Mitigating Cascading Failures in Power Grids via Markov Decision-Based Load-Shedding With DC Power Flow Model

Das, Pankaz; Shuvro, Rezoan A.; Povinelli, Kassie; Sorrentino, Francesco; Hayat, Majeed M.

doi:10.1109/jsyst.2022.3175359

Cited by 6 publications

(1 citation statement)

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“…This model runs iteratively to find failed lines based on a probabilistic model with respect to overloading conditions. The static models with DC power flows are adopted in many CF analyses (Soltan et al, 2017) (Yan et al, 2015) and mitigation strategies (Das et al, 2022). In dynamic models, the dynamic behavior of the grid is captured throughout the CF process.…”

Section: Cascading Failure In Power Gridmentioning

confidence: 99%

Cyber-physical cascading failure and resilience of power grid: A comprehensive review

Islam

Lin²,

Vokkarane³

et al. 2023

Front. Energy Res.

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Smart grid technologies are based on the integration of the cyber network and the power grid into a cyber-physical power system (CPPS). The increasing cyber-physical interdependencies bring about tremendous opportunities for the modeling, monitoring, control, and protection of power grids, but also create new types of vulnerabilities and failure mechanisms threatening the reliability and resiliency of system operation. A major concern regarding the interdependent networks is the cascading failure (CF), where a small initial disturbance/failure in the network results in a seemingly unexpected large-scale failure. Although there has been a significant volume of recent work in the CF research of CPPS, a comprehensive review remains unavailable. This article aims to fill the gap by providing a systematic literature survey regarding the modeling, analysis, and mitigation of CF in CPPS. The open research questions for further research are also discussed. This article allows researchers to easily understand the state of the art of CF research in CPPS and fosters future work required towards full resolutions to the remaining questions and challenges.

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Section: Cascading Failure In Power Gridmentioning

confidence: 99%