Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Subhash Lakshminarayana"'
Publikováno v:
IET Smart Grid, Vol 5, Iss 3, Pp 203-218 (2022)
Abstract Advances in edge computing are powering the development and deployment of Internet of Things (IoT) systems to provide advanced services and resource efficiency. However, large‐scale IoT‐based load‐altering attacks (LAAs) can seriously
Externí odkaz:
https://doaj.org/article/323c20f876444b52adf5592bc7988b10
Publikováno v:
IEEE Open Access Journal of Power and Energy, Vol 9, Pp 109-120 (2022)
Recently there has been a surge of interest in adopting deep neural networks (DNNs) for solving the optimal power flow (OPF) problem in power systems. Computing optimal generation dispatch decisions using a trained DNN takes significantly less time w
Externí odkaz:
https://doaj.org/article/110be1cf24cf4eb88250008916d31cfe
Publikováno v:
IEEE Open Access Journal of Power and Energy, Vol 9, Pp 226-240 (2022)
The COVID-19 pandemic has impacted our society by forcing shutdowns and shifting the way people interacted worldwide. In relation to the impacts on the electric grid, it created a significant decrease in energy demands across the globe. Recent studie
Externí odkaz:
https://doaj.org/article/84ec2c6dd9d644ae8d11fd766735886e
Publikováno v:
Sensors, Vol 22, Iss 21, p 8544 (2022)
Future wireless networks will be required to provide more wireless services at higher data rates and with global coverage. However, existing homogeneous wireless networks, such as cellular and satellite networks, may not be able to meet such requirem
Externí odkaz:
https://doaj.org/article/2e1bc463021141499ba6bcfcc25db521
Publikováno v:
Applied Sciences, Vol 12, Iss 19, p 10093 (2022)
The safe and efficient function of critical national infrastructure (CNI) relies on the accurate demand forecast. Cyber-physical system-based demand forecasting systems (CDFS), typically found in CNI (such as energy, water, and transport), are highly
Externí odkaz:
https://doaj.org/article/1190a5118fce4da68647cee42c91db2c
Large-scale Load-Altering Attacks (LAAs) against Internet-of-Things (IoT) enabled high-wattage electrical appliances (e.g., wifi-enabled air-conditioners, electric vehicles, etc.) pose a serious threat to power systems' security and stability. In thi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b5212ffef3020217e698091708c9fd7e
http://arxiv.org/abs/2206.13129
http://arxiv.org/abs/2206.13129
Publikováno v:
DSN
We study moving-target defense (MTD) that actively perturbs transmission line reactances to thwart stealthy false data injection (FDI) attacks against state estimation in a power grid. Prior work on this topic has proposed MTD based on randomly selec
Publikováno v:
IEEE Transactions on Smart Grid. 12:635-646
We address the problem of constructing false data injection (FDI) attacks that can bypass the bad data detector (BDD) of a power grid. The attacker is assumed to have access to only power grid measurement data traces collected over a limited period o
Publikováno v:
IEEE Internet of Things Journal. :1-1
Publikováno v:
Sustainability; Volume 14; Issue 4; Pages: 2051
Power grid parameter estimation involves the estimation of unknown parameters, such as the inertia and damping coefficients, from the observed dynamics. In this work, we present physics-informed machine learning algorithms for the power system parame