VISUALIZING THE RESULTS FROM UNSUPERVISED DEEP LEARNING FOR THE ANALYSIS OF POWER-QUALITY DATA

Artikel i vetenskaplig tidskrift, 2021

This paper presents a visualisation method, based on deep learning (DL), to assist power engineers in the analysis of large
amounts of power-quality data. The method assists in extracting and understanding daily, weekly and seasonal variations in
harmonic voltage. Measurements from 10 kV and 0.4 kV in a Swedish distribution network are applied to method to obtain
daily harmonic patterns and their distribution over the week and the year. The results are presented in graphs that allow the
interpretation of the results without having to understand the mathematical details of the method. The inferences given by the results demonstrate that the method can become a new tool that compresses PQ big data in a form that is easier to interpret. (for details, see the full paper (5 pages) in the conf. proceeding)