Abstract
We propose a method for power theft detection based on predictive models for technical losses in electrical distribution networks estimated entirely from data collected by smart meters in smart grids. Although the data sampling rate of smart meters is not sufficiently high to detect power theft with complete certainty, detection is still possible in a statistical decision theory sense, based on statistical models estimated from collected data sets. Even without detailed knowledge of the exact topology of the distribution network, it is possible to estimate a statistical model of the technical losses that allows indirect estimation of the non-technical losses (power theft) with high accuracy.
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Nikovski, D.N. et al. (2013). Smart Meter Data Analysis for Power Theft Detection. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2013. Lecture Notes in Computer Science(), vol 7988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39712-7_29
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DOI: https://doi.org/10.1007/978-3-642-39712-7_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39711-0
Online ISBN: 978-3-642-39712-7
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