AUTHOR=Zhou Xiu , Luo Yan , Tian Tian , Bai Haonan , Wu Peng , Liu Weifeng 

TITLE=Transformer fault diagnosis based on probabilistic neural networks combined with vibration and noise characteristics

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2023.1169508

DOI=10.3389/fenrg.2023.1169508

ISSN=2296-598X

ABSTRACT=When the transformer is running, the vibration which generated in the core and winding will spread outward through the medium of metal, oil and air. The magnetic field of the core change with the variation of the transformer excitation source and the state of the core, so that the corresponding vibration and noise change. Therefore, the vibration and noise of the transformer contain a lot of information. If the information can be associated with the fault characteristics of the transformer, it is significance to evaluate the running state of the transformer through the vibration and noise signal, which improve the intelligent, safety and stability of the transformer operation. Based on this, modeling and simulation of transformer multi-point grounding, DC bias and short-circuit between silicon steel sheets fault are firstly carried out in this paper, and vibration and noise distribution of transformer under different faults are given. Secondly, a fault diagnosis method based on transformer vibration and noise characteristics is proposed. In the process of implementation, the vibration and noise signals under multi-point grounding, DC bias and short-circuit between silicon steel sheets are taken as the sample data, and the probabilistic neural network algorithm is used to effectively predict the transformer fault. Finally, the effectiveness of the proposed scheme is verified by identifying the simulation faults.