Research on Quantitative Evaluation Methods for Artificial Intelligence in Electric Power
The advancement of artificial intelligence technology offers a novel approach to addressing the complexities inherent in electricity regulation. As the application of artificial intelligence technology in power regulation continues to expand, there is a growing demand for comprehensive evaluations of this technology's capabilities. In light of this context, this paper introduces an objective quantitative evaluation methodology for assessing the multi-dimensional capabilities of electric power artificial intelligence technologies. The proposed method begins by establishing an intelligent evaluation index system, followed by the selection of relevant intelligence indices, and culminates in the formulation of an evaluation equation for these indices to compute the multi-dimensional intelligence level associated with electric power artificial intelligence technologies. Subsequently, this study employs the proposed methodology to conduct simulation experiments on the IEEE 39-bus systems aimed at assessing system security post-regulation through artificial intelligence interventions. The results demonstrate that the proposed method effectively evaluates the multi-dimensional intelligence levels within power-related artificial intelligences while confirming its reliability.
Authors
- Han Xiao (ORCID: https://orcid.org/0000-0001-5655-0173)
- Yun Zhang
- Jun Zhang (ORCID: https://orcid.org/0000-0002-1320-1886)
- Huan Ma
- Zeyu Mu
- Xinying Wang (ORCID: https://orcid.org/0000-0001-8485-9979)
- Qi Zhao (ORCID: https://orcid.org/0000-0001-6750-966X)
- Tianyun Zhang (ORCID: https://orcid.org/0009-0004-3215-9603)
- Haozhe Zhan
Institutions
- State Grid Corporation of China (China) (CN)
- Beijing Electronic Science and Technology Institute (CN)
- Wuhan University (CN)
Publication Details
- Published
- 2024-12-06
- DOI
- https://doi.org/10.1109/icips64173.2024.10900333
- Primary Topic
- Smart Grid and Power Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00