Electricity theft detection via time series analysis of state estimation measurement residuals

Conference: NEIS 2020 - Conference on Sustainable Energy Supply and Energy Storage Systems
09/14/2020 - 09/15/2020 at Hamburg, Deutschland

Proceedings: NEIS 2020

Pages: 6Language: englishTyp: PDF

Ban, Hemanta; Pirak, Chaiyod (The Sirindhorn International Thai-German Graduate School of Engineering, King Mongkut’s University of Technology North Bangkok, Thailand)
Pau, Marco; Ponci, Ferdinanda; Monti, Antonello (RWTH Aachen University, Aachen, Germany)

In the smart grid scenario, monitoring and automation of the distribution system are required up to the low voltage level of the electric system. An option to monitor the low voltage grids is to use measurements collected from the end-user smart meters. Even in this case, a still open issue is the detection and identification of possible bad data, which cannot be easily achieved with traditional methods due to the low measurement redundancy. At low voltage level, a particular case of bad data is given by possible electricity thefts, which clearly need to be detected both to prevent revenue losses and to avoid an incorrect operation of the monitoring system. This paper deals with this topic and presents an approach to detect and identify smart meter bad data associated to electricity thefts. The proposed approach allows identifying the source of the bad data via the time series analysis of the measurement residuals obtained during the low voltage grid state estimation process. The investigations and tests carried out in this work show that the proposed method can be an effective way to discover electricity thefts in the grid also in cases where traditional methods fail.