Bai Wei, Xiong Lan, Liao Yubei, Tan Zhengyang, Wang Jingang, Zhang Zhanlong
College of Electrical Engineering, Chongqing University, Chongqing 400044, China.
Cincinnati Joint Co-Op Institute, Chongqing University, Chongqing 400044, China.
Sensors (Basel). 2024 Sep 19;24(18):6057. doi: 10.3390/s24186057.
The advent of smart grids has facilitated data-driven methods for detecting electricity theft, with a preponderance of research efforts focused on user electricity consumption data. The multi-dimensional power state data captured by Advanced Metering Infrastructure (AMI) encompasses rich information, the exploration of which, in relation to electricity usage behaviors, holds immense potential for enhancing the efficiency of theft detection. In light of this, we propose the Catch22-Conv-Transformer method, a multi-dimensional feature extraction-based approach tailored for the detection of anomalous electricity usage patterns. This methodology leverages both the Catch22 feature set and complementary features to extract sequential features, subsequently employing convolutional networks and the Transformer architecture to discern various types of theft behaviors. Our evaluation, utilizing a three-phase power state and daily electricity usage data provided by the State Grid Corporation of China, demonstrates the efficacy of our approach in accurately identifying theft modalities, including evasion, tampering, and data manipulation.
智能电网的出现推动了基于数据驱动的窃电检测方法的发展，大量研究工作集中在用户用电数据上。高级计量基础设施（AMI）捕获的多维度电力状态数据包含丰富信息，探索这些信息与用电行为的关系，对于提高窃电检测效率具有巨大潜力。有鉴于此，我们提出了Catch22-Conv-Transformer方法，这是一种基于多维度特征提取的方法，专为检测异常用电模式而设计。该方法利用Catch22特征集和互补特征来提取序列特征，随后使用卷积网络和Transformer架构来识别各种类型的窃电行为。我们利用中国国家电网公司提供的三相电力状态和每日用电数据进行评估，证明了我们的方法在准确识别包括逃避、篡改和数据操纵等窃电方式方面的有效性。
