Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Accuracy improvement of power transformer faults diagnostic using KNN classifier with decision tree principle

Kherif, Omar, Benmahamed, Youcef, Teguar, Madjid, Boubakeur, Ahmed and Ghoneim, Sherif S. M. 2021. Accuracy improvement of power transformer faults diagnostic using KNN classifier with decision tree principle. IEEE Access 9 , 81693 - 81701. 10.1109/ACCESS.2021.3086135

[thumbnail of Accuracy_Improvement_of_Power_Transformer_Faults_Diagnostic_Using_KNN_Classifier_With_Decision_Tree_Principle.pdf] PDF - Published Version
Available under License Creative Commons Attribution.

Download (4MB)


Dissolved gas analysis (DGA) is the standard technique to diagnose the fault types of oil-immersed power transformers. Various traditional DGA methods have been employed to detect the transformer faults, but their accuracies were mostly poor. In this light, the current work aims to improve the diagnostic accuracy of power transformer faults using artificial intelligence. A KNN algorithm is combined with the decision tree principle as an improved DGA diagnostic tool. A total of 501 dataset samples are used to train and test the proposed model. Based on the number of correct detections, the neighbor’s number and distance type of the KNN algorithm are optimized in order to improve the classifier’s accuracy rate. For each fault, indeed, several input vectors are assessed to select the most appropriate one for the classifier’s corresponding layer, increasing the overall diagnostic accuracy. On the basis of the accuracy rate obtained by knots and type of defect, two models are proposed where their results are compared and discussed. It is found that the global accuracy rate exceeds 93% for the power transformer diagnosis, demonstrating the effectiveness of the proposed technique. An independent database is employed as a complimentary validation phase of the proposed research.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Additional Information: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 2169-3536
Date of First Compliant Deposit: 5 August 2021
Date of Acceptance: 28 May 2021
Last Modified: 13 May 2023 01:00

Citation Data

Cited 17 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics