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Turkish Journal of Electrical Engineering and Computer Sciences

Author ORCID Identifier

AHMAD FARID ABIDIN: 0000-0001-5260-762X

MOHD ABDUL TALIB MAT YUSOH: 0000-0002-4503-826X

ABDUL HAFIZ KASSIM: 0000-0003-3433-6007

NIK HAKIMI NIK ALI: 0000-0003-0672-9129

Abstract

The impact of excessive harmonic distortion on electrical distribution networks highlights the critical need to understand harmonics in different lighting technologies to detect sources of issues and their effects. The increasing prevalence of nonlinear loads in power systems has contributed to significant harmonic pollution, deteriorating power line quality. This paper aims to address this issue by focusing on power line frequency detection and the classification of lighting technologies, combining empirical mode decomposition (EMD) with the one-versus-one support vector machine (OVO-SVM) approach. EMD is utilized to analyze signals through intrinsic mode functions, preserving the signal’s characteristics while extracting features related to harmonic distortion from various lighting types, including light emitting diodes, compact fluorescent lamps, and incandescent bulbs. The integration with MATLAB/Simulink and an Arduino Uno enables real-time monitoring, and the use of quantization and FFT implementations measures total harmonic distortion. In terms of classification, the OVO-SVM using polynomial kernel functions outperforms the RBF kernel functions across various training-to-testing splits (50/50, 60/40, 70/30, and 80/20). Additionally, the performance of the OVO-SVM with polynomial kernel functions aligns closely with the gradient boosting machine classifier, showing identical results in accuracy, precision, recall, and F1-score. This suggests that both models are highly effective in classifying the types of harmonic distortions associated with different lighting technologies, offering an efficient method for power quality disturbances detection and classification in power systems.

DOI

10.55730/1300-0632.4130

Keywords

Empirical Mode Decomposition (EMD), Harmonic, One Versus One- Support Vector Machine (OVO-SVM)

First Page

337

Last Page

356

Publisher

The Scientific and Technological Research Council of Türkiye (TÜBİTAK)

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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