Dissolved Gas Analysis of Transformer: An Approach Based on ML and MCDM-Reference-Cited by-同舟云学术

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Dissolved Gas Analysis of Transformer: An Approach Based on ML and MCDM

Published:2023-10 Issue:5 Volume:30 Page:2429-2438
ISSN:1070-9878
Container-title:IEEE Transactions on Dielectrics and Electrical Insulation
language:
Short-container-title:IEEE Trans. Dielect. Electr. Insul.

Author:

Saroja S.¹,Haseena S.²^ORCID,Madavan R.³^ORCID

Affiliation:

1. Computer Applications Department, National Institute of Technology at Tiruchirappalli, Tiruchirappalli, Tamilnadu, India

2. Information Technology Department, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India

3. Electrical and Electronics Engineering Department, PSR Engineering College, Sivakasi, Tamilnadu, India

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/94/10266905/10110923.pdf?arnumber=10110923

Reference32 articles.

1. Prediction of dissolved gas in power transformer oil based on random forests algorithm

2. Power transformer fault diagnosis based on extension theory

3. Energy Theft Detection Using Gradient Boosting Theft Detector With Feature Engineering-Based Preprocessing

4. Fault diagnosis of power transformer based on support vector machine with genetic algorithm

5. Random indices for incomplete pairwise comparison matrices

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1. A Review of Predictive Analytics Models in the Oil and Gas Industries;Sensors;2024-06-20

2. Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers;Scientific Reports;2024-03-13

3. Detecting multiple coexisting emotions from public emergency opinions;Journal of Information Science;2024-02-21

4. Discernment of transformer oil stray gassing anomalies using machine learning classification techniques;Scientific Reports;2024-01-03

5. Functional Failure Criticality Assessment and Control of Power Distribution Systems: Failure Prioritization Using AHP;IEEE Access;2024

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