Zobrazeno 1 - 10
of 93
pro vyhledávání: '"Krishnamoorthy Gayathri"'
Publikováno v:
In Applied Soft Computing October 2024 164
Autor:
Vaidya, Rohan Yatindra a, I.N, Aparna a, Balakrishnan, Dhanasekar a, Nakata, Hidemi b, S, Karthik a, ⁎, 1, Krishnamoorthy, Gayathri a
Publikováno v:
In Heliyon 30 September 2024 10(18)
Reinforcement learning has been found useful in solving optimal power flow (OPF) problems in electric power distribution systems. However, the use of largely model-free reinforcement learning algorithms that completely ignore the physics-based modeli
Externí odkaz:
http://arxiv.org/abs/2109.01659
Analyzing the interactions between the transmission and distribution (T&D) system is becoming imperative with the increased penetrations of distributed energy resources (DERs) on electric power distribution networks. An assessment of the impacts of d
Externí odkaz:
http://arxiv.org/abs/1912.07198
Battery energy storage systems (BESS) are proving to be an effective solution in providing frequency regulation services to the bulk grid. However, there are several concerns for the transmission/distribution system operators (TSO/DSO) with the frequ
Externí odkaz:
http://arxiv.org/abs/1912.07204
With the increased penetrations of distributed energy resources (DERs), the need for integrated transmission and distribution system analysis (T&D) is imperative. This paper presents an integrated unbalanced T&D analysis framework using an iterativel
Externí odkaz:
http://arxiv.org/abs/1912.07191
With the growing penetrations of distributed energy resources (DERs), it is imperative to evaluate their impacts on transmission system operations. In this paper, an iteratively coupled transmission and distribution (T&D) co-simulation framework is e
Externí odkaz:
http://arxiv.org/abs/1912.07193
Publikováno v:
In Japanese Dental Science Review November 2018 54(4):169-173
Autor:
Hu, Lei, Shi, Jingyi, Ma, Zhongming, Krishnamoorthy, Gayathri, Sieling, Fred, Zhang, Guangping, Horrigan, Frank T., Cui, Jianmin
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2003 Sep 01. 100(18), 10488-10493.
Externí odkaz:
https://www.jstor.org/stable/3147743
Publikováno v:
In Big Data Application in Power Systems Edition: Second Edition. 2024:161-188