Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Huaixun Zhang"'
Publikováno v:
IET Generation, Transmission & Distribution, Vol 18, Iss 16, Pp 2587-2603 (2024)
Abstract The high uncertainty of wind power output greatly affects the rapid reactive power optimization of power systems. This paper proposes a neural network‐based comprehensive reactive power optimization method for large‐scale wind power grid
Externí odkaz:
https://doaj.org/article/2be24d95275b4de98fa76a919ed488d0
Publikováno v:
Energy Reports, Vol 8, Iss , Pp 1568-1577 (2022)
To precisely forecast the operation status of transmission line during an ice storm and achieve early warning, a method based on adaptive relevance vector machine (ARVM) is proposed for fault probability prediction of transmission line icing. Accordi
Externí odkaz:
https://doaj.org/article/40802586b7414d9eb1be68f4432e9382
Publikováno v:
Energy Reports, Vol 8, Iss , Pp 1622-1638 (2022)
The application of energy routers will play an important role in the future AC/DC allocation networks. Based on the characteristics of energy routers, this article analyzes the application scenarios of several distribution network topologies and esta
Externí odkaz:
https://doaj.org/article/d4cf12c8dfd24a3aae775f3e40b89556
Publikováno v:
Sustainability; Volume 14; Issue 20; Pages: 13057
Large-scale power outage events bring serious economic losses to national and social development and cause bad impacts, and the reasonable formulation of a unit black-start strategy is the basis of power outage recovery. Firstly, the Dijkstra shortes
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::56a2712b8d12b99076a0cc1fe318ebe1
Publikováno v:
Sustainability, Vol 13, Iss 10526, p 10526 (2021)
Sustainability
Volume 13
Issue 19
Sustainability
Volume 13
Issue 19
The effective prediction of bus load can provide an important basis for power system dispatching and planning and energy consumption to promote environmental sustainable development. A bus load forecasting method based on variational modal decomposit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::52163e50f1965f7e68c5a66d1719c604
https://www.mdpi.com/2071-1050/13/19/10526
https://www.mdpi.com/2071-1050/13/19/10526
Publikováno v:
2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS).
Power data resources have complex problems such as heterogeneous, multi-source dispersion, high dimensionality, and diverse forms. This paper proposes one heterogeneous data feature extraction framework based on long-short term memory graph convoluti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d5d06caa4d5fa067e08468e5767f8d71
https://doi.org/10.1109/icaiis49377.2020.9194865
https://doi.org/10.1109/icaiis49377.2020.9194865
Publikováno v:
Journal of Physics: Conference Series. 1575:012130
In the field of deep learning, for problems and tasks that are sensitive to time series, such as natural language processing or speech recognition, the recurrent neural network is usually more suitable. Long short-term memory (LSTM) is a representati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b0ae6a292e7e320898f1d28d63b0ac18
https://doi.org/10.1088/1742-6596/1575/1/012130
https://doi.org/10.1088/1742-6596/1575/1/012130