A Reliability Assessment Method for Distribution Networks Based on Conditional Generative Adversarial Network and Multi-agent Reinforcement Learning

Huihui XU; Yunfei TIAN; Yuyang ZHAO; Jing PENG; Qingxin SHI; Rui CHENG

doi:10.11930/j.issn.1004-9649.202409074

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A Reliability Assessment Method for Distribution Networks Based on Conditional Generative Adversarial Network and Multi-agent Reinforcement Learning

Artificial Intelligence in Power System | 更新时间：2025-10-29

- A Reliability Assessment Method for Distribution Networks Based on Conditional Generative Adversarial Network and Multi-agent Reinforcement Learning
- “In the field of large-scale distributed photovoltaic access, a reliability evaluation method for distribution networks based on conditional generative adversarial networks and multi-agent reinforcement learning has been proposed, which significantly improves the evaluation accuracy and computational efficiency, and has practical value.”
- Electric Power Vol. 58, Issue 4, Pages: 230-236(2025)
- 作者机构：
  
  1.国网甘肃省电力公司经济技术研究院，甘肃兰州　730030
  2.新能源电力系统国家重点实验室（华北电力大学），北京　102206
- 作者简介：
- 基金信息：
  
  This work is supported by National Natural Science Foundation of China (No.52307094) and Consulting Project of State Grid Gansu Electric Power Co., Ltd. (No.W24FZ2730050)
- DOI：10.11930/j.issn.1004-9649.202409074
  CLC：
- Received：18 September 2024，
  
  Revised：2025-03-14，
  
  Published：28 May 2025
- 稿件说明：
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Huihui XU, Yunfei TIAN, Yuyang ZHAO, et al. A Reliability Assessment Method for Distribution Networks Based on Conditional Generative Adversarial Network and Multi-agent Reinforcement Learning[J]. Electric power, 2025, 58(4): 230-236.
DOI：

Huihui XU, Yunfei TIAN, Yuyang ZHAO, et al. A Reliability Assessment Method for Distribution Networks Based on Conditional Generative Adversarial Network and Multi-agent Reinforcement Learning[J]. Electric power, 2025, 58(4): 230-236. DOI： 10.11930/j.issn.1004-9649.202409074.

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A Reliability Assessment Method for Distribution Networks Based on Conditional Generative Adversarial Network and Multi-agent Reinforcement Learning

DOI：10.11930/j.issn.1004-9649.202409074