cables, and is particularly useful in the latter case, where shunt susceptance is more significant. By avoiding iterative solutions, the proposed method ensures convergence and eliminates the risk of multiple outcomes.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author produced version of an article accepted for publication in IEEE Transactions on Power Systems, made available under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Hybrid Γ-model, parameter estimation, unbalanced distribution feeders, unsynchronized measurements |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 May 2025 12:25 |
Last Modified: | 23 May 2025 13:42 |
Status: | In Press |
Publisher: | IEEE |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:226952 |