Unsupervised Deep Learning and Analysis of Harmonic Variation Patterns using Big Data from Multiple Locations
Artikel i vetenskaplig tidskrift, 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.

clustering

power system harmonics

variation data

Electric Power distribution

unsupervised deep learning

pattern analysis

autoencoder

power quality

big data analytics

Författare

Chenjie Ge

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Roger Alves De Oliveira

Luleå tekniska universitet

Irene Yu-Hua Gu

Chalmers, Elektroteknik

Math H. J. Bollen

Luleå tekniska universitet

Electric Power Systems Research

0378-7796 (ISSN)

Vol. 194 107042

Styrkeområden

Energi

Ämneskategorier

Energisystem

Annan elektroteknik och elektronik

DOI

10.1016/j.epsr.2021.107042

Mer information

Senast uppdaterat

2021-02-22