smart power grids
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Author(s):  
Sina Sontowski ◽  
Nigel Lawrence ◽  
Deepjyoti Deka ◽  
Maanak Gupta

2021 ◽  
Vol 75 ◽  
pp. 103335
Author(s):  
Yubin Lin ◽  
Chenbing Cheng ◽  
Fen Xiao ◽  
Khalid Alsubhi ◽  
Hani Moaiteq Abdullah Aljahdali

2021 ◽  
pp. 122-147
Author(s):  
Mark Maslin

‘Solutions’ outlines the three types of solutions to climate change. The first is adaptation, which is providing protection for the population from the impacts of climate change. Both physical and social adaptations are required to protect people’s lives and livelihoods. The second solution is mitigation, which in its simplest terms is reducing our carbon footprint and thus reversing the trend of ever-increasing GHG emissions. This type of solution includes switching to renewable energy and electric vehicles, fossil-fuel subsidy reforms, smart power grids, sustainable agriculture, reforestation and rewilding. The third solution is geoengineering, which involves large-scale extraction of carbon dioxide from the atmosphere or modification of the global climate.


2021 ◽  
Author(s):  
Iason-Iraklis Avramidis ◽  
Himanshu Nagpal ◽  
Mahdi Mehrtash ◽  
Florin Capitanescu

2021 ◽  
Vol Special (June) ◽  
pp. 10-12
Author(s):  
Ayushi Ghill ◽  
Anshita Dharmawat ◽  
Yogesh Shirmali

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3551
Author(s):  
Gu Xiong ◽  
Krzysztof Przystupa ◽  
Yao Teng ◽  
Wang Xue ◽  
Wang Huan ◽  
...  

With the development of smart power grids, electronic transformers have been widely used to monitor the online status of power grids. However, electronic transformers have the drawback of poor long-term stability, leading to a requirement for frequent measurement. Aiming to monitor the online status frequently and conveniently, we proposed an attention mechanism-optimized Seq2Seq network to predict the error state of transformers, which combines an attention mechanism, Seq2Seq network, and bidirectional long short-term memory networks to mine the sequential information from online monitoring data of electronic transformers. We implemented the proposed method on the monitoring data of electronic transformers in a certain electric field. Experiments showed that our proposed attention mechanism-optimized Seq2Seq network has high accuracy in the aspect of error prediction.


Author(s):  
Davide Lauria ◽  
Fabio Mottola ◽  
Cosimo Pisani ◽  
Francesco Del Pizzo ◽  
Enrico Maria Carlini ◽  
...  

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