Zobrazeno 1 - 10
of 127
pro vyhledávání: '"P. Pinceti"'
The electrical power grid is a critical infrastructure, with disruptions in transmission having severe repercussions on daily activities, across multiple sectors. To identify, prevent, and mitigate such events, power grids are being refurbished as 's
Externí odkaz:
http://arxiv.org/abs/2209.03514
The availability of large datasets is crucial for the development of new power system applications and tools; unfortunately, very few are publicly and freely available. We designed an end-to-end generative framework for the creation of synthetic bus-
Externí odkaz:
http://arxiv.org/abs/2107.03547
A framework for the generation of synthetic time-series transmission-level load data is presented. Conditional generative adversarial networks are used to learn the patterns of a real dataset of hourly-sampled week-long load profiles and generate uni
Externí odkaz:
http://arxiv.org/abs/2107.03545
Publikováno v:
IET Smart Grid, Vol 6, Iss 5, Pp 492-502 (2023)
Abstract The availability of large datasets is crucial for the development of new power system applications and tools; unfortunately, very few are publicly and freely available. The authors designed an end‐to‐end generative framework for the crea
Externí odkaz:
https://doaj.org/article/1557d5a1829c42009b52b5a9d3d7d432
Autor:
Chu, Zhigang, Pinceti, Andrea, Kaviani, Ramin, Khodadadeh, Roozbeh, Li, Xingpeng, Zhang, Jiazi, Saikumar, Karthik, Sahraei-Ardakani, Mostafa, Mosier, Christopher, Podmore, Robin, Hedman, Kory, Kosut, Oliver, Sankar, Lalitha
In this paper, we investigate the feasibility and physical consequences of cyber attacks against energy management systems (EMS). Within this framework, we have designed a complete simulation platform to emulate realistic EMS operations: it includes
Externí odkaz:
http://arxiv.org/abs/2104.13908
A nearest neighbor-based detection scheme against load redistribution attacks is presented. The detector is designed to scale from small to very large systems while guaranteeing consistent detection performance. Extensive testing is performed on a re
Externí odkaz:
http://arxiv.org/abs/1912.09453
Autor:
Chu, Zhigang, Pinceti, Andrea, Biswas, Reetam Sen, Kosut, Oliver, Pal, Anamitra, Sankar, Lalitha
Intelligently designed false data injection (FDI) attacks have been shown to be able to bypass the $\chi^2$-test based bad data detector (BDD), resulting in physical consequences (such as line overloads) in the power system. In this paper, it is show
Externí odkaz:
http://arxiv.org/abs/1905.02271
Publikováno v:
Journal of Modern Power Systems and Clean Energy, Vol 11, Iss 1, Pp 234-242 (2023)
In this study, a machine learning based method is proposed for creating synthetic eventful phasor measurement unit (PMU) data under time-varying load conditions. The proposed method leverages generative adversarial networks to create quasi-steady sta
Externí odkaz:
https://doaj.org/article/ff2f2acb688b4b6fb5fc6dafa027fa3b
Publikováno v:
Journal of Modern Power Systems and Clean Energy, Vol 10, Iss 2, Pp 361-370 (2022)
A nearest-neighbor-based detector against load redistribution attacks is presented. The detector is designed to scale from small-scale to very large-scale systems while guaranteeing consistent detection performance. Extensive testing is performed on
Externí odkaz:
https://doaj.org/article/e1439adebe554be9b944c7dc20c67570
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