Advanced electricity theft detection in smart metering systems via Channel-Correlation enhanced hierarchical kernel networks and matrix completion

IF 5.2 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Tao Jin , Wanhao Wang , Yulong Liu , Qinyu Huang , Mohamed A. Mohamed
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引用次数: 0

Abstract

Electricity theft can result in attacks and tampering with advanced metering infrastructure. Although electricity theft has decreased with the widespread adoption of smart meters, significantly increasing the amount of measured data, the issue persists. This paper presents a novel method termed channel-correlation exploited hierarchical kernel network to address the problem of electricity theft detection, integrating matrix completion and channel optimization techniques. Initially, the proposed method addresses the issue of missing or abnormal original data by employing the alternating direction method of multipliers, enhancing the quality of data samples for training purposes. Subsequently, the Hierarchical Kernel Network utilizes different kernel sizes to extract diverse feature sets, thereby capturing comprehensive information to improve recognition accuracy. Furthermore, leveraging the channel correlation exploitation module of the compression and excitation network, the network effectively analyzes and learns the unique features of each channel, significantly enhancing classification performance. Through ablation studies and experiments conducted with varying proportions of missing data and different fraud rates, the proposed model consistently demonstrates superior performance across all metrics compared to other models. The practicality and effectiveness of the model are further validated through implementation on hardware platforms. These findings provide robust evidence of the model’s efficacy and superiority in detecting electricity theft.
基于信道相关增强层次核网络和矩阵补全的智能计量系统中的先进窃电检测
电力盗窃可能导致攻击和篡改先进的计量基础设施。尽管随着智能电表的广泛采用,窃电现象有所减少,测量数据量也显著增加,但这个问题仍然存在。本文提出了一种新的方法,称为通道相关利用层次核网络来解决电力盗窃检测问题,将矩阵补全和通道优化技术相结合。首先,该方法采用乘数交替方向法解决原始数据缺失或异常的问题,提高了训练数据样本的质量。随后,层次核网络利用不同的核大小提取不同的特征集,从而获得全面的信息,提高识别精度。此外,利用压缩激励网络的通道相关性挖掘模块,网络有效地分析和学习了每个通道的独特特征,显著提高了分类性能。通过使用不同比例的缺失数据和不同的欺诈率进行消融研究和实验,与其他模型相比,所提出的模型在所有指标上始终表现出优越的性能。通过在硬件平台上的实现,进一步验证了模型的实用性和有效性。这些发现为该模型在检测电力盗窃方面的有效性和优越性提供了有力的证据。
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来源期刊
Measurement
Measurement 工程技术-工程:综合
CiteScore
10.20
自引率
12.50%
发文量
1589
审稿时长
12.1 months
期刊介绍: Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material, whose ultimate goal is an advancement in the state of the art of: measurement and metrology fundamentals, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures and methodologies for plants and industrial processes, performance analysis of systems, processes and algorithms, mathematical models for measurement-oriented purposes, distributed measurement systems in a connected world.
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