Blind topology identification for power systems

Xiao, Li; Poor, H. Vincent; Scaglione, Anna

doi:10.1109/smartgridcomm.2013.6687939

Cited by 57 publications

(59 citation statements)

References 25 publications

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“…2. The optimization problem (14) is convex, and it can be solved by any offthe-shelf convex programming method. In the following, we examine the performance of our method for identifying the interactions on real datasets.…”

Section: Identification Of Higher-order Grid Interactionsmentioning

confidence: 99%

“…To find the grid topology, we first estimate the graph Volterra kernels in (11) with the voltage time-series {v(t)} T t=1 , by solving (14) and construct R (1) and R (2) [cf. (12)].…”

Section: Numerical Testsmentioning

confidence: 99%

“…Even though these methods improve with respect to the impedance-based methods, they do not account for the intrinsic nonlinear dependencies between nodal voltage measurements, yielding sub-optimal performance. Finally, leveraging the DC power flow model, a blind topology identification algorithm based on power injections was developed for transmission networks in [14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Learning connectivity and higher-order interactions in radial distribution grids

Yang

Coutiño

Wang

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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To perform any meaningful optimization task, distribution grid operators need to know the topology of their grids. Although power grid topology identification and verification has been recently studied, discovering instantaneous interplay among subsets of buses, also known as higher-order interactions in recent literature, has not yet been addressed. The system operator can benefit from having this knowledge when re-configuring the grid in real time, to minimize power losses, balance loads, alleviate faults, or for scheduled maintenance. Establishing a connection between the celebrated exact distribution flow equations and the so-called self-driven graph Volterra model, this paper puts forth a nonlinear topology identification algorithm, that is able to reveal both the edge connections as well as their higher-order interactions. Preliminary numerical tests using real data on a 47-bus distribution grid showcase the merits of the proposed scheme relative to existing alternatives.

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Section: Identification Of Higher-order Grid Interactionsmentioning

confidence: 99%

“…To find the grid topology, we first estimate the graph Volterra kernels in (11) with the voltage time-series {v(t)} T t=1 , by solving (14) and construct R (1) and R (2) [cf. (12)].…”

Section: Numerical Testsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Learning connectivity and higher-order interactions in radial distribution grids

Yang

Coutiño

Wang

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Graph learning has been widely used electric grids applications, such as state estimation [11,12] and topology identification [38,16]. Most of the literature focuses on topology identification or change detection, but there is not much recent work on joint topology and parameter recovery, with notable exceptions of [28,46,34]. Moreover, there is little exploration on the fundamental performance limits (estimation error and sample complexity) on topology and parameter identification of power networks, with the exception of [48] where a sparsity condition is provided for exact recovery of outage lines.…”

Section: Parameter Identification Of Power Systemsmentioning

confidence: 99%

Learning Graphs From Linear Measurements: Fundamental Trade-Offs and Applications

Werner

Low

2020

IEEE Trans. on Signal and Inf. Process. over Networks

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We consider a specific graph learning task: reconstructing a symmetric matrix that represents an underlying graph using linear measurements. We study fundamental trade-offs between the number of measurements (sample complexity), the complexity of the graph class, and the probability of error by first deriving a necessary condition (fundamental limit) on the number of measurements.Then, by considering a two-stage recovery scheme, we give a sufficient condition for recovery. Furthermore, assuming the measurements are Gaussian IID, we prove upper and lower bounds on the (worst-case) sample complexity. In the special cases of the uniform distribution on trees with n nodes and the Erdős-Rényi (n, p) class, the fundamental trade-offs are tight up to multiplicative factors. Applying the Kirchhoff's matrix tree theorem, our results are extended to the scenario when part of the topology information is known a priori. In addition, for practical applications, we design and implement a polynomial-time (in n) algorithm based on the two-stage recovery scheme.We apply the heuristic algorithm to learn admittance matrices in electric grids. Simulations for several canonical graph classes and IEEE power system test cases demonstrate the effectiveness of the proposed algorithm for accurate topology and parameter recovery.Keywords Graph signal processing · sample complexity · network parameter identification · information theory · sparse recovery * Li, Werner and Low are with the Computing

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“…Even though this assumption does not always hold and depends on the profile of the attacker, Li et al [16] showed that an attacker, with access to limited data, can learn the topology of the power system. Different models were proposed to identify critical components in the power grid [17].…”

Section: Related Workmentioning

confidence: 99%

A Game-Theoretical Model for Security Risk Management of Interdependent ICT and Electrical Infrastructures

Ismail

Leneutre

Bateman

et al. 2015

2015 IEEE 16th International Symposium on High Assurance Systems Engineering

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The communication infrastructure is a key element for management and control of the power system in the smart grid. The communication infrastructure, which can include equipment using off-the-shelf vulnerable operating systems, has the potential to increase the attack surface of the power system. The interdependency between the communication and the power system renders the management of the overall security risk a challenging task. In this paper, we address this issue by presenting a mathematical model for identifying and hardening the most critical communication equipment used in the power system. Using non-cooperative game theory, we model interactions between an attacker and a defender. We derive the minimum defense resources required and the optimal strategy of the defender that minimizes the risk on the power system. Finally, we evaluate the correctness and the efficiency of our model via a case study.

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Blind topology identification for power systems

Cited by 57 publications

References 25 publications

Learning connectivity and higher-order interactions in radial distribution grids

Learning connectivity and higher-order interactions in radial distribution grids

Learning Graphs From Linear Measurements: Fundamental Trade-Offs and Applications

A Game-Theoretical Model for Security Risk Management of Interdependent ICT and Electrical Infrastructures

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