Development of adapted ammeter for fraud detection in low-voltage installations

Henriques, Henrique Oliveira; Barbero, Andrés Pablo López; Ribeiro, Ricardo M.; Fortes, Márcio Zamboti; Zanco, Wagner S.; Xavier, Odair; Amorim, Rafhael

doi:10.1016/j.measurement.2014.06.015

Cited by 25 publications

(9 citation statements)

References 22 publications

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“…RFID tags have been proposed to be used to seal meters and speed up inspections [35]. In Brazil, a metering architecture using two reading points has been tested and concluded as enabling easy theft detection [36].…”

mentioning

confidence: 99%

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

Viegas

Esteves

Melício

et al. 2017

Renewable and Sustainable Energy Reviews

143

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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.

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mentioning

confidence: 99%

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

Viegas

Esteves

Melício

et al. 2017

Renewable and Sustainable Energy Reviews

143

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“…In the previous studies [3][4][5][6], researchers have explored the utilization of specific external hardware devices and designs, specialized metering devices, distribution transformers, sensors, and various types of metering devices, for ETD. A specific method discussed in [7] involves the utilization of an adapted ammeter device for theft detection on the low-voltage (LV) side of the power network. This approach focuses on comparing the differences in electrical parameters between local and remote devices to identify theft [7].…”

Section: State-estimation Basedmentioning

confidence: 99%

“…A specific method discussed in [7] involves the utilization of an adapted ammeter device for theft detection on the low-voltage (LV) side of the power network. This approach focuses on comparing the differences in electrical parameters between local and remote devices to identify theft [7]. The state estimation method is employed at the substation level to detect anomalies and, subsequently, electricity theft within a cluster [7].…”

Section: State-estimation Basedmentioning

confidence: 99%

“…This approach focuses on comparing the differences in electrical parameters between local and remote devices to identify theft [7]. The state estimation method is employed at the substation level to detect anomalies and, subsequently, electricity theft within a cluster [7]. However, this method has certain drawbacks, primarily the high cost associated with the implementation of additional devices and the inherent challenges involved in installing these devices within the existing system.…”

Section: State-estimation Basedmentioning

confidence: 99%

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Using machine learning ensemble method for detection of energy theft in smart meters

Kawoosa,

Prashar,

Faheem

et al. 2023

IET Generation Trans & Dist

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Electricity theft is a primary concern for utility providers, as it leads to substantial financial losses. To address the issue, a novel extreme gradient boosting (XGBoost)‐based model utilizing the consumers’ electricity consumption patterns for analysis is proposed for electricity theft detection (ETD). To remove the imbalance in the real‐world electricity consumption dataset and ensure an even distribution of theft and non‐theft data instances, six different artificially created theft attacks were used. Moreover, the utilization of the XGBoost algorithm for classification, especially to identify malicious instances of electricity theft, yielded commendable accuracy rates and a minimal occurrence of false positives. The proposed model identifies electricity theft specific to the regions, utilizing electricity consumption parameters, and other variables as input features. The authors’ model outperformed existing benchmarks like k‐neural networks, light gradient boost, random forest, support vector machine, decision tree, and AdaBoost. The simulation results using the false attacks for balancing the dataset have improved the proposed model's performance, achieving a precision, recall, and F1‐score of 96%, 95%, and 95%, respectively. The results of the detection rate and the false positive rate (FPR) of the proposed XGBoost‐based detection model have achieved 96% and 3%, respectively.

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“…The authors in [95] addressed the NTL detection problem by proposing a radio frequency identification (RFID) technology for the sealing of energy meters and speeding the inspection process. The authors in [65] proposed two points reading that identifies the difference in the electric current flowing from the power pole to the meter wire. The difference in readings of both points can lead to the detection of an NTL activity.…”

Section: Hardware-based Solutionsmentioning

confidence: 99%

Detection of Non-Technical Losses in Power Utilities—A Comprehensive Systematic Review

et al. 2020

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Electricity theft and fraud in energy consumption are two of the major issues for power distribution companies (PDCs) for many years. PDCs around the world are trying different methodologies for detecting electricity theft. The traditional methods for non-technical losses (NTLs) detection such as onsite inspection and reward and penalty policy have lost their place in the modern era because of their ineffective and time-consuming mechanism. With the advancement in the field of Artificial Intelligence (AI), newer and efficient NTL detection methods have been proposed by different researchers working in the field of data mining and AI. The AI-based NTL detection methods are superior to the conventional methods in terms of accuracy, efficiency, time-consumption, precision, and labor required. The importance of such AI-based NTL detection methods can be judged by looking at the growing trend toward the increasing number of research articles on this important development. However, the authors felt the lack of a comprehensive study that can provide a one-stop source of information on these AI-based NTL methods and hence became the motivation for carrying out this comprehensive review on this significant field of science. This article systematically reviews and classifies the methods explored for NTL detection in recent literature, along with their benefits and limitations. For accomplishing the mentioned objective, the opted research articles for the review are classified based on algorithms used, features extracted, and metrics used for evaluation. Furthermore, a summary of different types of algorithms used for NTL detection is provided along with their applications in the studied field of research. Lastly, a comparison among the major NTL categories, i.e., data-based, network-based, and hybrid methods, is provided on the basis of their performance, expenses, and response time. It is expected that this comprehensive study will provide a one-stop source of information for all the new researchers and the experts working in the mentioned area of research.

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Development of adapted ammeter for fraud detection in low-voltage installations

Cited by 25 publications

References 22 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

Using machine learning ensemble method for detection of energy theft in smart meters

Detection of Non-Technical Losses in Power Utilities—A Comprehensive Systematic Review

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