Physics-Informed Neural Networks for modelling insulation paper degradation in Power Transformers-Reference-Cited by-同舟云学术

Physics-Informed Neural Networks for modelling insulation paper degradation in Power Transformers

Published:2022-10-26 Issue: Volume: Page:
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Container-title:2022 IEEE International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM)
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Author:

Oueslati Khaoula¹,Dhahbi-Megriche Nabila¹,Bragone Federica²,Morozovska Kateryna²,Laneryd Tor³,Luvisotto Michele³

Affiliation:

1. MMA Lab University of Carthage,National Institute of Applied Sciences and Technology,Tunis,Tunisia

2. KTH Royal Institute of Technology,Department of Computer Science,Stockholm,Sweden

3. Hitachi Energy Research,Västerås,Sweden

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10043831/10043859/10043884.pdf?arnumber=10043884

Reference20 articles.

1. The development of the Arrhenius equation

2. The behaviour of cellulose in mineral acid solutions: kinetic study of the decomposition of cellulose in acid solutions;ekenstam;Berichte der deutschen chemischen Gesellschaft,1936

3. Kinetics and mechanisms of the low-temperature degradation of cellulose

4. Aging of Oil-Impregnated Paper in Power Transformers

5. Characterizing possible failure modes in physics-informed neural networks;krishnapriyan;Advances in neural information processing systems,2021

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1. A PINN-Based Lifetime Predictor for Oil-Impregnated Paper Insulation Degradation in Power Transformers Using Degree of Polymerization;2024 IEEE 18th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG);2024-06-24