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Detecting Non-Technical Energy Losses through Structural Periodic Patterns in AMI data

Viktor Botev (Institutionen för data- och informationsteknik, Nätverk och system (Chalmers) ) ; Magnus Almgren (Institutionen för data- och informationsteknik, Nätverk och system (Chalmers) ) ; Vincenzo Gulisano (Institutionen för data- och informationsteknik, Nätverk och system (Chalmers) ) ; Olaf Landsiedel (Institutionen för data- och informationsteknik, Nätverk och system (Chalmers) ) ; Marina Papatriantafilou (Institutionen för data- och informationsteknik, Nätverk och system (Chalmers) ) ; Joris van Rooij (Institutionen för data- och informationsteknik, Nätverk och system (Chalmers) )
BDSG/BigData: Proceedings of the Workshop on Big Data in Smart Grids at the IEEE International Conference on BigData p. 3121-3130. (2016)
[Konferensbidrag, refereegranskat]

The introduction of Advanced Metering Infrastructures in electricity networks brings new means of dealing with issues influencing financial margins and system-safety problems, thanks to the information reported continuously by smart meters. Such an issue is the detection of Non-Technical Losses (NTLs) in electric power grids. We introduce a data-driven method, called Structure&Detect, to identify possible sources of NTLs; the method is based on spectral analysis of structural periodic patterns in consumption traces, that allows for scalable processing, using features in the frequency domain. Structure&Detect uses only on consumption traces, with no need for exogenous data about customers (e.g., trust or credit history) or explicit information from domain experts. As such, it complies better with privacy concerns that may be present when processing data from different sources. Using real-world consumption traces, we show that it provides high accuracy and detection rates comparable to methods that require additional, customer-specific information. Moreover, Structure&Detect can also be used orthogonally due to its high detection rate, as a filter, providing a narrowed-down input set to methods requiring different treatment (e.g. additional data or on-site inspection) and thus make the search for NTLs more scalable. Structure&Detect also enables processing each meter trace on-the-fly, as well as in a parallel and distributed fashion. These properties make Structure& Detect suitable for online analysis that can address common big data challenges such as the need for scalable, distributed and parallel analysis close to IoT edge devices, such as smart meters.

Nyckelord: Non-Technical Losses; NTL; Power-Grid; Data-Driven; Discrete Fourier Transform; DFT

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Denna post skapades 2017-01-20. Senast ändrad 2017-07-13.
CPL Pubid: 247568


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