NTL Detection in electric distribution systems using the maximal overlap discrete wavelet-packet transform and random undersampling boosting

Nelson Fabian Avila, Gerardo Figueroa, Chia Chi Chu*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

106 Scopus citations

Abstract

The illegal use of electricity, defective meters, and a malfunctioning infrastructure are major causes of Non-technical losses (NTLs) in electric distribution systems. Although the use of supervised machine learning techniques to detect NTLs has been widely studied, further research is needed in order to address some significant challenges. (i) Given that fraudulent consumers remarkably outnumber non-fraudulent ones, the imbalanced nature of the dataset can have a major negative impact on the performance of supervised machine learning methods. (ii) Given the large number of dimensions present in the time series data used for training and testing classifiers, advanced signal processing techniques are required in order to extract the most relevant information. (iii) The effectiveness of classifiers must be evaluated using meaningful performance measures for imbalanced data. This paper proposes a framework that addresses the three previous challenges. The core of the proposed framework is the application of the maximal overlap discrete wavelet-packet transform (MODWPT) for feature extraction from time series data and the random undersampling boosting (RUSBoost) algorithm for NTL detection. Moreover, our framework is evaluated using an extensive list of performance metrics. Experiments show that the MODWPT combined with the RUSBoost algorithm can significantly improve the quality of NTL predictions.

Original languageEnglish
Article number8404135
Pages (from-to)7171-7180
Number of pages10
JournalIEEE Transactions on Power Systems
Volume33
Issue number6
DOIs
StatePublished - 11 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Non-technical losses
  • boosting methods
  • classification algorithms
  • maximal overlap discrete wavelet packet transform
  • outlier detection

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