Application of Clustering and Dimensionality Reduction Methods for Finding Patterns on Supraharmonics Data

Supraharmonics (waveform distortion between 2 and 150 kHz) proliferate in electrical installations due to the increasing use of power electronics converters and power-line communication. Due to the wide range that the supraharmonics cover and the high frequency resolution needed to measure them, a considerable amount of data is acquired. The analysis is usually done manually by experts. More efficient methods for data mapping and analysis are needed. Machine learning methods are explored in this paper for the analysis of supraharmonics data.