一种基于CAEs-LSTM融合模型的窃电检测方法

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

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。

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.