Topology identification for a class of complex dynamical networks using output variables

A problem of topology identification for complex dynamical networks is investigated in this paper. An adaptive observer is proposed to identify the topology of a complex dynamical networks based on the Lyapunov stability theory. Here the output of the network and the states of the observer are used to construct the updating law of the topology such that the communication resources from the network to its observer are saved. Some convergent criteria of the adaptive observer are derived in the form of linear inequality matrices. Several numerical examples are shown to demonstrate the effectiveness of the proposed observer.

Optimization-based topology identification of complex networks

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.

Data-Model Hybrid Driven Topology Identification Framework for Distribution Networks

Extensive penetration of distribution energy resources(DERs)brings increasing uncertainties to distribution networks.Accurate topology identification is a critical basis to guarantee robust distribution network operation.Many algorithms that estimate distribution network topology have already been employed.Unfortunately,most are based on data-driven alone method and are hard to deal with ever-changing distribution network physical structures.Under these backgrounds,this paper proposes a data-model hybrid driven topology identification scheme for distribution networks.First,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 identification.Then,the rough identification results in the previous step are used to make a model of distribution network topology.The model transforms the topology identification problem into a mixed integer programming problem to correct the rough topology further.Performance of the proposed method is verified in an IEEE 33-bus test system and modified 292-bus system.

Partial Topology Identification of Stochastic Multi-Weighted Complex Networks Based on Graph-Theoretic Method and Adaptive Synchronization

This article aims to identify the partial topological structures of delayed complex network.Based on the drive-response concept,a more universal model,which includes nonlinear couplings,stochastic perturbations and multi-weights,is considered into drive-response networks.Different from previous methods,we obtain identification criteria by combining graph-theoretic method and adaptive synchronization.After that,the partial topological structures of stochastic multi-weighted complex networks with or without time delays can be identified successfully.Moreover,response network can reach synchronization with drive network.Ultimately,the effectiveness of the proposed theoretical results is validated through numerical simulations.

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

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 identification.Second,a maximal information coefficient-based maximum spanning tree algorithm is developed to obtain the network topology by evaluating the dependence among the synthetic measurements.The 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.

Synchronization-Based Topology Identification of Uncertain Stochastic Delay Complex Networks

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.

Identification of Topological Surface State in PdTe2 Superconductor by Angle-Resolved Photoemission Spectroscopy

High-resolution angle-resolved photoemission measurements are carried out on transition metal dichalcogenide PdTe2 that is a superconductor with a Tc at 1.7K. Combined with theoretical calculations, we discover for the first time the existence of topologically nontrivial surface state with Dirac cone in PbTe2 superconductor. It is located at the Brillouin zone center and possesses helical spin texture. Distinct from the usual three-dimensional topological insulators where the Dirac cone of the surface state lies at the Fermi level, the Dirac point of the surface state in PdTe2 lies deeply below the Fermi level at - 1.75 eV binding energy and is well separated from the bulk states. The identification of topological surface state in PdTe2 superconductor deeply below the Fermi level provides a unique system to explore new phenomena and properties and opens a door for finding new topological materials in transition metal ehalcogenides.

Partial Correlation Analysis Based Identification of Distribution Network Topology

Accurately identifying distribution network topol-ogy,which tends to be a mesh configuration with increasing penetration rate of distributed energy resources(DERs),is critical for reliable operation of a smart distribution network.Multicollinearity among node voltages makes existing topology identification methods unstable and inaccurate.Considering partial correlation analysis can reveal the intrinsic correlation of two variables by eliminating the influence of other variables,this paper develops a novel data-driven method based on partial correlation analysis to identify distribution network topology(radial,mesh,or including DERs)using only historical voltage amplitude data.First,maximum spanning tree of network is generated through Prim algorithm.Then,the loops of network are identified by taking tree neighbors as controlling variables in partial correlation analysis.Finally,a new topology verification mechanism based on partial correlation analysis is developed to correct wrong connections caused by multicollinearity.Test results on IEEE 33-node system,IEEE 123-node system and practical distribution network demonstrate that our method outperforms common data-driven methods,and can robustly identify both radial and mesh distribution network with DERs.IndexTerms-Data-driven,linear correlation,partial correlation,smart meter,topology identification.

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

Accurate topological information is crucial in supporting the coordinated operational requirements of source-load-storage in low-voltage distribution networks.Comprehensive 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 distorted.This can seriously affect the performance of LVTI methods.Thus,this paper defines hidden errors and proposes an LVTI method based on layer-by-layer stepwise regression.In the first step,a multi-linear regression model is developed for consumer-branch connectivity identification based on the energy conservation principle.In the second step,a significance factor based on the t-test is proposed to modify the identification results by considering the hidden errors.In 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 results.Finally,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.