Analysis and prospect of power transformer fault diagnosis technology

Yong Gao; Jie Wang

doi:10.1117/12.3015347

6 February 2024 Analysis and prospect of power transformer fault diagnosis technology

Yong Gao, Jie Wang

Proceedings Volume 12979, Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023); 129795H (2024) https://doi.org/10.1117/12.3015347
Event: 9th International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023), 2023, Guilin, China

Abstract

The transformer stands as a vital component within the power system, serving a crucial role in voltage regulation and energy transmission throughout the entire process of electricity generation, transmission, distribution, and consumption. Furthermore, it holds a pivotal position in facilitating Alternate Current (AC) and Direct Current (DC) conversion within DC transmission. This paper begins by offering an overview of common anomalies and fault categories that arise during transformer operation, focusing on the role of power transformers within system operations. It subsequently delves into the contemporary methods and diagnostic processes used for identifying and diagnosing transformer faults. The paper then proceeds to expound upon cases involving winding deformation and insulating oil failure, illustrating instances of these particular types of faults. Finally, the paper outlines two prospective avenues for transformer fault diagnosis technology. The first entails the incorporation of artificial intelligence theories in the realm of transformer fault diagnosis. The second involves the development of an expert system for comprehensive transformer fault diagnosis, grounded in artificial intelligence technology.

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

Citation Download Citation

Yong Gao and Jie Wang "Analysis and prospect of power transformer fault diagnosis technology", Proc. SPIE 12979, Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023), 129795H (6 February 2024); https://doi.org/10.1117/12.3015347

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KEYWORDS

Transformers

Dielectrics

Iron

Deformation

Artificial intelligence

Diagnostics

Environmental monitoring

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