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Optimization-based topology identification of complex networks

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Chinese Physics B 2011年第11期20卷 127-133页

作者：唐圣学陈丽何怡刚 Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability Hebei University of Technology College of Electrical and Information Engineering Hunan University

In many cases, the topological structures of a complex network are unknown or uncertain, and it is of significance to identify the exact topological structure. An optimization-based method of identifying the topological structure of a complex network is proposed in this paper. identification of the exact network topological structure is converted into a minimal optimization problem by using the estimated network. Then, an improved quantum-behaved particle swarm optimization algorithm is used to solve the optimization problem. Compared with the previous adaptive synchronization- based method, the proposed method is simple and effective and is particularly valid to identify the topological structure of synchronization complex networks. In some cases where the states of a complex network are only partially observable, the exact topological structure of a network can also be identified by using the proposed method. Finally, numerical simulations are provided to show the effectiveness of the proposed method.

关键词： complex networks topology identification optimization particle swarm

Data-driven distribution network topology identification considering correlated generation power of distributed energy resource

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Frontiers in Energy 2022年第1期16卷 121-129页

作者： Jialiang CHEN Xiaoyuan XU Zheng YAN Han WANG Key Laboratory of Control of Power Transmission and Conversion(Ministry of Education) Shanghai Jiao Tong UniversityShanghai 200240China

This paper proposes a data-driven topology identification method for distribution systems with distributed energy resources(DERs).First,a neural network is trained to depict the relationship between nodal power injections and voltage magnitude measurements,and then it is used to generate synthetic measurements under independent nodal power injections,thus eliminating the influence of correlated nodal power injections on topology ***,a maximal information coefficient-based maximum spanning tree algorithm is developed to obtain the network topology by evaluating the dependence among the synthetic *** proposed method is tested on different distribution networks and the simulation results are compared with those of other methods to validate the effectiveness of the proposed method.

关键词： power distribution network data-driven topology identification distributed energy resource maximal information coefficient

Data-Model Hybrid Driven topology identification Framework for Distribution Networks

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CSEE Journal of Power and Energy Systems 2024年第4期10卷 1478-1490页

作者： Dongliang Xu Zaijun Wu Junjun Xu Qinran Hu School of Electrical Engineering Southeast UniversityNanjing 210096China College of automation Nanjing University of Posts and TelecommunicationsNanjing 210096China

Extensive penetration of distribution energy resources(DERs)brings increasing uncertainties to distribution *** topology identification is a critical basis to guarantee robust distribution network *** algorithms that estimate distribution network topology have already been ***,most are based on data-driven alone method and are hard to deal with ever-changing distribution network physical *** these backgrounds,this paper proposes a data-model hybrid driven topology identification scheme for distribution ***,a data-driven method based on a deep belief network(DBN)and random forest(RF)algorithm is used to realize the distribution network topology rough ***,the rough identification results in the previous step are used to make a model of distribution network *** model transforms the topology identification problem into a mixed integer programming problem to correct the rough topology *** of the proposed method is verified in an IEEE 33-bus test system and modified 292-bus system.

关键词： Data-model hybrid driven DBN-RF mixed-integer programming topology identification

Sinusoidal disturbance induced topology identification of Hindmarsh-Rose neural networks

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Science China(Information Sciences) 2016年第11期59卷 83-91页

作者： Junchan ZHAO M.A.AZIZ-ALAOUI Cyrille BERTELLE Nathalie CORSON The School of Mathematics and Statistics Hunan University of Commerce Key Laboratory of Hunan Province for Mobile Business Intelligence Hunan University of Commerce UNIHAVRE LMAH Normandie University 4. UNIHAVRE LITIS Normandie University

topology identification of complex networks is an important problem. Existing research shows that the synchronization of network nodes is an obstacle in the identification of network topology. identification of the structure of the network presents an interesting challenge during the synchronization of complex *** developed a new method using the sinusoidal disturbance to identify the topology when the complex network achieves synchronization. Compared with the disturbance of all the nodes, the disturbance of the key nodes alone can achieve a very good effect. Finally, numerical simulation data are provided to validate our hypothesis.

