Deep Feature Clustering for Seeking Patterns in Daily Harmonic Variations
Journal article, 2021

This article proposes a novel scheme for analyzing power system measurement data. The main question that we seek answers in this study is on “whether one can find some important patterns that are hidden in the large data of power system measurements such as variational data.” The proposed scheme uses an unsupervised deep feature learning approach by first employing a deep autoencoder (DAE) followed by feature clustering. An analysis is performed by examining the patterns of clusters and reconstructing the representative data sequence for the clustering centers. The scheme is illustrated by applying it to the daily variations of harmonic voltage distortion in a low-voltage network. The main contributions of the article include: 1) providing a new unsupervised deep feature learning approach for seeking possible underlying patterns of power system variation measurements and 2) proposing an effective empirical analysis approach for understanding the measurements through examining the underlying feature clusters and the associated reconstructed data by DAE.

clustering

power quality

Autoencoder

unsupervisedlearning.

deep learning

power system harmonics

patternanalysis

Author

Chenjie Ge

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

Roger Alves de Oliverra

Luleå University of Technology

Irene Yu-Hua Gu

Chalmers, Electrical Engineering

Math H.J. Bollen

Luleå University of Technology

IEEE Transactions on Instrumentation and Measurement

0018-9456 (ISSN)

Vol. 70 1-10 2501110

Areas of Advance

Energy

Subject Categories

Signal Processing

Other Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1109/TIM.2020.3016408

More information

Latest update

1/28/2021