基于小波和长短期记忆混合神经网络的电力用户异常用电模式检测

为了约束输配电系统中存在的异常用电行为,文中提出一种基于小波和长短期记忆混合神经网络的电力用户异常用电模式检测模型。提出异常用电模拟算法用于生成异常用电数据序列;利用长短期记忆网络构建特征提取网络,从用电数据中提取出不同的序列特征;以小波神经网络为核心构建模式映射网络,实现序列特征到用电模式的映射,完成异常用电模式检测。通过CER Smart Metering Project数据集测试,文章提出的异常用电检测模型与传统网络模型相比,具有更高的检出率、更低的误检率和更高的贝叶斯检出率。

Anomaly detection for power consumption patterns based on Wavelet and LSTM hybrid neural network

A hybrid neural network model based on Wavelet and LSTM is proposed to restrict the anomaly power consumption behavior in transmission and distribution system. Firstly, an abnormal power consumption simulation algorithm is proposed to generate abnormal power consumption data sequence. Next, the feature extracting network which is constructed by using LSTM network extracts different sequence features from power consumption data. Lastly, the mode mapping network with wavelet neural network as the core, uses the extracted different sequence features to detect the anomalous electrical power consumptions. Case studies on CER Smart Metering Project datasets have demonstrated that the proposed model has higher detection rate, lower false positive rate and higher Bayesian detection rate, compared with conventional networks.