电测与仪表Issue(6)：64-69,6.
基于SVM的AMI环境下用电异常检测研究
SVM Based Energy Consumption Abnormality Detection in AMI System
简富俊 1曹敏 2王磊 3孙中伟 3张建伟 4王洪亮3
作者信息
- 1. 华北电力大学 云南电网公司研究生工作站，昆明650217
- 2. 华北电力大学电气与电子工程学院，北京102206
- 3. 云南电力试验研究院 集团 有限公司电力研究院，昆明650217
- 4. 华北电力大学电气与电子工程学院，北京102206
- 折叠
摘要
Abstract
Electrical power system is facing serious security problems due to Advanced Metering Infrastructure(AMI) system which introduces a lot of new technologies in traditional electrical power system. As a result of smart grid, the contradiction of openness and security is increased which will give rise to the increase of electricity fraud. How to detect electricity fraud has become a new issue of grid informatization. On the basis of the AMI’s architecture, the paper adopts One-class SVM technique to detect the abnormal behavior of electricity users which works at a non-supervision Machine learning mode and can get a high accuracy of detection in small sample or unbalanced classification environment. In order to reduce the false alarm rate of the system, the system uses filtering method to handle the test results of SVM classification processing. System can improve the efficiency of electrical inspection and reduce the Non-Technical Losses(NTL) of power system. The paper also gives an implementation of the system which verifies execution efficiency and detection efficiency of the algorithm by real example.关键词
高级测量体系/One-class SVM/用电异常/机器学习/非技术性损失Key words
AMI/One-class SVM/energy consumption abnormality/machine learning/NTL分类
信息技术与安全科学引用本文复制引用
简富俊,曹敏,王磊,孙中伟,张建伟,王洪亮..基于SVM的AMI环境下用电异常检测研究[J].电测与仪表,2014,(6):64-69,6.