关键词： complex network topology identification sinusoidal disturbance persistently exciting condition Hindmarsh-Rose system

Synchronization-Based topology identification of Uncertain Stochastic Delay Complex Networks

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Wuhan University Journal of Natural Sciences 2013年第4期18卷 337-342页

作者： LIU Hongfang TU Lilan YU Le Hubei Province Key Laboratory of Systems Science in Metallurgical Process Wuhan University of Science and Technology

In this paper, topology identification of general weighted complex network with time-varying delay and stochastic perturbation,which is a zero-mean real scalar Wiener process, is investigated. Based on the adaptive-feedback control method, the stochastic Lyapunov stability theory and the ito formula, some synchronous criteria are established, which guarantee the asymptotical mean square synchronization of the drive network and the response network with stochastic disturbances, as well as identify the topological structure of the uncertain general drive complex network. Finally, numerical simulations are presented to verify the correctness and effectiveness of the proposed scheme.

关键词： topology identification time-varying coupling delay stochastic perturbation synchronization complex networks

A Two-Step Approach for Multi-topology identification and Parameter Estimation of Power Distribution Networks

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CSEE Journal of Power and Energy Systems 2024年第6期10卷 2446-2456页

作者： Li Ma Lei Wu Nian Liu Wei Pei Institute of Electrical Engineering and Key Laboratory of Long-Duration and Large-Scale Energy StorageChinese Academy of SciencesBeijing 100190China Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources North China Electric Power UniversityBeijing 102206China

To improve observability in power distribution networks(PDN),a two-step framework of multi-topology identification and parameter estimation is proposed in this ***,in the first step,a mixed-integer linear program(MILP)model-based split method is proposed to recognize mixed topologies in a multi-record dataset without a prerequisite on the number of topology categories and values of nodal voltage phase *** the second step,line parameters and nodal voltage phase angles are estimated using the Newton-Raphson method based on nodal measurements of real and reactive power injections,as well as voltage ***,a modified estimation model is proposed to apply to the multitopology ***,case studies on an IEEE 33-bus system illustrate the effectiveness of the proposed models in identifying the PDN’s topologies,as well as estimating line parameters and voltage phase angles.

关键词： MILP Newton-Raphson method parameter estimation power distribution system topology identification

topology identification and Inner Desynchronization of Hindmarsh-Rose Neural Networks

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Topology Identification and Inner Desynchronization of Hindm...

topology identification for multi-layer complex dynamical networks with intra-layer coupling time delay

第32届中国控制与决策会议

作者： Yan Li Hui Liu Zengyang Li School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Computer Science Central China Normal University

topology identification of biological neural networks has received great attention in recent *** most existing researches ignored the importance of linear independence *** network occurs inner synchronization,such a condition would inevitably break,which could lead to the failure of topology ***,synchronous firing between neurons is unavoidable in biological neural *** phenomenon makes it difficult to identify the topology of neural *** this paper,we proposed a new method to identify Hindmarsh-Rose Neural *** this method,the original neural network can implement inner desynchronization and the topology observers can successfully identify the unknown *** is worth mentioning that the our method can ensure that the linear independence condition naturally *** means that topology identification will not meet with ***,the effectiveness of the proposed method is successfully validated by numerical simulations.

关键词： Hindmarsh-Rose Neural Network topology identification Inner Desynchronization

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Topology identification for multi-layer complex dynamical ne...

topology identification of Multiple Output Coupled Neural Networks with and without Parameter Uncertainties

第40届中国控制会议

作者： Zhiheng Huang Xinwei Wang Guo-Ping Jiang College of Automation ＆ Artificial Intelligence Nanjing University of Posts and Telecommunications

This paper investigates the topology identification of multi-layer complex dynamical network with intra-layer coupling time *** adaptive topology identification method with the same number of nodes in each layer is *** original network is regarded as the drive *** topology identification of the drive network,the response network with adaptive controller is constructed,the error dynamical network is also *** Lyapunov stability theory and LaSalle’s invariant set principle are used to prove the asymptotic stability of the error dynamical *** on the adaptive controller,our approach can identify the topological structures of the complex dynamical ***,the numerical simulation is used to verify the effectiveness of the approach.

关键词： topology identification Multi-layer complex dynamical network Adaptive controller Intra-layer coupling time delay

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Topology Identification of Multiple Output Coupled Neural Ne...