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Using appliance signatures for monitoring residential loads at meter panel level
Abstract
The author presents an initial approach to identifying electrical household appliances strictly by analyzing variations in current-voltage signals at the meter panel level. The study shows that local measurement of variations in active and reactive power is sufficient in most cases to identify an appliance. This measurement must be synchronized with the events. This necessitates a numerical measurement device which can monitor the signal and isolate the times when variations occur. In a second stage, a sorting algorithm identifies the appliance that has operated. An attempt is made to show the feasibility of this device. Test findings secured on site using a test prototype are presented.>
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Citations
Cited by
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Neural-Network-Based Signature Recognition for Harmonic Source Identification
Srinivasan, D., Ng, W. S., & Liew, A. C. (2006) · IEEE Transactions on Power Delivery · doi:10.1109/tpwrd.2005.852370 · Cited by 453
Big data analytics in smart grids: a review
Zhang, Y., Huang, T., & Bompard, E. F. (2018) · Energy Informatics · doi:10.1186/s42162-018-0007-5 · Cited by 382
A Novel Method to Construct Taxonomy Electrical Appliances Based on Load Signaturesof
Lam, H., Fung, G., & Lee, W. (2007) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2007.381742 · Cited by 324
Metadata
- Authors
- Sultanem, F.
- Venue
- IEEE Transactions on Power Delivery
- Published
- 1991
- Volume
- 6
- Issue
- 4
- Pages
- 1380-1385
- ISSN
- 0885-8977 (ISSN-L)
- OpenAlex
- W2062707341
- Cited by
- 209
- References
- 0
- FWCI
- 0.46
- Type
- journal article
- Language
- en
- Open access
- closed
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- Sultanem, F. (1991). Using appliance signatures for monitoring residential loads at meter panel level. IEEE Transactions on Power Delivery. https://doi.org/10.1109/61.97667
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.