Non-Intrusive Load Monitoring Algorithm for PV Identification in the Residential Sector

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a novel approach for identification of photovoltaic systems in the residential sector. This is needed in response to increasing embedded generation on distribution networks. To date non-intrusive load monitoring techniques have focused mostly on identifying conventional loads on the customer side. This paper demonstrates the application of non-intrusive load monitoring to identify residential distributed generation, thereby enabling techniques to improve system flexibility and stability. The demonstrated methodology combines basic statistics with the Support Vector Machine technique, to identify PV load signatures. PMU measurements from the residential sector are used to aggregate measurements based largely on electric current records. The methods presented have applications for network operators, both in real time control and generation scheduling.

Original languageEnglish
Title of host publication2020 31st Irish Signals and Systems Conference (ISSC 2020): Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728194189
DOIs
Publication statusPublished - 31 Aug 2020
Event31st Irish Signals and Systems Conference, ISSC 2020 - Letterkenny, Ireland
Duration: 11 Jun 202012 Jun 2020

Publication series

Name2020 31st Irish Signals and Systems Conference, ISSC 2020
PublisherIEEE
ISSN (Print)2688-1446
ISSN (Electronic)2688-1454

Conference

Conference31st Irish Signals and Systems Conference, ISSC 2020
CountryIreland
CityLetterkenny
Period11/06/202012/06/2020

Keywords

  • Distributed Energy Resources
  • Non-Intrusive Load Monitoring
  • OpenPMU
  • Photovoltaic systems
  • Support Vector Machine

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Signal Processing

Fingerprint Dive into the research topics of 'Non-Intrusive Load Monitoring Algorithm for PV Identification in the Residential Sector'. Together they form a unique fingerprint.

Cite this