Identification of electrical appliances using non-intrusive magnetic field and Probabilistic Neural Network (PNN)

Nurul Aishah Mohd Rosdi, Farah Hani Nordin, Agileswari K. Ramasamy

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

4 Citations (Scopus)

Abstract

The electricity waste is severe especially in large organizational buildings where the use of air conditioners, fridges and electrical motors are rampant. Due to lack of energy saving consciousness, users may not switch off this equipment after use. Thus, it would be an advantage if there exist a system that will be able to identify the appliances from one place without the residence having to go and check the state of the appliance or without having to place various sensors intrusively. Since most electrical appliances emit magnetic fields, the paper proposes to use non-intrusive magnetic field signature waveforms to identify the type of appliance used. The magnetic field emitted by table fan, blender and hairdryer are chosen for this purpose. The magnetic field from these three appliances are collected from four different measurement distances i.e. (i) 0cm (ii) 10cm (iii) 30cm and (iv) 60cm. The features of the magnetic field are then extracted and trained offline using the Probabilistic Neural Network (PNN). Once trained, the PNN shows that it is able to successfully identify the appliances regardless of the measurement distance.

Original languageEnglish
Title of host publicationConference Proceeding - 2014 IEEE International Conference on Power and Energy, PECon 2014
EditorsMohd Izhwan Muhamad, Mohammad Nawawi Seroji
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages47-52
Number of pages6
ISBN (Electronic)9781479972975
DOIs
Publication statusPublished - 17 Mar 2014
Event2014 IEEE International Conference on Power and Energy, PECon 2014 - Kuching, Sarawak, Malaysia
Duration: 01 Dec 201403 Dec 2014

Publication series

NameConference Proceeding - 2014 IEEE International Conference on Power and Energy, PECon 2014

Other

Other2014 IEEE International Conference on Power and Energy, PECon 2014
CountryMalaysia
CityKuching, Sarawak
Period01/12/1403/12/14

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All Science Journal Classification (ASJC) codes

  • Fuel Technology
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Cite this

Mohd Rosdi, N. A., Nordin, F. H., & Ramasamy, A. K. (2014). Identification of electrical appliances using non-intrusive magnetic field and Probabilistic Neural Network (PNN). In M. I. Muhamad, & M. N. Seroji (Eds.), Conference Proceeding - 2014 IEEE International Conference on Power and Energy, PECon 2014 (pp. 47-52). [7062412] (Conference Proceeding - 2014 IEEE International Conference on Power and Energy, PECon 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PECON.2014.7062412