Electricity Theft Detection in AMI Using Customers’ Consumption Patterns

Jokar, Paria; Arianpoo, Nasim; Leung, Victor C. M.

doi:10.1109/tsg.2015.2425222

Cited by 557 publications

(367 citation statements)

References 23 publications

Supporting

Mentioning

362

Contrasting

Unclassified

Order By: Relevance

“…The following studies [1,6,20,24] present an overview about sources of NTLs. Also, with the emergence of the SG concept 190 and the emerging global roll-out of meters with advanced communications capabilities, SMs studies are identifying an interest with lines of research on to new potential points of attack/vulnerability [19,25,26]. Cyber and data attacks are identified need to be taken into account in solutions for the detection and mitigation of NTLs.…”

Section: Non-technical Lossesmentioning

confidence: 99%

“…Cyber and data attacks are identified need to be taken into account in solutions for the detection and mitigation of NTLs. A focus on possible attack vectors on metering equipment with advanced communication capabilities [19,[25][26][27]] is a line of recent research that have to be carried out in order to face the menace of NTLs in the context of SGs. Sources of NTLs and attack/vulnerability points are pictured in Figure 1.…”

Section: Non-technical Lossesmentioning

confidence: 99%

“…In [12,19] the techniques to detect electricity theft based on smart metering data are analyzed. In [20] an overview of the types of techniques is presented, but in limited way only covering data-based solutions.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Solutions for detection of non-technical losses in the electricity grid: A review

Viegas

Esteves

Melício

et al. 2017

Renewable and Sustainable Energy Reviews

134

View full text Add to dashboard Cite

This paper is a review of literature with an analysis on a selection of scientific studies for detection of non-technical losses. Non-technical losses occurring in the electric grid at level of transmission or of distribution have negative impact on economies, affecting utilities, paying consumers and states. The paper is concerned with the lines of research pursued, the main techniques used and the limitations on current solutions. Also, a typology for the categorization of solutions for detection of non-technical losses is proposed and the sources and possible attack/vulnerability points are identified. The selected literature covers a wide range of solutions associated with non-technical losses. Of the 103 selected studies, 6 are theoretical, 25 propose hardware solutions and 72 propose non-hardware solutions. Data based classification models and data from consumption with high resolution are respectively required in about 47% and 35% of the reported solutions. Available solutions cover a wide range of cases, with the main limitation found being the lack of an unified solution, which enables the detection of all kinds of non-technical losses.

show abstract

Section: Non-technical Lossesmentioning

confidence: 99%

Section: Non-technical Lossesmentioning

confidence: 99%

See 1 more Smart Citation

Solutions for detection of non-technical losses in the electricity grid: A review

Viegas

Esteves

Melício

et al. 2017

Renewable and Sustainable Energy Reviews

134

View full text Add to dashboard Cite

show abstract

“…A cyber security issue for smart meters is energy theft, e.g., an attacker modifies the values of energy consumption readings. Several detection systems have been developed against energy theft [51][52][53][54]. Through monitoring load profiles, detection systems are able to recognize anomalies, such as the drastic change of power usage at a specific time instant and unusual power usage patterns.…”

Section: Detection Systems Of Ami Networkmentioning

confidence: 99%

Cyber-Physical System Security of a Power Grid: State-of-the-Art

2016

View full text Add to dashboard Cite

Abstract:As part of the smart grid development, more and more technologies are developed and deployed on the power grid to enhance the system reliability. A primary purpose of the smart grid is to significantly increase the capability of computer-based remote control and automation. As a result, the level of connectivity has become much higher, and cyber security also becomes a potential threat to the cyber-physical systems (CPSs). In this paper, a survey of the state-of-the-art is conducted on the cyber security of the power grid concerning issues of: (1) the structure of CPSs in a smart grid; (2) cyber vulnerability assessment; (3) cyber protection systems; and (4) testbeds of a CPS. At Washington State University (WSU), the Smart City Testbed (SCT) has been developed to provide a platform to test, analyze and validate defense mechanisms against potential cyber intrusions. A test case is provided in this paper to demonstrate how a testbed helps the study of cyber security and the anomaly detection system (ADS) for substations.

show abstract

“…An important part of the tariff design process is the identification of meaningful customer classes that exhibit different consumption patterns, enabling the development of diversifiable products. Moreover, electrical customer classification can also play a crucial role in load forecasting [1][2] and modeling [3], electricity market development [4], energy system planning and operation [5] and theft detection [6]. Naturally, information on customer type (e.g.…”

mentioning

confidence: 99%

C-Vine copula mixture model for clustering of residential electrical load pattern data

Sun

Konstantelos

Štrbac

2017

2017 IEEE Power &Amp; Energy Society General Meeting

View full text Add to dashboard Cite

Abstract-The ongoing deployment of residential smart meters in numerous jurisdictions has led to an influx of electricity consumption data. This information presents a valuable opportunity to suppliers for better understanding their customer base and designing more effective tariff structures. In the past, various clustering methods have been proposed for meaningful customer partitioning. This paper presents a novel finite mixture modeling framework based on C-vine copulas (CVMM) for carrying out consumer categorization. The superiority of the proposed framework lies in the great flexibility of pair copulas towards identifying multi-dimensional dependency structures present in load profiling data. CVMM is compared to other classical methods by using real demand measurements recorded across 2,613 households in a London smart-metering trial. The superior performance of the proposed approach is demonstrated by analyzing four validity indicators. In addition, a decision tree classification module for partitioning new consumers is developed and the improved predictive performance of CVMM compared to existing methods is highlighted. Further case studies are carried out based on different loading conditions and different sets of large numbers of households to demonstrate the advantages and to test the scalability of the proposed method.Index Terms-Clustering, customer classification, C-vine, decision trees, mixture models, pair-copula construction, smart meters. I. INTRODUCTIONlectricity market liberalization has largely unbundled the distribution and supply services in many jurisdictions, providing customers with the freedom to select their electricity supplier. In this competitive environment, retail companies can improve the commercial attractiveness of their product by formulating tariffs aimed at different customer types. An important part of the tariff design process is the identification of meaningful customer classes that exhibit different consumption patterns, enabling the development of diversifiable products. Moreover, electrical customer classification can also play a crucial role in load forecasting [1][2] and modeling [3], electricity market development [4], energy system planning and operation [5] and theft detection [6]. Naturally, information on customer type (e.g. industrial, commercial, residential) provides important information regarding the likely electricity consumption pattern and intensity. However, for further partitioning and exploratory analysis to be carried out effectively, high-frequency demand measurements are necessary [7]. As such, the advent of smart metering has led to large-scale availability of consumption data that render clustering analysis increasingly possible.Load profile clustering aims to allocate consumers into a small number of homogeneous groups, ensuring that elements of the same cluster are similar between them, while being dissimilar to elements of different clusters. A large number of clustering techniques have been proposed in the past and applied to electrical load ...

show abstract

Electricity Theft Detection in AMI Using Customers’ Consumption Patterns

Cited by 557 publications

References 23 publications

Solutions for detection of non-technical losses in the electricity grid: A review

Solutions for detection of non-technical losses in the electricity grid: A review

Cyber-Physical System Security of a Power Grid: State-of-the-Art

C-Vine copula mixture model for clustering of residential electrical load pattern data

Contact Info

Product

Resources

About