Implementation of a virtual-instrument for non-intrusive intelligent real-time load monitoring

S. C. Chen, H. T. Yang

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

2 Citations (Scopus)

Abstract

A load monitoring system can be used to monitor the power consumption of a load. Traditionally, a measurement device is needed for an individual load. In this paper, a real-time load recognition algorithm is applied to a non-intrusive load monitoring (NILM) system. Data for voltages and currents are acquired through the monitoring device installed in the power entrance. To achieve real-time load recognition, the Self-Organizing Maps (SOM) based on effective input features is used to classify the loads on line. The system performing the NILM is developed on virtual instrument (VI) using Lab VIEW software associated with a data acquisition (DAQ) card. The performance of the system has been tested and verified by using practical combinations of loads. Promising results have been obtained and presented in this paper.

Original languageEnglish
Title of host publicationProceedings of the IASTED International Conference on Neural Networks and Computational Intelligence
EditorsO. Coastillo
Pages174-178
Number of pages5
Publication statusPublished - 2003 Dec 1
EventProceedings of the IASTED International Conference on Neural Networks and Computational Intelligence - Cancun, Mexico
Duration: 2003 May 192003 May 21

Publication series

NameProceedings of the IASTED International Conference on Neural Networks and Computational Intelligence

Other

OtherProceedings of the IASTED International Conference on Neural Networks and Computational Intelligence
CountryMexico
CityCancun
Period03-05-1903-05-21

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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    Chen, S. C., & Yang, H. T. (2003). Implementation of a virtual-instrument for non-intrusive intelligent real-time load monitoring. In O. Coastillo (Ed.), Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence (pp. 174-178). (Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence).