一种基于CAEs-LSTM融合模型的窃电检测方法

为解决现有的智能电网电力盗窃行为检测方法中准确性不足、检测效率低下等问题，提出了一种由卷积自编码器网络(convolutional auto-encoders,?CAEs)和长短期记忆网络(long short term memory,?LSTM)相结合的CAEs-LSTM检测模型。该模型通过分析数据集的特点对电力数据进行二维转换，设计卷积自编码器结构，采用池化、下采样和上采样重构电力数据的二维空间特征，加入高斯噪声提高模型鲁棒性，并构建长短期记忆网络以学习全局时序特征。最后，对提取的时空特征进行融合从而检测能源窃贼，并进行了参数调优。在由国家电网公布的真实数据集上，通过将CAEs-LSTM模型与支持向量机、LSTM以及宽深度卷积神经网络进行对比，CAEs-LSTM模型的平均精度均值和曲线下面积值均最优。仿真实验表明，基于CAEs-LSTM模型的窃电检测方法具有更高的窃电检测效率和精度。

Electricity theft detection method based on a CAEs-LSTM fusion model

To solve the problems of insufficient accuracy and low detection efficiency in existing detection methods of electricity theft in smart grids, a CAEs-LSTM detection model combining convolutional auto-encoders (CAEs) with long short-term memory networks (LSTM) is proposed. The model conducts two-dimensional conversion to power data, designs the encoder structure by analyzing the characteristics of data set, and reconstructs the two-dimensional space characteristics of the electricity data using pooling layers, down and up sampling layers. It adds Gaussian noise to improve its robustness, and builds long short-term memory networks to learn the global characteristics. Finally, spatial-temporal characteristics are fused to detect energy thieves, and parameter tuning is performed. Based on the public available real data set of the State Grid, the CAEs-LSTM model is optimal in the value of mean average prediction and area under curve, by comparing the CAEs-LSTM model with support vector machines, the LSTM model, and wide and deep convolutional neural networks. Simulation experiments show that the theft detection method based on the CAEs-LSTM model has higher detection efficiency and accuracy. This work is supported by the National Natural Science Foundation of China (No. 61873277).