Support-vector machine and naïve bayes based diagnostic analytic of harmonic source identification

Mohd Hatta Jopri (Universiti Teknikal Malaysia Melaka)
Abdul Rahim Abdullah (Universiti Teknikal Malaysia Melaka)
Jingwei Too (Universiti Teknikal Malaysia Melaka)
Tole Sutikno (Universitas Ahmad Dahlan)
Srete Nikolovski (University of Osijek)
Mustafa Manap (Universiti Teknikal Malaysia Melaka)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 20, No 1: October 2020

A harmonic source diagnostic analytic is a vital to identify the location and type of harmonic source in the power system. This paper introduces a comparison of machine learning (ML) algorithm which are support vector machine (SVM) and naïve bayes (NB). Voltage and current features are used as the input for ML are extracted from time-frequency representation (TFR) of S-transform. Several unique cases of harmonic source location are considered, whereas harmonic voltage and harmonic current source type-load are used in the diagnosing process. To identify the best ML, the performance measurement of the propose method including accuracy, specificity, sensitivity, and F-measure are calculated. The adequacy 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 different partitions and to prevent any overfitting result.