Application Prospect of New Power System Based on Generative Artificial Intelligence
Generative artificial intelligence (AIGC) is an artificial intelligence technology based on generative models, which has powerful capabilities to process and generate video, text, audio, and other content, and can understand and generate interactive information of multimodal complex content. This article focuses on the construction needs of new power systems. Firstly, it introduces the basic principle architecture, underlying models, and application advantages of AIGC. Secondly, based on this, it summarizes the application principles and implementation process of AIGC in four major scenarios: power system prediction, new power system analysis and planning, fault diagnosis and recovery, and power trading assistance. Compared with traditional artificial intelligence technology, it has significant advantages in small sample and multimodal task processing. Finally, the article proposes six solutions from the perspectives of dataset, computing power, and algorithm, taking into account the risks faced in the application of AIGC. These solutions provide a reference for promoting the practical application of AIGC in the power system.
Authors
- Hang Fan (ORCID: https://orcid.org/0000-0002-8308-9332)
- Xiao Tan (ORCID: https://orcid.org/0000-0003-3992-5812)
- Shuaikang Wang
- Hui Qi (ORCID: https://orcid.org/0000-0002-2720-1238)
- Yunjie Duan (ORCID: https://orcid.org/0009-0004-0162-8382)
- Xudong Zhao (ORCID: https://orcid.org/0000-0002-6521-7054)
- Hanhan Qian
- Tao Zhou (ORCID: https://orcid.org/0000-0002-4695-0381)
- Honghu Cheng (ORCID: https://orcid.org/0009-0003-1086-8372)
- Chen Zhang (ORCID: https://orcid.org/0000-0001-9049-091X)
- Zhihui Zhang (ORCID: https://orcid.org/0000-0002-6081-3236)
- Wei Zhang (ORCID: https://orcid.org/0000-0002-1059-3642)
- Dong Wei (ORCID: https://orcid.org/0000-0001-5969-6987)
- Dunnan Liu
- Liuqing Yang (ORCID: https://orcid.org/0009-0000-2568-1373)
Institutions
- North China Electric Power University (CN)
Publication Details
- Published
- 2024-11-29
- DOI
- https://doi.org/10.1109/ei264398.2024.10991506
- Citations
- 1
- Primary Topic
- Smart Grid and Power Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.21