Research and Engineering Practice on Reactive Power Control Based on Deep Reinforcement Learning in Local Power Grid-Reference-Cited by-同舟云学术

Research and Engineering Practice on Reactive Power Control Based on Deep Reinforcement Learning in Local Power Grid

Published:2022 Issue: Volume: Page:487-505
ISSN:1876-1100
Container-title:Lecture Notes in Electrical Engineering
language:
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Author:

Jiang Minrui,Xu Dawei,Chen Tianhua,Li Huijun,Xu Xiaochun,Wang Shuai

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-7156-2_34

Reference18 articles.

1. Yongjun Z, Qinhao L, Xuan L (2017) Reactive power and voltage control in UHVAC power grid. Autom Electric Power Syst 41(5):159–167. https://doi.org/10.7500/AEPS20160813003

2. Xuran WANG, Qinglai GUO, Hongbin SUN et al (2015) Secondary voltage control considering rapid dynamic reactive power compensation. Autom Electric Power Syst 39(2):53–60. https://doi.org/10.7500/AEPS20131107004

3. Jinquan Z, Zhenwei Z, Jianguo Y et al (2019) Heterogeneous decomposition based distributed reactive power optimization method for global transmission and distribution network. Autom Electric Power Syst 43(3):108–115. https://doi.org/10.7500/AEPS20180111009

4. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539

5. Zhe H (2016) Towards the age of artificial intelligence——also on the direction of american artificial intelligence strategy and its reference to Chinese artificial intelligence strategy. E-Government 12:2–10

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