VISUALIZING THE RESULTS FROM UNSUPERVISED DEEP LEARNING FOR THE ANALYSIS OF POWER-QUALITY DATA
Conference contribution, 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)

MACHINE LEARNING

POWER-QUALITY

DEEP LEARNING

POWER-QUALITY MONITORING

POWER-SYSTEM HARMONICS

Author

Roger Alves De Oliveira

Luleå University of Technology

Chenjie Ge

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Signal Processing

Irene Yu-Hua Gu

Chalmers, Electrical Engineering

Math H. J. Bollen

Luleå University of Technology

26th International Conference on Electricity Distribution CIRED, 2021
Geneva, Switzerland,

Areas of Advance

Energy

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering

More information

Latest update

1/28/2021