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pro vyhledávání: '"Osipov, Denis A."'
Balanced data is required for deep neural networks (DNNs) when learning to perform power system stability assessment. However, power system measurement data contains relatively few events from where power system dynamics can be learnt. To mitigate th
Externí odkaz:
http://arxiv.org/abs/2406.09235
Publikováno v:
In Electric Power Systems Research October 2024 235
Publikováno v:
In Intermetallics January 2024 164
Akademický článek
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Autor:
Osipov, Denis, Sun, Kai
The letter proposes an adaptive model reduction approach based on tensor decomposition to speed up time-domain power system simulation. Taylor series expansion of a power system dynamic model is calculated around multiple equilibria corresponding to
Externí odkaz:
http://arxiv.org/abs/1904.00433
Publikováno v:
In International Journal of Electrical Power and Energy Systems May 2023 147
Autor:
Ditenberg, Ivan A., Osipov, Denis A., Smirnov, Ivan V., Grinyaev, Konstantin V., Esikov, Maksim A.
Publikováno v:
In Advanced Powder Technology January 2023 34(1)
Publikováno v:
In Electric Power Systems Research October 2022 211
Autor:
Osipov, Denis, Sun, Kai
The paper proposes a new adaptive approach to power system model reduction for fast and accurate time-domain simulation. This new approach is a compromise between linear model reduction for faster simulation and nonlinear model reduction for better a
Externí odkaz:
http://arxiv.org/abs/1711.03583
Autor:
Gorchinskiy, Sergey, Osipov, Denis
Publikováno v:
Functional Analysis and Its Applications, Vol. 50, No. 4, p. 268-280, 2016
We prove that the higher-dimensional Contou-Carr\`ere symbol is invariant under continuous automorphisms of algebras of iterated Laurent series over a ring. Applying this property, we obtain a new explicit formula for the higher-dimensional Contou-Ca
Externí odkaz:
http://arxiv.org/abs/1604.08049