Investigation on machine learning algorithms to support transformer dissolved gas analysis fault identification
Author:
Funder
Politeknik Negeri Malang
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Electrical and Electronic Engineering
Link
https://link.springer.com/content/pdf/10.1007/s00202-022-01532-5.pdf
Reference72 articles.
1. Abu-Siada A (2019) Improved consistent interpretation approach of fault type within power transformers using dissolved gas analysis and gene expression programming. Energies 12:730. https://doi.org/10.3390/en12040730
2. Bakar NA, Abu-Siada A, Cui H, Li S (2017) Improvement of DGA interpretation using scoring index method. In: ICEMPE 2017—1st international conference on electrical materials and power equipment. https://doi.org/10.1109/ICEMPE.2017.7982139
3. Pattanadech N, Sasomponsawatline K, Siriworachanyadee J, Angsusatra W (2019) The conformity of DGA interpretation techniques: experience from transformer 132 units. In: 2019 IEEE 20th international conference on dielectric liquids, pp 1–4. https://doi.org/10.1109/icdl.2019.8796588
4. Faiz J, Soleimani M (2017) Dissolved gas analysis evaluation in electric power transformers using conventional methods a review. IEEE Trans Dielectr Electr Insul 24:1239–1248. https://doi.org/10.1109/TDEI.2017.005959
5. Abu-Siada A, Hmood S, Islam S (2013) A new fuzzy logic approach for consistent interpretation of dissolved gas-in-oil analysis. IEEE Trans Dielectr Electr Insul 20:2343–2349. https://doi.org/10.1109/TDEI.2013.6678888
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