Power Transformer Breathing System Condition Monitoring Based on Pressure–Temperature Optical Sensing and Deep Learning Method.
Saved in:
| Title: | Power Transformer Breathing System Condition Monitoring Based on Pressure–Temperature Optical Sensing and Deep Learning Method. |
|---|---|
| Authors: | Liang, Jiabi1 (AUTHOR), Shao, Jian1,2 (AUTHOR), Wu, Peng1 (AUTHOR), Li, Qun1,2 (AUTHOR), Lu, Yuncai1 (AUTHOR), Wang, Yalin2 (AUTHOR), Lei, Zhaokai2 (AUTHOR) yikeanee@163.com |
| Source: | Energies (19961073). Mar2026, Vol. 19 Issue 5, p1130. 24p. |
| Subject Terms: | *Power transformers, *Deep learning, *Fabry-Perot interferometers, *Convolutional neural networks, *Fault diagnosis, *Transformer models, *Optical fiber detectors, *Fiber Bragg gratings |
| Abstract: | During long-term operation of power transformers, oil temperature and pressure exhibit strong non-stationarity and multi-scale coupling, which makes early-stage breathing system faults difficult to detect accurately. To address this issue, this paper proposes an integrated diagnosis and early-warning method for transformer breathing systems. It combines a multi-parameter optical sensor with a deep-learning algorithm. The pressure–temperature optical sensing system based on Fabry–Pérot (F–P) interferometry and fiber Bragg grating (FBG) technology is developed to achieve high-precision synchronous measurement of pressure and temperature. To handle the non-stationary and multi-scale characteristics of the measured signals, a swarm-intelligence-optimized variational mode decomposition (VMD) method is employed to adaptively decompose time series temperature and pressure data. On this basis, a joint forecasting model integrating a temporal convolutional network (TCN) and an inverted Transformer (iTransformer) is constructed to capture both local temporal dynamics and long-term dependencies. Furthermore, based on the pressure equilibrium mechanism of transformer breathing systems, oil temperature and equivalent oil level are inferred, and abnormality criteria suitable for both multi-point and single-point monitoring are established. Experimental and field tests on a 220 kV transformer demonstrate that the proposed method outperforms conventional models in prediction accuracy. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
Be the first to leave a comment!
