A Levenberg–Marquardt algorithm‐based line parameters identification method for distribution network considering multisource measurement

Author:

Xu Dongliang1ORCID,Song Xuewen1,Wu Zaijun1,Xu Junjun12,Hu Qinran1ORCID

Affiliation:

1. School of Electrical Engineering Southeast University Nanjing China

2. School of Automation and Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing China

Abstract

AbstractThe uncertainty brought by the integration of distributed generations in distribution networks poses a higher demand for situation awareness in the distribution network. Accurate identification of distribution network line parameters is of great significance for the operation and control of the distribution network. This paper proposes a method for identifying distribution network line parameters considering multisource measurement. Firstly, the initial values of conductivity and susceptance are obtained through linear regression and converted into resistance and reactance, respectively. Then, based on the series parallel connection of the network end branches, a non‐linear function about resistance reactance is derived. By combining the measurement data of micro phasor measurement unit and advanced metering infrastructure at multiple times, the non‐linear measurement equation of the line is established, and the Levenberg–Marquardt algorithm is used to solve the non‐linear function, thus achieving the identification of distribution line parameters. The case study demonstrates the accuracy and effectiveness of the proposed method.

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Reference21 articles.

1. Topology identification in distribution networks based on alternating optimisation

2. Distribution network state estimation based on attention‐enhanced recurrent neural network pseudo‐measurement modeling;Wang Y.;Prot. Control Mod. Power Syst.,2023

3. Power generation maximization of distributed photovoltaic systems using dynamic topology reconfiguration;Fang X.;Prot. Control Mod. Power Syst.,2022

4. Optimal distributed generation planning in distribution networks: A comparison of transmission network models with FACTS;Sadiq A.A.;Eng. Sci. Technol. Int. J.,2019

5. A dynamic‐model‐based fault diagnosis method for a wind turbine planetary gearbox using a deep learning network;Li D.;Prot. Control Mod. Power Syst.,2022

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