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鲁棒混合深度学习模型


针对智能电网中的窃电检测，本文介绍了一种混合深度学习模型。该模型解决了现有模型的类不平衡问题、维数灾难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。一维电力消耗 (EC) 数据被输入 GRU 以记住电力消耗的周期性模式。而 GoogLeNet 模型用于从二维每周堆积的 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）来解决类别不平衡问题。 TLSGAN 使用无监督和监督损失函数来生成假盗窃样本，这些样本与现实世界的盗窃样本高度相似。标准生成对抗网络仅更新决策边界错误一侧可用的那些点的权重。而 TLSGAN 甚至修改了决策边界正确一侧可用的点的权重，以防止模型出现梯度消失问题。此外，利用 dropout 和批量归一化层来提高模型的收敛速度和泛化能力。所提出的模型与不同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-长期短期记忆网络以及广深卷积神经网络。它的曲线下精确召回面积和曲线下接收者操作特征面积分别达到 96% 和 97%，优于所有分类器。