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

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dc.citedby6
dc.contributor.authorMohd Rosdi N.A.en_US
dc.contributor.authorNordin F.H.en_US
dc.contributor.authorRamasamy A.K.en_US
dc.contributor.authorid56602822900en_US
dc.contributor.authorid25930510500en_US
dc.contributor.authorid16023154400en_US
dc.date.accessioned2023-05-16T02:46:01Z
dc.date.available2023-05-16T02:46:01Z
dc.date.issued2014
dc.description.abstractThe 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. © 2014 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo7062412
dc.identifier.doi10.1109/PECON.2014.7062412
dc.identifier.epage52
dc.identifier.scopus2-s2.0-84946690076
dc.identifier.spage47
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84946690076&doi=10.1109%2fPECON.2014.7062412&partnerID=40&md5=f1888a1c2115fdfc5e4ffae9644cf582
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/21911
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleConference Proceeding - 2014 IEEE International Conference on Power and Energy, PECon 2014
dc.titleIdentification of electrical appliances using non-intrusive magnetic field and Probabilistic Neural Network (PNN)en_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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