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Research Article |

Data Mining in Power System Fault Identification using Artificial Intelligence

Author(s): Nhung Le Thi Hong1, Trong Nghia Le2*, Tan Phung Trieu3, Hoang Le Thi Thanh4, Thanh Nguyen Tan5

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 13, Issue 2

Publisher : FOREX Publication

Published : 30 May 2025

e-ISSN : 2347-470X

Page(s) : 209-217

DOI: https://doi.org/10.37391/IJEER.130204

Abstract

This paper presents a power system fault identification method by simultaneously applying the Kendall and Spearman correlation coefficients for feature selection, combined with an Artificial Neural Network (ANN) to enhance accuracy and optimize training time. Experimental results indicate that Kendall demonstrates superior performance in handling nonlinear data and mitigating the impact of outliers, leading to more optimal fault identification outcomes. Backpropagation Neural Network (BPNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) models are trained on datasets after feature selection using both correlation coefficients. The results show that Kendall significantly improves model performance, with BPNN + Kendall achieving RMSE = 0.0001, MAE = 0.0059, and a training time of 0.8946s, outperforming other methods. The proposed approach not only enhances accuracy but also optimizes processing time, reaffirming the effectiveness of Kendall and Spearman in feature selection for power system fault identification.

Keywords: Data preprocessing, Correlation coefficient, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Backpropagation Neural Network (BPNN).




Nhung Le Thi Hong, Master of Engineering, Faculty of Electrical and Electronic Engineering, HCMC University of Technology and Education;Email: nhunglth@hcmute.edu.vn

Trong Nghia Le, Doctor of Philosophy, Faculty of Electrical and Electronic Engineering, HCMC University of Technology and Education;Email: trongnghia@hcmute.edu.vn

Tan Phung Trieu, Master of Engineering, Faculty of Electrical and Electronics Engineering, Cao Thang Technical College, Ho Chi Minh city, Vietnam ;

Hoang Le Thi Thanh, Master of Engineering, Faculty of Electrical and Electronic Engineering, HCMC University of Technology and Education;Email: hoangltt@hcmute.edu.vn

Thanh Nguyen Tan, Master of Engineering, Faculty of Electrical and Electronics Engineering, Cao Thang Technical College, Ho Chi Minh city, Vietnam; Email: ntthanh@caothang.edu.vn

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Nhung Le Thi Hong, Trong Nghia Le, Tan Phung Trieu, Hoang Le Thi Thanh, and Thanh Nguyen Tan (2025), Data Mining in Power System Fault Identification using Artificial Intelligence . IJEER 13(2), 209-217. DOI: 10.37391/IJEER.130204.