基于非侵入式负荷监测的反窃电预警方法

为了有效检测用户是否存在窃电行为,文中对用户用电行为展开分析,提出一种基于非侵入式负荷监测的反窃电预警方法。在该方法中,首先利用负荷事件检测、特征提取以及meanshift聚类方法,获得用户各个负荷特征、类别等情况,建立负荷类别对比库以及窃电概率预测模型；其次,根据所建立的窃电行为模型,通过对窃电后的负荷投切事件、使用时长、能耗等进行概率估计,并采用贝叶斯理论对用户用电行为进行推断,实现窃电监测。最后,在实际的电能表数据上采用文中方法进行测试,文中方法能够为反窃电提供数据支持,进而为新一代智能电表反窃电应用奠定基础。

Electricity theft alarming method based on non-intrusive load monitoring

In order to achieve electricity theft detection, this paper proposes a methd through the analysis of electricity consumption behavior of users, which is based on non-instrusive load monitring. In this method, the commonly-used process is carried out, including the load detection, feature extraction and meanshift clustering, in order to obtain the features of each type load inner the home. Thus, the dataset of load is built which consists of the non-electrical features, such as the time point of load turing on and off, the length of load works, and so on. Thereby, the electricity theft detection model can be built for load cluster and probability prediction. Meanwhile, according to the electrical electricity theft detection model, the predition is performed by the information, including the load event, the length of load work and the energy consumption. The bayes theory is then introduced to infer whether its electrical consumption behavior is normal or not. Finally, the experiments are carried out by using the real information from the smart meter. The results show that the proposed method can provide the basis and support for the electriity theft detection.