Energy-theft detection issues for advanced metering infrastructure in smart grid

By Rong Jiang, Rongxing Lu, Yeyu Wang, Jun Luo, Changxiang Shen, Xuemin Shen. Tsinghua Science and Technology, 2014

296 citations recorded by the source corpus.

With the proliferation of smart grid research, the Advanced Metering Infrastructure (AMI) has become the first ubiquitous and fixed computing platform. However, due to the unique characteristics of AMI, such as complex network structure, resource-constrained smart meter, and privacy-sensitive data, it is an especially challenging issue to make AMI secure. Energy theft is one of the most important concerns related to the smart grid implementation. It is estimated that utility companies lose more than $25 billion every year due to energy theft around the world. To address this challenge, in this paper, we discuss the background of AMI and identify major security requirements that AMI should meet. Specifically, an attack tree based threat model is first presented to illustrate the energy-theft behaviors in AMI. Then, we summarize the current AMI energy-theft detection schemes into three categories, i.e., classification-based, state estimation-based, and game theory-based ones, and make extensive comparisons and discussions on them. In order to provide a deep understanding of security vulnerabilities and solutions in AMI and shed light on future research directions, we also explore some open challenges and potential solutions for energy-theft detection.

Subfields: Control and Systems Engineering, Electrical and Electronic Engineering, Information Systems

View Paper: https://www.semanticscholar.org/paper/be0c5d88e1ea01eec9979fb0eefbdb755c8dc169

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In Control and Systems Engineering

In Electrical and Electronic Engineering

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Citations in the corpus

Electricity Theft Detection in AMI Using Customers’ Consumption Patterns (2016, background): “Current AMI ETDSs are mainly categorized into three groups [5]: 1) state; 2) game theory; and 3) classificationbased.”
Energy system resilience – A review (2021, background): “The former group includes consumers interested in stealing energy [168] and companies interested in gaining competitive advantage through unlawful means [7,26].”
Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids (2018, methodology): “However, most of these approaches have the following limitations: a) many of them require specific devices [1]; b) most of them are based on artificial feature extraction according to domain knowledge (requiring manual interventions); c) many methods [such as support vector machine (SVM) and linear ”
Anomaly Detection for IoT Time-Series Data: A Survey (2020, methodology): “In addition to the detection of technical losses within power networks a number of approaches have been suggested for the detection of non-technical losses (energy theft) using the information provided by commonly installed smart meters [19], [20].”