Unsupervised Deep Learning and Analysis of Harmonic Variation Patterns using Big Data from Multiple Locations
Journal article, 2021

This paper addresses the issue of automatically seeking and identifying daily, weekly and seasonal patterns in harmonic voltage from measurement data at multiple locations. We propose a novel framework that employs deep autoencoder (DAE) followed by k-mean clustering. The DAE is used for extracting principal features from time series of harmonic voltages. A new strategy is used for training the encoder in DAE from data at one selected location that is effective for subsequent feature extraction from data at multiple locations. To analyze the patterns, several empirical analysis approaches are applied on the clustered principal features, including the distribution of daily patterns over the week and the year, representative waveform sequences of individual classes, and feature maps for visualizing high-dimensional feature space through low-dimensional embedding. The proposed scheme has been tested on a dataset containing harmonic measurements at 10 low-voltage locations in Sweden for the whole year of 2017. Results show distinct principal patterns for most harmonics that can be related to the use of equipment causing harmonic distortion. This information can assist network operators in finding the origin of harmonic distortion and deciding about mitigation actions. The proposed scheme is the first to provide a useful analysis tool and insight for finding and analyzing underlying patterns from harmonic variation data at multiple locations.


power system harmonics

variation data

Electric Power distribution

unsupervised deep learning

pattern analysis


power quality

big data analytics


Chenjie Ge

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

Roger Alves De Oliveira

Luleå University of Technology

Irene Yu-Hua Gu

Chalmers, Electrical Engineering

Math H. J. Bollen

Luleå University of Technology

Electric Power Systems Research

0378-7796 (ISSN)

Vol. 194 107042

Areas of Advance


Subject Categories

Energy Systems

Other Electrical Engineering, Electronic Engineering, Information Engineering



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