Dynamic Equivalent Model of Active Distribution Network Based on Improved Reinforcement Learning Algorithm-Reference-Cited by-同舟云学术

Dynamic Equivalent Model of Active Distribution Network Based on Improved Reinforcement Learning Algorithm

Published:2023 Issue: Volume: Page:587-597
ISSN:1876-1100
Container-title:Lecture Notes in Electrical Engineering
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Author:

Wei Qianlong,Kang Jiaxi,Guo Xiaolong,Wang Tian,Tang Wenhu,Qian Tong,Zheng Jiehui

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-1439-5_54

Reference19 articles.

1. Mitrentsis, G., Lens, H.: Data-driven dynamic models of active distribution networks using unsupervised learning techniques on field measurements. IEEE Trans. Smart Grid 12(4), 2952–2965 (2021)

2. Annakkage, U.D., Nair, N.K.C., Liang, Y., et al.: Dynamic system equivalents: a survey of available techniques. IEEE Trans. Power Delivery 27(1), 411–420 (2012)

3. Tang, Y., Zhang, H., Hou, J., Zhang, H.: A synthesis load model with distribution network. Power Syst. Technol. 31(5), 34–38 (2007)

4. Kim, J.K., et al.: Fast and reliable estimation of composite load model parameters using analytical similarity of parameter sensitivity. IEEE Trans. Power Syst. 31(1), 663–671 (2016)

5. Chen, Q., Tang, Y., Ju, P.: Parameter identification of the synthesis load model considering distribution impedance and compensation capacitor. Proc. CSEE 30(22), 44–50 (2010)

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