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Anomaly Detection Using Power Signature of Consumer Electrical DevicesCERNAZANU-GLAVAN, C. , MARCU, M.
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feature extraction, pattern matching, signal analysis, signal processing
power(9), smart(8), signatures(5), energy(5), grid(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2015-02-28
Volume 15, Issue 1, Year 2015, On page(s): 89 - 94
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.01013
Web of Science Accession Number: 000352158600013
SCOPUS ID: 84924760263
The use of the smart grid for developing intelligent applications is a current trend of great importance. One advantage lies in the possibility of direct monitoring of all devices connected to the electrical network in order to prevent possible malfunctions. Therefore, this paper proposes a method for an automatic detection of the malfunctioning of low-intelligence consumer electrical devices. Malfunctioning means any deviation of a household device from its normal operating schedule. The method is based on a comparison technique, consisting in the correlation between the current power signature of a device and an ideal signature (the standard signature provided by the manufacturer). The first step of this method is to achieve a simplified form of power signature which keeps all the original features. Further, the signal is segmented based on the data provided by an event detection algorithm (values of the first derivatives) and each resulting component is approximated using a regression function. The final step consists of an analysis based on the correlation between the computed regression coefficients and the coefficients of the standard signal. Following this analysis all the differences are classified as a malfunctioning of the analyzed device.
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 Detection of anomalies in smart meter data: A density-based approach, Fathnia, Froogh, Fathnia, Farid, Javidi, D. B. Mohammad Hossein, 2017 Smart Grid Conference (SGC), ISBN 978-1-5386-4279-5, 2017.
Digital Object Identifier: 10.1109/SGC.2017.8308852 [CrossRef]
 Component level energy accounting and fault detection on electrical devices using power signatures, Marcu, Marius, Darie, Marius, Cernazanu-Glavan, Cosmin, 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), ISBN 978-1-5090-3596-0, 2017.
Digital Object Identifier: 10.1109/I2MTC.2017.7969888 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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