Classification of Unintended Radiated Emissions in a Multi-Device Environment

Jason M. Vann, Thomas Karnowski, Adam L. Anderson

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Unintended radiated emissions (URE) from electronic devices are conducted on to the power infrastructure and can be collected and analyzed for non-intrusive load monitoring applications. Dimensionally aligned signal projections (DASP) were previously introduced as specialized signal transforms to enable device classification using machine learning on statistical features derived from the DASP transforms. In this paper, we explore multi-device classification which requires more sophisticated methods owing to the complexity of the feature space, such as large dynamic range, non-contiguous features, over-lapping features, and nonlinear interactions of features. In particular, we introduce an additional DASP algorithm for increased resolution and sensitivity to intermodulation products and examine the utility of convolutional neural networks to extract and learn features directly from DASP images for classification of single and multiple device URE captures.

Original languageEnglish
Pages (from-to)5506-5513
Number of pages8
JournalIEEE Transactions on Smart Grid
Volume10
Issue number5
DOIs
StatePublished - Sep 1 2018

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Keywords

  • Unintended radiated emissions
  • convolutional neural networks
  • multi-label classification
  • non-intrusive load monitoring

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