Nontechnical Losses Detection Through Coordinated BiWGAN and SVDD

Hu, Tianyu; Guo, Qinglai; Sun, Hongbin; Huang, Tianen; Lan, Jian

doi:10.1109/tnnls.2020.2994116

Cited by 27 publications

(22 citation statements)

References 41 publications

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“…The authors remove highly correlated and overlapped features, which helps to improve DR and decrease FPR. In [11], [20], [21], the authors work on feature engineering and describe why the curse of dimensionality affects the performance of ML models. In [9], the authors generate new features from the smart meter and auxiliary data.…”

Section: Related Workmentioning

confidence: 99%

“…In [11], maximal overlap discrete wavelet packet transform is leveraged to extract the optimal features. In [21], the authors implement a bidirectional Wasserstein GAN to extract the optimal features from time series data. In [9], the authors pass a combination of newly created features in different conventional ML classifiers and compare their results.…”

Section: Related Workmentioning

confidence: 99%

“…In [2], [3], [10], SMOTE and ROS are leveraged to solve the uneven distribution of samples. In [7], [8], [15], [17], [20], [21], [27], [28], the authors do not tackle the above mentioned problems. In [25], the authors use SMOTE and near miss method to handle class imbalance problem.…”

Section: Related Workmentioning

confidence: 99%

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A Robust Hybrid Deep Learning Model for Detection of Non-Technical Losses to Secure Smart Grids

et al. 2021

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For dealing with the electricity theft detection in the smart grids, this article introduces a hybrid deep learning model. The model tackles various issues such as class imbalance problem, curse of dimensionality and low theft detection rate of the existing models. The model integrates the benefits of both GoogLeNet and gated recurrent unit (GRU). The one dimensional electricity consumption (EC) data is fed into GRU to remember the periodic patterns of electricity consumption. Whereas, GoogLeNet model is leveraged to extract the latent features from the two dimensional weekly stacked EC data. Furthermore, the time least square generative adversarial network (TLSGAN) is proposed to solve the class imbalance problem. The TLSGAN uses unsupervised and supervised loss functions to generate fake theft samples, which have high resemblance with real world theft samples. The standard generative adversarial network only updates the weights of those points that are available at the wrong side of the decision boundary. Whereas, TLSGAN even modifies the weights of those points that are available at the correct side of decision boundary that prevent the model from vanishing gradient problem. Moreover, dropout and batch normalization layers are utilized to enhance model's convergence speed and generalization ability. The proposed model is compared with different state-of-the-art classifiers including multilayer perceptron (MLP), support vector machine, naive bayes, logistic regression, MLP-long short term memory network and wide and deep convolutional neural network. It outperforms all classifiers by achieving 96% and 97% precision-recall area under the curve and receiver operating characteristics area under the curve, respectively.INDEX TERMS Electricity theft detection, gated recurrent unit, GoogLeNet, non-technical losses, smart grids, SGCC.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Robust Hybrid Deep Learning Model for Detection of Non-Technical Losses to Secure Smart Grids

et al. 2021

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“…The former creates synthetic data similar to original data by selecting random input samples from the dataset. The latter discriminates between fake and original data [52]. During GAN's process, both generator and discriminator modules are trained until discriminator is failed half of the time to distinguish between fake and original samples, which means that generator is successful in creating fake samples.…”

Section: ) Classificationmentioning

confidence: 99%

Using GANCNN and ERNET for Detection of Non Technical Losses to Secure Smart Grids

et al. 2021

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In this paper, two supervised learning models based solutions are proposed for Electricity Theft Detection (ETD). In the first solution, Adaptive Synthetic Edited Nearest Neighbor (ADASYNENN) is used to solve class imbalanced problem. For feature extraction, Locally Linear Embedding (LLE) technique is utilized. Moreover, Self-Attention Generative Adversarial Network (SAGAN) is used in combination with Convolutional Neural Network (CNN) for the classification of electricity consumers. In the second solution, Synthetic Minority Oversampling Technique Edited Nearest Neighbor (SMOTEENN) is proposed. Moreover, a novel classification model, named as ERNET, which is based on EfficientNet, Residual Network (ResNet) and Gated Recurrent Unit (GRU), is used to detect Non-Technical Losses (NTLs). We also used a Sparse Auto Encoder (SAE) for effective feature extraction that makes the classification more robust and easy. Furthermore, a robust Root Mean Square Propagation (RMSProp) optimizer is used to improve the learning rate of the model. To validate the proposed models, simulations are performed using different performance metrics, such as precision, recall, F1-score, Area Under the Curve (AUC), FPR and Root Mean Square Error (RMSE). All simulations are performed using State Grid Corporation of China (SGCC) dataset. The proposed models are compared with benchmark models, such as SAGAN, Wide and Deep Convolutional Neural Network (WDCNN), CNN and Long Short Term Memory (LSTM). The simulation results prove that the proposed models outperform the existing models in terms of the aforementioned performance metrics.

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“…GAN has gained much attention for anomaly detection [32]. It is proficient at learning the distribution of provided data to generate synthesized data close to the real data [33], [34]. CWGAN-GP is trained using the labeled electricity consumption data.…”

mentioning

confidence: 99%

Towards Sustainable Energy Efficiency With Intelligent Electricity Theft Detection in Smart Grids Emphasising Enhanced Neural Networks

et al. 2021

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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.

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Nontechnical Losses Detection Through Coordinated BiWGAN and SVDD

Cited by 27 publications

References 41 publications

A Robust Hybrid Deep Learning Model for Detection of Non-Technical Losses to Secure Smart Grids

A Robust Hybrid Deep Learning Model for Detection of Non-Technical Losses to Secure Smart Grids

Using GANCNN and ERNET for Detection of Non Technical Losses to Secure Smart Grids

Towards Sustainable Energy Efficiency With Intelligent Electricity Theft Detection in Smart Grids Emphasising Enhanced Neural Networks

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