Naslov
Support-vector machine and Naïve Bayes based diagnostic analytic of harmonic source identification

Autori
Mohd, Hatta, Jopri ; Abdul, Rahim, Abdullah ; Jingwei, Too ; Tole, Sutikno ; Nikolovski, Srete ; Mustafa, Manap ;

Izvornik
Indonesian Journal of Electrical Engineering and Computer Science (2502-4752) 20 (2020), 1; 1-7

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Harmonic source diagnosis, Naïve Bayes, S-transform, Support-vector machineine
(Harmonic source diagnosis, Naïve Bayes, S-transform, Support-vector machine)

Sažetak
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. 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.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika

POVEZANOST RADA