Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine
Energy signature analysis of power appliance is the core of nonintrusive load monitoring (NILM) where the detailed data of the appliances used in houses are obtained by analyzing changes in the voltage and current. This paper focuses on developing an automatic power load event detection and applianc...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2012/742461 |
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| _version_ | 1832554843389034496 |
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| author | Lei Jiang Jiaming Li Suhuai Luo Sam West Glenn Platt |
| author_facet | Lei Jiang Jiaming Li Suhuai Luo Sam West Glenn Platt |
| author_sort | Lei Jiang |
| collection | DOAJ |
| description | Energy signature analysis of power appliance is the core of nonintrusive load monitoring (NILM) where the detailed data of the appliances used in houses are obtained by analyzing changes in the voltage and current. This paper focuses on developing an automatic power load event detection and appliance classification based on machine learning. In power load event detection, the paper presents a new transient detection algorithm. By turn-on and turn-off transient waveforms analysis, it can accurately detect the edge point when a device is switched on or switched off. The proposed load classification technique can identify different power appliances with improved recognition accuracy and computational speed. The load classification method is composed of two processes including frequency feature analysis and support vector machine. The experimental results indicated that the incorporation of the new edge detection and turn-on and turn-off transient signature analysis into NILM revealed more information than traditional NILM methods. The load classification method has achieved more than ninety percent recognition rate. |
| format | Article |
| id | doaj-art-6e91ffaecc0c4137aeda34dcb608d48e |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-6e91ffaecc0c4137aeda34dcb608d48e2025-02-03T05:50:27ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322012-01-01201210.1155/2012/742461742461Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector MachineLei Jiang0Jiaming Li1Suhuai Luo2Sam West3Glenn Platt4School of DCIT, University of Newcastle, Callaghan, NSW 2308, AustraliaICT Centre, Commonwealth Scientific and Industrial Research Organization, Clayton South, VIC 3169, AustraliaSchool of DCIT, University of Newcastle, Callaghan, NSW 2308, AustraliaEnergy Technology Division, Commonwealth Scientific and Industrial Research Organization, Clayton South, VIC 3169, AustraliaEnergy Technology Division, Commonwealth Scientific and Industrial Research Organization, Clayton South, VIC 3169, AustraliaEnergy signature analysis of power appliance is the core of nonintrusive load monitoring (NILM) where the detailed data of the appliances used in houses are obtained by analyzing changes in the voltage and current. This paper focuses on developing an automatic power load event detection and appliance classification based on machine learning. In power load event detection, the paper presents a new transient detection algorithm. By turn-on and turn-off transient waveforms analysis, it can accurately detect the edge point when a device is switched on or switched off. The proposed load classification technique can identify different power appliances with improved recognition accuracy and computational speed. The load classification method is composed of two processes including frequency feature analysis and support vector machine. The experimental results indicated that the incorporation of the new edge detection and turn-on and turn-off transient signature analysis into NILM revealed more information than traditional NILM methods. The load classification method has achieved more than ninety percent recognition rate.http://dx.doi.org/10.1155/2012/742461 |
| spellingShingle | Lei Jiang Jiaming Li Suhuai Luo Sam West Glenn Platt Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine Applied Computational Intelligence and Soft Computing |
| title | Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine |
| title_full | Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine |
| title_fullStr | Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine |
| title_full_unstemmed | Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine |
| title_short | Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine |
| title_sort | power load event detection and classification based on edge symbol analysis and support vector machine |
| url | http://dx.doi.org/10.1155/2012/742461 |
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