Linear discriminate analysis and K-nearest neighbor based diagnostic analytic of harmonic source identification

The diagnostic analytic of harmonic source is crucial research due to identify and diagnose the harmonic source in the power system. This paper presents a comparison of machine learning (ML) algorithm known as linear discriminate analysis (LDA) and k-nearest neighbor (KNN) in identifying and diagnosing the harmonic sources. Voltage and current features that estimated from time-frequency representation (TFR) of S-transform analysis are used as the input for ML. Several unique cases of harmonic source location are considered, whereas harmonic voltage (H V ) and harmonic current (H C ) source type-load are used in the diagnosing process. To identify the best ML, the performance criteria are measured consist of the accuracy, precision, geometric mean, specificity, sensitivity, and F-measure are calculated. The adequacy of the proposed methodology is tested and verified on the IEEE 4-bust test feeder, and each ML algorithm is executed 10 times due to prevent any overfitting result.