Abstract
This paper investigates the aging behavior of oil-impregnated paper insulation in power transformers by estimating the degree of polymerization (DP). The paper proposes an efficient and reliable physics-based lifetime estimation model cou-pled with artificial intelligence techniques. The RUL estimation is important for the industry to develop the power transformers' condition-based maintenance plan and to avoid operation inter-ruption in the power systems. As a result, this approach will help transition the prognostics and health management system from a “fail and fix” strategy to a “predict and prevent” strategy. The physics-informed neural network (PINN) model is proposed to predict the DP of the insulation paper over a certain operating time considering an additive noise to the input measurements and two types of insulation papers (non-thermally upgraded paper and thermally upgraded paper). The simulation results are carried out to demonstrate that the proposed PINN offers an improvement in RUL estimation relative to the conventional neural network without the inclusion of the physical model. Furthermore, the proposed solution is predicated on using the model that achieves the optimal solution with the least possible number of learning epochs.
Original language | English |
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Title of host publication | CPE-POWERENG 2024 - 18th International Conference on Compatibility, Power Electronics and Power Engineering, Proceedings |
Editors | Kalina Detka, Krzysztof Gorecki, Pawel Gorecki |
Place of Publication | Gdynia, Poland |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 1-6 |
Number of pages | 6 |
ISBN (Electronic) | 9798350318265 |
ISBN (Print) | 979-8-3503-1826-5 |
DOIs | |
Publication status | Published - 30 Jul 2024 |
Keywords
- Degree of polymerization
- oil-impregnated paper insulation
- physics-informed neural networks
- predictive mainte-nance
- remaining useful lifetime