Compressive Sensing-Based Topology Identification for Smart Grids

Babakmehr, Mohammad; Simões, Marcelo Godoy; Wakin, Michael B.; Harirchi, Farnaz

doi:10.1109/tii.2016.2520396

Cited by 89 publications

(47 citation statements)

References 32 publications

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“…All lines are represented by the edge set E, E = {e 1 The topological analysis-based method can provide a quick and effective way to identify the network structure and find propagation paths of power flow transfer [34]. Some new approaches, such as the compressive sensing-based approach [35] and graphical learning-based approach [36,37] were proposed in recent years. Based on Dijkstra's algorithm [38], CAA is proposed in this study.…”

Section: Location Of Sensitive Regions With Caamentioning

confidence: 99%

Identification of Critical Transmission Lines in Complex Power Networks

Wang

Nechifor

et al. 2017

Energies

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Growing load demands, complex operating conditions, and the increased use of intermittent renewable energy pose great challenges to power systems. Serious consequences can occur when the system suffers various disturbances or attacks, especially those that might initiate cascading failures. Accurate and rapid identification of critical transmission lines is helpful in assessing the system vulnerability. This can realize rational planning and ensure reliable security pre-warning to avoid large-scale accidents. In this study, an integrated "betweenness" based identification method is introduced, considering the line's role in power transmission and the impact when it is removed from a power system. At the same time, the sensitive regions of each line are located by a cyclic addition algorithm (CAA), which can reduce the calculation time and improve the engineering value of the betweenness, especially in large-scale power systems. The simulation result verifies the effectiveness and the feasibility of the identification method.

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Section: Location Of Sensitive Regions With Caamentioning

confidence: 99%

Identification of Critical Transmission Lines in Complex Power Networks

Wang

Nechifor

et al. 2017

Energies

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“…Since the nodal-admittance matrix gives a full description of the structure of the networks' graph, we can represent the PNTI problem as that of determining the structure of the nodal-admittance matrix [23]. As has been discussed before [5], [22], and [30], power flow injection originates from the aggregated load requests of a large number of users and can be well approximated using Gaussian random variables; in addition, there is uncertainty caused by the utilization of renewable resources.…”

Section: B DC Power Flow Model and Its Graphmentioning

confidence: 99%

“…Similar to the CS-based approach that we have developed in [23], our key assumption is that the PN is a sparse interconnected system. This sparse structure helps us to reformulate the PNTI problem as a sparse recovery problem that can be solved using a small set of measurements in a fast and accurate way using SRP solvers.…”

Section: Smart Grid Sparse Topology Identificationmentioning

confidence: 99%

“…Recently, we have addressed some of these issues in [23], which employs a compressive sensing-based approach involving some modifications to greedy-based sparse recovery solvers. In this work we further investigate the optimization-based formulations of the sparse PNTI, and we aim to exploit additional structure within the sparse vectors in order to improve the performance of the optimization-based sparse recovery techniques for PNTI using a few measurements of the system parameters (that can also be used to solve the POIP).…”

Section: Introductionmentioning

confidence: 99%

“…The approaches utilized here and in [23] are completely different from the literature in the formulation. Our methods rely only on the measurements from system parameters and do not require any a priori information about the topology of the network.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Smart-Grid Topology Identification Using Sparse Recovery

Babakmehr

Simões

Wakin

et al. 2016

IEEE Trans. on Ind. Applicat.

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Smart grid (SG) technology reshapes the traditional power grid into a dynamical network with a layer of information that flows along the energy system. Recorded data from a variety of parameters in SGs can improve analysis of different supervisory problems, but an important issue is their cost and power efficiency in data analysis procedures. This paper develops an efficient solution for power network topology identification (PNTI) and monitoring activities in SG. The basic idea combines optimization-based sparse recovery techniques with a graph theory foundation. The power network (PN) is modeled as a large interconnected graph, which can be evaluated with the DC power-flow model. It has been shown that topology identification for such a system can mathematically be reformulated as a sparse recovery problem (SRP), and the corresponding SRP can efficiently be solved using SRP solvers. In this work we especially exploit the concentration of nonzero elements in the corresponding sparse vectors around the main diagonal in the nodal-admittance or structure matrix of the PN to improve the results. The network models have been generated with the MATPOWER toolbox, and Matlab-based simulation results have indicated the promising performance of the proposed method for real time TI in SGs.

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Search space reduction–based new approach for transmission branch outage identification using minimum PMU

Alam

Kundu

Thakur

et al. 2021

Int Trans Electr Energ Syst

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This study presents a search space reduction-based new approach for branch outage identification in the transmission network employing the minimum number of phasor measurement units (PMUs). Two innovative logical terms, namely normalized generator bus current variation (NGBCV) and branch power distribution factor (BPDF), are introduced. The search space is reduced by selecting a single generator bus named as critical generator bus (CGB) based on proposed NGBCV. Thereafter, a single branch named as critical transmission branch (CTB) connected to the CGB is chosen. The identification of branch outage is performed by comparing the calculated power flow through the CTB using BPDF, and PMU provided monitored power flow through CTB. Moreover, the measurement error is also incorporated in mathematical modeling in order to make the developed model practically viable.

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Compressive Sensing-Based Topology Identification for Smart Grids

Cited by 89 publications

References 32 publications

Identification of Critical Transmission Lines in Complex Power Networks

Identification of Critical Transmission Lines in Complex Power Networks

Smart-Grid Topology Identification Using Sparse Recovery

Search space reduction–based new approach for transmission branch outage identification using minimum PMU

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