Naïve Bayes and linear discriminate analysis based diagnostic analytic of harmonic source identification

Abstract

The diagnostic analytic type of harmonic source is a vital research due to diagnose and identify type of harmonic source that exist in the power system. This paper presents a comparison of machine learning (ML) algorithm namely as the Naïve Bayes (NB) and linear discriminate analysis (LDA) in identifying and diagnosing the harmonic sources.  The MLs inputs are the voltage and current feature sets that estimated from the time-frequency representation (TFR) of S-transform analysis. Four specific cases of harmonic source location are considered in this research, whereas harmonic voltage (H_V) and harmonic current (H_C) source type-load are used in the diagnosing process. The sufficiency of the proposed methodology is tested and verified on the IEEE 4-bust test feeder, and to prevent overfitting, the K-fold cross-validation technique is implemented for performance evaluation. To identify the best ML, the performance measurement consist of the accuracy, precision, geometric mean, F-measure, sensitivity, and specificity are conducted.