Unsupervised deep learning and analysis of harmonic variation patterns using big data from multiple locations

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.

Automated summary

This paper introduces a novel framework using deep autoencoder and k-mean clustering to automatically seek and identify daily, weekly and seasonal patterns in harmonic voltage from measurement data at multiple locations. By training the encoder at one selected location and extracting features from multiple locations, the patterns can be effectively analyzed. Various empirical analysis methods are applied to extract underlying patterns from harmonic data and assist network operators in decision-making.