Data twin system based on high-dimensional data analysis

Qiang Li; Feng Zhao; Linlin Zhao; Xuhong Qin; Yana Zhu; Yubo Wang

doi:10.1117/12.3045152

30 September 2024 Data twin system based on high-dimensional data analysis

Qiang Li, Feng Zhao, Linlin Zhao, Xuhong Qin, Yana Zhu, Yubo Wang

Author Affiliations +

Proceedings Volume 13286, Third International Conference on Electronics Technology and Artificial Intelligence (ETAI 2024); 132861I (2024) https://doi.org/10.1117/12.3045152
Event: Third International Conference on Electronics Technology and Artificial Intelligence (ETAI 2024), 2024, Guangzhou, China

Abstract

The integration of distributed massive renewable energy, energy storage, and electric vehicles has altered the original operation mode of the distribution network, transforming it from deterministic to stochastic, and from unidirectional to bidirectional power flow. Phenomena such as voltage exceeding limits, forward and reverse overloads, and reverse power flow in transformer zones have become increasingly frequent. Therefore, it is crucial to accurately and promptly grasp the operational status of the distribution network, eliminate potential safety hazards and operational risks, and ensure the safe and stable operation of the power grid. However, the distribution network faces challenges such as a wide distribution of points, complex structure, and incomplete coverage of measurement and acquisition points. With the construction of a new power system, the number of electrical equipment, generator nodes, and load nodes has multiplied, resulting in a surge in the amount of data and increasing demands for computational efficiency. Additionally, the complexity of the distribution network and the uncertainty of distributed power sources pose challenges for data identification and analysis. To address these issues, this paper proposes a high-dimensional data analysis technology based on digital twins, which is used to analyze the operational status of the distribution network and the interactive and collaborative relationship between power sources, loads, and storage. This approach enhances the management and control capabilities of optimal scheduling, maximum consumption, and safe and stable operation of the distribution network.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qiang Li, Feng Zhao, Linlin Zhao, Xuhong Qin, Yana Zhu, and Yubo Wang "Data twin system based on high-dimensional data analysis", Proc. SPIE 13286, Third International Conference on Electronics Technology and Artificial Intelligence (ETAI 2024), 132861I (30 September 2024); https://doi.org/10.1117/12.3045152

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KEYWORDS

Data modeling

Power grids

Data analysis

Data storage

Artificial intelligence

Matrices

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