Precise transformer fault diagnosis via random forest model enhanced by synthetic minority over-sampling technique-Reference-Cited by-同舟云学术

Precise transformer fault diagnosis via random forest model enhanced by synthetic minority over-sampling technique

Published:2023-07 Issue: Volume:220 Page:109361
ISSN:0378-7796
Container-title:Electric Power Systems Research
language:en
Short-container-title:Electric Power Systems Research

Author:

Prasojo Rahman Azis,Putra Muhammad Akmal A.,Ekojono ,Apriyani Meyti Eka,Rahmanto Anugrah Nur,Ghoneim Sherif S.M.,Mahmoud Karar,Lehtonen Matti,Darwish Mohamed M.F.

Funder

Taif University

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Reference43 articles.

1. Dissolved gas analysis equipment for online monitoring of transformer oil: a review;Bustamante;Sensors (Switzerland),2019

2. Accuracy improvement of power transformer faults diagnostic using KNN classifier with decision tree principle;Kherif;IEEE Access,2021

3. Development of analytic hierarchy process technique in determining weighting factor for power transformer health index;Prasojo,2019

4. A multiple expert consensus model for transformer assessment index weighting factor determination;Prasojo,2020

5. Integrated ANN-based proactive fault diagnostic scheme for power transformers using dissolved gas analysis;Ghoneim;IEEE Trans. Dielectr. Electr. Insul.,2016

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