Diagnosis of AD and DSV Winding Faults Based on FRA Method and Random Forest Algorithm
Author:
Affiliation:
1. Northewest A&F University,Department of Power and Electrical Engineering,Yangling,China
2. State Grid Shaanxi Electric Power Company,Xi’an,China
3. Huangling Mining Group Co., Ltd. of Shaanxi Coal and Chemical Industry Group Huangling,China
Funder
Fundamental Research Funds for the Central Universities
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10139313/10138934/10139451.pdf?arnumber=10139451
Reference17 articles.
1. Transformer Oil Quality Assessment Using Random Forest with Feature Engineering
2. Fault discrimination scheme for power transformer using random forest technique
3. The actual measurement and analysis of transformer winding deformation fault degrees by FRA using mathematical indicators;jianqiang;Electr Power Syst Res,2020
4. Classification and Discrimination Among Winding Mechanical Defects, Internal and External Electrical Faults, and Inrush Current of Transformer
5. Identification of Power Transformer Currents by Using Random Forest and Boosting Techniques
Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Diagnosing Fault Types and Degrees of Transformer Winding Combining FRA Method With SOA-KELM;IEEE Access;2024
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