An in-depth study of transformer fault diagnosis and prediction based on digital twin technology

Jun Niu; Wenbo Shi; Sisi Long

doi:10.1117/12.3035519

19 July 2024 An in-depth study of transformer fault diagnosis and prediction based on digital twin technology

Jun Niu, Wenbo Shi, Sisi Long

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 1321330 (2024) https://doi.org/10.1117/12.3035519
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

Transformer is the key equipment in the construction of the power system, and its safe and stable operation is the foundation of the power system. Aiming at the problems of difficult identification and low detection accuracy of transformer windings in the running state, a fault diagnosis method of transformer windings based on digital twin technology is proposed. Digital twin, is to make full use of the physical model, sensor updates, operation history and other data, integrate multi-disciplinary, multi-physical quantity, multi-scale, multi-probability simulation process, and complete the mapping in the virtual space, so as to reflect the corresponding physical equipment of the whole life cycle process. A high simulation digital twin transformer model based on the transformer entity is established, and then the information changes of the tank surface vibration information of the digital twin transformer under different working conditions and various winding faults are deduced through multiple physical field simulations. The paper studies and analyses the fault diagnosis of transformers based on the support of digital twin technology and focuses on the prediction of transformer faults. It promotes the prediction of transformer faults as well as research based on digital twin technology.

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

Citation Download Citation

Jun Niu, Wenbo Shi, and Sisi Long "An in-depth study of transformer fault diagnosis and prediction based on digital twin technology", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 1321330 (19 July 2024); https://doi.org/10.1117/12.3035519

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KEYWORDS

Transformers

Data modeling

Education and training

Diagnostics

Mathematical optimization

Statistical modeling

Data acquisition

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