迈向节能智能电网：通过BIWWAN的数据增强，使用混合2dcnn和bilstm的特征提取和分类

抽象的

在本文中，提出了一种新的混合深度学习方法，以检测智能电网中发生的非技术损失（NTLS），由于非法使用电网，仪表，仪表故障，未缴纳票据等。所提出的方法是基于数据-driven methods due to the sufficient availability of smart meters’ data. Therefore, a bi-directional wasserstein generative adversarial network (Bi-WGAN) is utilized to generate the synthetic theft samples for solving the class imbalance problem. The Bi-WGAN efficiently synthesizes the minority class theft samples by leveraging the capabilities of an additional encoder module. Moreover, the curse of dimensionality degrades the model’s generalization ability. Therefore, the high dimensionality issue is solved using the two dimensional convolutional neural network (2D-CNN) and bidirectional long short-term memory network (Bi-LSTM). The 2D-CNN is applied on 2D weekly data to extract the most prominent features. In 2D-CNN, the convolutional and pooling layers extract only the potential features and discard the redundant features to reduce the curse of dimensionality. This process increases the convergence speed of the model as well as reduces the computational overhead. Meanwhile, a Bi-LSTM is also used to detect the non-malicious changes in consumers’ load profiles using its strong memorization capabilities. Finally, the outcomes of both models are concatenated into a single feature map and a sigmoid activation function is applied for final NTL detection. The simulation results demonstrate that the proposed model outperforms the existing scheme in terms of mathew correlation coefficient (MCC), precision-recall (PR) and area under the curve (AUC). It achieves 3%, 5% and 4% greater MCC, PR and AUC scores, respectively as compared to the existing model.