Automated Transformer Health Prediction: Evaluation of Complexity and Linearity of Models for Prediction-Reference-Cited by-同舟云学术

Automated Transformer Health Prediction: Evaluation of Complexity and Linearity of Models for Prediction

Published:2024 Issue: Volume: Page:21-33
ISSN:1876-1100
Container-title:Lecture Notes in Electrical Engineering
language:en
Short-container-title:

Author:

Er Andrea Wong Saen,Wong W. K.^ORCID,Juwono Filbert H.^ORCID,Chew I. M.^ORCID,Sivakumar Saaveethya^ORCID,Gurusamy Arul Paruvachi^ORCID

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-9833-3_3

Reference13 articles.

1. Aizpurua, J.I., Stewart, B.G., McArthur, S.D.J., Lambert, B., Cross, J.G.: Towards a comprehensive DGA health index. In: IEEE 2nd International Conference on Dielectrics (ICD), pp. 1–4 (2018). https://doi.org/10.1109/ICD.2018.8514697

2. Alqudsi, A., El-Hag, A.: Application of machine learning in transformer health index prediction. Energies 12, 2694 (2019). https://doi.org/10.3390/en12142694

3. Arias Velásquez, R.M., Mejía Lara, J.V.: Data for: root cause analysis improved with machine learning for failure analysis in power transformers. https://www.kaggle.com/datasets/shashwatwork/failure-analysis-in-power-transformers-dataset

4. Chantola, A., Sharma, M., Saini, A.: Integrated fuzzy logic approach for calculation of health index of power transformer. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 1045–1050 (2018). https://doi.org/10.1109/ICICCT.2018.8473316

5. Ghoneim, S.S.M., Taha, I.B.M.: Comparative study of full and reduced feature scenarios for health index computation of power transformers. IEEE Access 8, 181326–181339 (2020). https://doi.org/10.1109/ACCESS.2020.3028689