NTL Detection in Electric Distribution Systems Using the Maximal Overlap Discrete Wavelet-Packet Transform and Random Undersampling Boosting

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 ord...

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Bibliographic Details
Published in:IEEE transactions on power systems 2018-11, Vol.33 (6), p.7171-7180
Main Authors: Avila, Nelson Fabian, Figueroa, Gerardo, Chu, Chia-Chi
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
boosting methods
classification algorithms
Classifiers
Data mining
Discrete Wavelet Transform
Feature extraction
Machine learning
Machine learning algorithms
maximal overlap discrete wavelet packet transform
Measurement
Measuring instruments
Non-technical losses
outlier detection
Performance measurement
Signal classification
Signal processing
Time series
Wavelet packets
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Summary: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.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2018.2853162