基于三元组孪生网络的窃电检测算法

Electricity Theft Detection Algorithm Based on Triplet Network

窃电数据量的缺乏对窃电检测算法的辨识准确度造成了极大影响,因此该文提出在小样本条件下基于三元组孪生网络的窃电检测方法。利用格拉姆角场(gramianangular field,GAF)实现用电序列图像化,再使用三元组孪生网络提取用户用电数据中的特征向量,基于欧氏距离进行特征向量的相似度比对,实现窃电检测。由于三元组孪生网络不仅对训练样本本身的特征进行提取,还对同类样本间的相似性与非同类样本间的差异性进行了学习,提高了特征向量的聚类效果,拥有较高的轮廓系数(silhouette score)。算例结果验证了所提算法在小样本情况下的准确性和优越性。

The lack of electricity theft data has a great impact on the identification accuracy of the electricity theft detection algorithm.Therefore,this paper proposed an electricity theft detection method based on the triplet network for few-shot learning.Firstly,the gramian angular field was used to convert the electricity consumption sequence to images.Secondly,the triplet network was used to extract the feature vector of the user’s electricity consumption data and measure their similarity based on the Euclidean distance.Finally,electricity theft detection was realized.The triplet network not only extracts the feature of the training samples,but also learns the similarity between the samples of the same class and the difference between the samples of different classes,thus improving the clustering effect of the feature vectors and showing a higher silhouette score.The results of calculation examples verify the accuracy and superiority of the proposed algorithm for few-shot learning.