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.

中文翻译：

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用于检测非技术损失以保护智能电网的稳健混合深度学习模型

针对智能电网中的窃电检测，本文引入了混合深度学习模型。该模型解决了现有模型的类别不平衡问题、维数诅咒和盗窃检测率低等各种问题。该模型集成了 GoogLeNet 和门控循环单元 (GRU) 的优点。将一维电力消耗 (EC) 数据输入 GRU 以记住电力消耗的周期性模式。而利用 GoogLeNet 模型从二维每周堆叠 EC 数据中提取潜在特征。此外，提出了时间最小二乘生成对抗网络（TLSGAN）来解决类不平衡问题。TLSGAN 使用无监督和有监督的损失函数来生成假盗窃样本，与现实世界的盗窃样本高度相似。标准的生成对抗网络只更新那些在决策边界错误一侧可用的点的权重。而 TLSGAN 甚至修改了那些在决策边界正确一侧可用的点的权重，以防止模型消失梯度问题。此外，利用dropout和batch normalization层来提高模型的收敛速度和泛化能力。将所提出的模型与不同的最先进的分类器进行了比较，包括多层感知器 (MLP)、支持向量机、朴素贝叶斯、逻辑回归、MLP-长短期记忆网络和广而深的卷积神经网络。