Electricity Theft Detection in AMI Based on Clustering and Local Outlier Factor
Yanlin Peng,
Yining Yang,
Yuejie Xu,
Yang Xue,
Runan Song,
Jinping Kang,
Haisen Zhao
Affiliations
Yanlin Peng
State Key Laboratory of Alternate Electricity Power System With Renewable Energy Sources, North China Electric Power University, Changping, Beijing, China
Yining Yang
China Electric Power Research Institute, Haidian, Beijing, China
Yuejie Xu
State Key Laboratory of Alternate Electricity Power System With Renewable Energy Sources, North China Electric Power University, Changping, Beijing, China
Yang Xue
China Electric Power Research Institute, Haidian, Beijing, China
Runan Song
China Electric Power Research Institute, Haidian, Beijing, China
State Key Laboratory of Alternate Electricity Power System With Renewable Energy Sources, North China Electric Power University, Changping, Beijing, China
State Key Laboratory of Alternate Electricity Power System With Renewable Energy Sources, North China Electric Power University, Changping, Beijing, China
As one of the key components of smart grid, advanced metering infrastructure (AMI) provides an immense number of data, making technologies such as data mining more suitable for electricity theft detection. However, due to the unbalanced dataset in the field of electricity theft, many AI-based methods such as deep learning are prone to under-fitting. To evade this problem and to detect as many types of theft attacks as possible, an outlier detection method based on clustering and local outlier factor (LOF) is proposed in this study. We firstly analyze the load profiles with $k$ -means. Then, customers whose load profiles are far from the cluster centers are selected as outlier candidates. After that, the LOF is utilized to calculate the anomaly degrees of outlier candidates. Corresponding framework for practical application is then designed. Finally, numerical experiments based on realistic dataset show the good performance of the presented method.
