Data Augmentation Using BiWGAN, Feature Extraction and Classification by Hybrid 2DCNN and BiLSTM to Detect Non-Technical Losses in Smart Grids

Abstract

In this paper, we present a hybrid deep learning model that is based on a two-dimensional convolutional neural network (2D-CNN) and a bidirectional long short-term memory network (Bi-LSTM)to detect non-technical losses (NTLs) in smart meters. NTLs occur due to the fraudulent use of electricity. The global integration of smart meters has proven to be beneficial for the storage of historical electricity consumption (EC) data. The proposed methodology learns the deep insights from the historical EC data and informs power utilities about the presence of NTLs. However, the effective detection of NTLs faces the problem of class imbalance that occurs due to the rare availability of fraudulent electricity consumers. To solve this issue, an evolutionary bidirectional Wasserstein generative adversarial network (Bi-WGAN) is employed. Bi-WGAN synthesizes the most plausible fraudulent EC samples by integrating an auxiliary encoder module. Besides, the inevitable curse of high dimensional data reduces the generalization ability of classifiers. The proposed hybrid model efficiently handles the highly dynamic data by utilizing its potent feature extracting capabilities. The one-dimensional daily EC data is passed to Bi-LSTM model for capturing the non-malicious changes from consumers' profiles. Meanwhile, 2D-CNN takes 2D weekly EC data as input to extract the potential features by applying different convolutions and pooling operations. Extensive experiments are conducted on a realistic smart meters dataset to prove the effectiveness of the proposed model. The results show that the proposed model outperforms the state-of-the-art models by achieving area under the curve receiver operating characteristics of 0.97 and precision-recall area under the curve of 0.98, which make it suitable for real-world scenarios.

RFE Based Feature Selection and KNNOR Based Data Balancing for Electricity Theft Detection Using BiLSTM-LogitBoost Stacking Ensemble Model

“…The proposed BiLSTM-LogitBoost stacking ensemble model, proposed for ETD in SGs, is evaluated and discussed in this section. Some recent benchmarks, such as SVM [19], [71], logistic regression (LR) [37], decision tree (DT) [37], LSTM [21], [71], adaptive boosting (AdaBoost) [37], BiLSTM [64], LogitBoost [65], and LSTM-AdaBoost [72] are also implemented for ETD and their results are compared with the proposed model. LogitBoost with n_estimators = 25 is employed as a benchmark technique to our proposed model.…”

“…In this subsection, the proposed deep and machine learning (ML) stacking ensemble model (BiLSTM-LogitBoost) is discussed, which consists of two models BiLSTM [64] and LogitBoost [65]. The details of the proposed model are given in the below subsection.…”

“…The cell maintains and remembers the data in arbitrary time-intervals. The BiLSTM [64] model is the enhanced version of the traditional LSTM [66].…”

Non-Technical Losses Detection Using Autoencoder and Bidirectional Gated Recurrent Unit to Secure Smart Grids

“…However, data balancing is performed via SMOTE, where, the models tend to overfit. Moreover, an improved SMOTE, i.e., k-means clustering SMOTE (K-SMOTE) based data balancing and improved RF based electricity theft classification is done in [22]. The proposed method provides accurate and reliable locations for manual on-site inspection, so that NTL is reduced and the power system's stability and reliability are improved.…”

A Comprehensive Investigation into the Application of Convolutional Neural Networks (ConvNet/CNN) in Smart Grids

“…This hybrid strategy increases the energy efficiency of SG while ensuring the stability and dependability of the broken power line system [107]. To detect non-technical losses in SG, a hybrid model including bidirectional Wasserstein generative adversarial network, feature extraction and classification by Hybrid 2DCNN, and bidirectional-LSTM is utilized [108]. AdaBoost and AlexNet's hyper-parameters are modified using an artificial bee colony optimization approach to represent the detection of electricity theft [109].…”