K-nearest neighbor and naïve Bayes based diagnostic analytic of harmonic source identification

Mohd Hatta Jopri, Mohd Ruddin Ab Ghani, Abdul Rahim Abdullah, Mustafa Manap, Tole Sutikno, Jingwei Too


This paper proposes a comparison of machine learning (ML) algorithm known as the k-nearest neighbor (KNN) and naïve Bayes (NB) in identifying and diagnosing the harmonic sources in the power system. A single-point measurement is applied in this proposed method, and using the S-transform the measurement signals are analyzed and extracted into voltage and current parameters. The voltage and current features that estimated from time-frequency representation (TFR) of S-transform analysis are used as the input for MLs. Four significant cases of harmonic source location are considered, whereas harmonic voltage (HV) and harmonic current (HC) source type-load are used in the diagnosing process. To identify the best ML, the performance measurement of the proposed method including the accuracy, precision, specificity, sensitivity, and F-measure are calculated. The sufficiency of the proposed methodology is tested and verified on IEEE 4-bust test feeder and each ML algorithm is executed for 10 times due to prevent any overfitting result.


Harmonic current source; Harmonic source diagnosis; Harmonic voltage source; K-nearest neighbor; Naïve Bayes; S-transform

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DOI: https://doi.org/10.11591/eei.v9i6.2685


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