Abstract
In smart grids, electricity theft is the most significant challenge. It cannot be identified easily since existing methods are dependent on specific devices. Also, the methods lack in extracting meaningful information from high-dimensional electricity consumption data and increase the false positive rate that limit their performance. Moreover, imbalanced data is a hurdle in accurate electricity theft detection (ETD) using data driven methods. To address this problem, sampling techniques are used in the literature. However, the traditional sampling techniques generate insufficient and unrealistic data that degrade the ETD rate. In this work, two novel ETD models are developed. A hybrid sampling approach, i.e., synthetic minority oversampling technique with edited nearest neighbor, is introduced in the first model. Furthermore, AlexNet is used for dimensionality reduction and extracting useful information from electricity consumption data. Finally, a light gradient boosting model is used for classification purpose. In the second model, conditional wasserstein generative adversarial network with gradient penalty is used to capture the real distribution of the electricity consumption data. It is constructed by adding auxiliary provisional information to generate more realistic data for the minority class. Moreover, GoogLeNet architecture is employed to reduce the dataset's dimensionality. Finally, adaptive boosting is used for classification of honest and suspicious consumers. Both models are trained and tested using real power consumption data provided by state grid corporation of China. The proposed models' performance is evaluated using different performance metrics like precision, recall, accuracy, F1-score, etc. The simulation results prove that the proposed models outperform the existing techniques, such as support vector machine, extreme gradient boosting, convolution neural network, etc., in terms of efficient ETD.
Highlights
Electricity has become inevitable for human life as almost all daily activities are dependent on it, such as communication, transportation, domestic appliances, heating and cooling systems, etc
SMOTEENN is used to balance the dataset that prevents a classifier from misclassification
AlexNet is used for feature extraction, and light gradient boosting (LGB) is used to differentiate legal consumers from illegal consumers
Summary
Electricity has become inevitable for human life as almost all daily activities are dependent on it, such as communication, transportation, domestic appliances, heating and cooling systems, etc. When ML algorithms are trained to detect the electricity theft in the power systems, most of them ignore the data distribution between both classes and become biased towards the majority class. As a result, these algorithms provide a high false DR [22]. In some studies, sampling techniques, such as undersampling, and oversampling are used to overcome the data imbalance problem The conventional GAN model stucks in training instability, vanishing and exploding gradient problems; CWGAN-GP is used in this study to deal with the issues of conventional GAN and efficiently balance the data by synthesizing the minority class samples.
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