VISUALIZING THE RESULTS FROM UNSUPERVISED DEEP LEARNING FOR THE ANALYSIS OF POWER-QUALITY DATA
Artikel i vetenskaplig tidskrift, 2021
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)
POWER-SYSTEM HARMONICS
POWER-QUALITY MONITORING
POWER-QUALITY
DEEP LEARNING
MACHINE LEARNING
Författare
Roger Alves De Oliveira
Luleå tekniska universitet
Chenjie Ge
Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik
Irene Yu-Hua Gu
Chalmers, Elektroteknik
Math H. J. Bollen
Luleå tekniska universitet
IET Conference Proceedings
27324494 (eISSN)
Vol. 2021 6 653-657Styrkeområden
Energi
Ämneskategorier
Elektroteknik och elektronik
Datavetenskap (datalogi)
DOI
10.1049/icp.2021.1771