Detecting Non-Technical Energy Losses through Structural Periodic Patterns in AMI data
Paper in proceedings, 2016

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.

Non-Technical Losses

Discrete Fourier Transform

NTL

DFT

Data-Driven

Power-Grid

Author

Viktor Botev

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Magnus Almgren

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Vincenzo Massimiliano Gulisano

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Olaf Landsiedel

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Marina Papatriantafilou

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

Joris Van Rooij

Chalmers, Computer Science and Engineering (Chalmers), Networks and Systems (Chalmers)

BDSG/BigData: Proceedings of the Workshop on Big Data in Smart Grids at the IEEE International Conference on BigData

3121-3130

Areas of Advance

Information and Communication Technology

Energy

Subject Categories

Computer Science

DOI

10.1109/BigData.2016.7840967

More information

Latest update

1/18/2019