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第40届中国控制会议

作者： Guanxue Yang Guanxiao Yang Xu Chen Zhaowei Wang School of Electrical and Information Engineering Jiangsu University

Identifying network topologies is a matter of great concern for us to better understand the evolutionary mechanisms and grasp the collective dynamics of complex networked systems. In this paper, a unified methodological framework for reconstructing nonlinear networks is proposed, termed Group Sparse Penalized Nonlinear Least Squares. Based on the theory of function approximation and feature selection, a nonlinear framework is firstly formulated with the equation of polynomial combination,where polynomial basis functions corresponding to the specific node should be taken as a group for either elimination or *** the topology of complex networked system would be identified by solving the problem of sparse group ***, the performance of our proposed method is evaluated on synthetic datasets from the classical Kuramoto oscillator *** influential cases of topology identification are also considered. All of the results demonstrate that the high-precision and robustness of our proposed method.

关键词： topology identification Nonlinear modelling Grouped sparsity Feature selection

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Decentralized topology identification of Complex Networks with Sensor Random Delays and Disturbances

Decentralized Topology Identification of Complex Networks wi...

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第30届中国控制与决策会议

作者： Chunxia Fan Ke Liu Qiliang Gao College of Automatica Nanjing University of Posts and Telecommunications

A topology identification scheme based on decentralized control architecture is proposed for a complex network with sensor random delays and *** node uses the information transmitted from its neighboring nodes to establish the adaptive observers which can correctly identify the links between it and its neighboring *** information is not needed to be exchange between the nodes and its neighboring ones in the adaptive observer so that the disadvantages induced by the communications of the observer are avoided including time delays and *** proposed scheme can be extended to the other complex networks such as time-delayed network and nominal ***,some numerical simulations are given to demonstrate the effectiveness of the proposed topology identification scheme.

关键词： topology identification decentralized control architecture complex networks sensor random delays

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A Reweighted l1-Minimization Algorithm for Joint topology and Line Parameter identification in Electric Grids

A Reweighted l1-Minimization Algorithm for Joint Topology an...

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第40届中国控制会议

作者： Zhiyue Wang Wei Zhou Junlin Li Zhiyong Chen Shifeng Chen Hai-Tao Zhang School of Artificial Intelligence and Automation and State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology School of Electrical Engineering and Computing The University of Newcastle Shenzhen Institute of Advanced Technology Chinese Academy of Sciences

topology and line parameter information is essential to the operation and control of electric grids. However, the gird topology may frequently change with the wide adoption of distributed energy resources, and the line parameters may be missing or inaccurate. The widespread deployment of advanced metering infrastructure enables the line parameters estimation and topology identification from a data-driven perspective. In this paper, we propose a reweighted 1-minimization algorithm to deal with the joint topology and line parameter estimation problem by leveraging the sparsity of topology structure. A modified alternating descent sub-optimal method is proposed to estimate the admittance matrix of the power system using nodal voltage and current phasor measurements. Extensive simulations performed on IEEE 14 and 57-bus benchmark systems are presented to substantiate the effectiveness of the proposed algorithm.

关键词： topology identification Line parameter estimation Electric grid Sparsity

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Consumer-branch Connectivity identification of Low Voltage Distribution Networks Based on Data-driven Approach

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Protection and Control of Modern Power Systems 2024年第4期9卷 69-82页

作者： Yongjun Zhang Yingqi Yi Wenyang Deng Siliang Liu Lai Zhou Kaidong Lin Yongzhi Cai the School of Electric Power Engineering and Guangdong Key Laboratory of Clean Energy Technology South China Uni-versity of TechnologyGuangzhou 510641China the School of Elec-tric Power Engineering South China University of TechnologyGuangzhou 510641China the Guangzhou Panyu vocational and technical college Guangzhou 511483China the Metrology Center Guangdong Power Company Grid Qingyuan 511500China

Accurate topological information is crucial in supporting the coordinated operational requirements of source-load-storage in low-voltage distribution *** coverage of smart meters provides a database for low-voltage topology identification(LVTI).However,because of electricity theft,power line commu-nication crosstalk,and interruption of communication,the measurement data may be *** can seriously affect the performance of LVTI ***,this paper defines hidden errors and proposes an LVTI method based on layer-by-layer stepwise *** the first step,a multi-linear regression model is developed for consumer-branch connectivity identification based on the energy conservation *** the second step,a significance factor based on the t-test is proposed to modify the identification results by considering the hidden *** the third step,the regression model and significance threshold parameters are iteratively updated layer by layer to improve the recall rate of the final identification ***,simulations of a test system with 63 users are carried out,and the practical application results show that the proposed method can guarantee over 90%precision under the influence of hidden errors.

关键词： Data driven hidden error linear re-gression low voltage distribution network topology identification

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