Online Adversarial Stabilization of Unknown Networked Systems

Yu, Jihong; Ho, Dimitar; Wierman, Adam

doi:10.48550/arxiv.2203.02630

Cited by 2 publications

(2 citation statements)

References 53 publications

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“…As the current algorithm is highly centralized, future works may consider more decentralized approaches to topology-robust voltage control in order to enable faster real-time control. Ideas from works such as [35] can potentially be adapted. Further studies may also explore loosening the radial topology assumption to accommodate a wider range of distribution grids.…”

Section: Discussionmentioning

confidence: 99%

Robust online voltage control with an unknown grid topology

Yeh

Shi

et al. 2022

Proceedings of the Thirteenth ACM International Conference on Future Energy Systems

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Voltage control generally requires accurate information about the grid's topology in order to guarantee network stability. However, accurate topology identification is a challenging problem for existing methods, especially as the grid is subject to increasingly frequent reconfiguration due to the adoption of renewable energy. Further, running existing control mechanisms with incorrect network information may lead to unstable control. In this work, we combine a nested convex body chasing algorithm with a robust predictive controller to achieve provably finite-time convergence to safe voltage limits in the online setting where the network topology is initially unknown. Specifically, the online controller does not know the true network topology and line parameters, but instead must learn them over time by narrowing down the set of network topologies and line parameters that are consistent with its observations and adjusting reactive power generation accordingly to keep voltages within desired safety limits. We demonstrate the effectiveness of the approach using a case study, which shows that in practical settings the controller is indeed able to narrow the set of consistent topologies quickly enough to make control decisions that ensure stability. CCS CONCEPTS• Hardware → Smart grid; Power networks; • Theory of computation → Online learning algorithms; • Computer systems organization → Sensor networks; • Computing methodologies → Control methods.

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Section: Discussionmentioning

confidence: 99%

Robust online voltage control with an unknown grid topology

Yeh

Shi

et al. 2022

Proceedings of the Thirteenth ACM International Conference on Future Energy Systems

Self Cite

View full text Add to dashboard Cite

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“…Similarly to [25,43,44], we assume throughout this paper that for any i ∈ V, it holds that D ij ≤ 1 for all j ∈ N i , where N i is given in Eq. ( 1).…”

Section: Decentralized Lqr With Sparsity and Delay Constraintsmentioning

confidence: 99%

Regret Bounds for Learning Decentralized Linear Quadratic Regulator with Partially Nested Information Structure

Ye¹,

Ming²,

Gupta³

2022

Preprint

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We study the problem of learning decentralized linear quadratic regulator under a partially nested information constraint, when the system model is unknown a priori. We propose an online learning algorithm that adaptively designs a control policy as new data samples from a single system trajectory become available. Our algorithm design uses a disturbance-feedback representation of state-feedback controllers coupled with online convex optimization with memory and delayed feedback. We show that our online algorithm yields a controller that satisfies the desired information constraint and enjoys an expected regret that scales as √ T with the time horizon T .

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Online Adversarial Stabilization of Unknown Networked Systems

Cited by 2 publications

References 53 publications

Robust online voltage control with an unknown grid topology

Robust online voltage control with an unknown grid topology

Regret Bounds for Learning Decentralized Linear Quadratic Regulator with Partially Nested Information Structure

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