Identification of Power Transformer Currents by Using Random Forest and Boosting Techniques
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-18
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
In this research, a differential protection technique for a power transformer is proposed by using random forest and boosting learning machines.
The proposed learning machines aim to provide a protection expert system that distinguishes between different transformer status which are normal, inrush, overexcitation, CT saturation, or internal fault.
Data for 20 different transformers with 5 operating cases are used in this research.
The utilized random forest and boosting techniques are trained using these data.
Meanwhile, the proposed models are validated by other measures such as out-of-bag error and confusion matrix.
In addition, variable importance analysis that shows signal’s component importance inside a transformer at different instances is provided.
According to the result, the proposed random forest model successfully identifies all of the current cases (100% accuracy for the conducted experiment).
Meanwhile, it is found that it is less accurate as a conditional monitoring element with accuracy in the range of 97%–98%.
On the other hand, the proposed boosting model identifies all of the currents for both cases (100% accuracy for the conducted experiment).
In addition to that, a comparison is conducted between the proposed models and other AI-based models.
Based on this comparison, the proposed boosting model is the simplest and the most accurate model as compared to other models.
American Psychological Association (APA)
Khatib, Tamer& Arar, Gazi. 2020. Identification of Power Transformer Currents by Using Random Forest and Boosting Techniques. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1193098
Modern Language Association (MLA)
Khatib, Tamer& Arar, Gazi. Identification of Power Transformer Currents by Using Random Forest and Boosting Techniques. Mathematical Problems in Engineering No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1193098
American Medical Association (AMA)
Khatib, Tamer& Arar, Gazi. Identification of Power Transformer Currents by Using Random Forest and Boosting Techniques. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1193098
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1193098