The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Power signature analysis
Abstract
Nonintrusive load monitoring (NILM) can determine operating schedule of electrical loads in a target system from measurements made at a centralized location, such as the electric utility service entry. NILM is an ideal platform for extracting useful information about any system that uses electromechanical devices. It has a low installation cost and high reliability because it uses a bare minimum of sensors. It is possible to use modem state and parameter estimation algorithms to verify remotely the "health" of electromechanical loads by using NILM to analyze measured waveforms associated with the operation of individual loads. NILM can also monitor the operation of the electrical distribution system itself, identifying situations where two or more otherwise healthy loads interfere with each other's operation through voltage waveform distortion or power quality problems. Strategies for nonintrusive monitoring have developed over the last 20 years. Advances in computing technology make a new wealth of computational tools useful in practical, field-based NILM systems. This article reviews techniques for high-performance nonintrusive load and diagnostic monitoring and illustrates key points with results from field tests.
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Cites
Nonintrusive appliance load monitoring
Hart, G. W. (1992) · Proceedings of the IEEE · doi:10.1109/5.192069 · Cited by 3,165
Norford, L. K., & Leeb, S. B. (1996) · Energy and Buildings · doi:10.1016/0378-7788(95)00958-2 · Cited by 323
Self-citationTransient event detection in spectral envelope estimates for nonintrusive load monitoring
Leeb, S. B., Shaw, S. R., & Kirtley, J. L. (1995) · IEEE Transactions on Power Delivery · doi:10.1109/61.400897 · Cited by 311
Self-citationNonintrusive monitoring of electric loads
Drenker, S., & Kader, A. (1999) · IEEE Computer Applications in Power · doi:10.1109/67.795138 · Cited by 187
Instrumentation for High Performance Nonintrusive Electrical Load Monitoring
Shaw, S. R., Abler, C. B., Lepard, R. F., Luo, D., Leeb, S. B., & Norford, L. K. (1998) · Journal of Solar Energy Engineering · doi:10.1115/1.2888073 · Cited by 44
Self-citation
Cited by
Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey
Zoha, A., Gluhak, A., Imran, M., & Rajasegarar, S. (2012) · Sensors · doi:10.3390/s121216838 · Cited by 954
Is disaggregation the holy grail of energy efficiency? The case of electricity
Carrie Armel, K., Gupta, A., Shrimali, G., & Albert, A. (2013) · Energy Policy · doi:10.1016/j.enpol.2012.08.062 · Cited by 595
Differential Privacy Techniques for Cyber Physical Systems: A Survey
Hassan, M. U., Rehmani, M. H., & Chen, J. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2944748 · Cited by 564
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
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
- Laughman, C., Lee, K., Cox, R., Shaw, S., Leeb, S., Norford, L., & Armstrong, P.
- Venue
- IEEE Power and Energy Magazine
- Published
- 2003-03
- Volume
- 1
- Issue
- 2
- Pages
- 56-63
- ISSN
- 1540-7977 (ISSN-L)
- 1558-4216
- OpenAlex
- W2010314082
- Cited by
- 572
- References
- 5
- FWCI
- 4.69
- Type
- journal article
- Language
- en
- Open access
- closed
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