Determining a set of suspicious electricity customers using statistical ACL Tukey’s control charts method

Spirić, Josif V.; Stanković, Slobodan S.; Dočić, Miroslav B.

doi:10.1016/j.ijepes.2016.04.035

Cited by 7 publications

(2 citation statements)

References 15 publications

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“…The same work is continued in [16] with experimental additions. In [17], a statistical method based on Tukey's control charts is used to analyze customers' time series to estimate the suspicious customers. The series are formed by using historical monthly data measurements of electricity consumption.…”

Section: B Related Workmentioning

confidence: 99%

Remote Detection and Identification of Illegal Consumers in Power Grids

2019

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Electricity theft is a common problem in electric power systems around the world. It causes heavy economic losses and badly affects the reliability of the power grid. One of the most common and simplest methods of stealing electricity is tapping energy directly from the overhead power feeder. The other most common method of theft is the tampering with meters to reduce the recorded consumption by illegal ways. In this paper, we present a cost-effective remote detection and identification method for detecting illegal electricity consumption. It also identifies the illegal user in real time without any pre-processing or extensive analysis of a huge amount of collected data. Moreover, it preserves the privacy of customers by destroying the high-resolution data of instantaneous power consumption collected from customers' meters. The system can detect suspicious consumer(s) online and sends notifications to the utility control center with the ID number(s) of the suspicious meter(s) or the amount of load that has been tapped to the power feeder within the area served by a single distribution transformer. The extensive simulations using Simulink were conducted to validate the proposed scheme. For further validation of the scheme, hardware-in-the-loop (HIL) simulation was conducted using three microcontroller-based meters and Simulink environment. The results of both types of experiments showed that the proposed scheme can successfully detect and identify fraudulent users in real time.

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Section: B Related Workmentioning

confidence: 99%

Remote Detection and Identification of Illegal Consumers in Power Grids

2019

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“…The suspected boundary region is calculated by rough sets. The authors in [117] proposed the asymmetric control limit (ACL)-based control charts for addressing the same problem. The upper and lower limits are used to enhance the balance between total energy used by customers, the total energy measured, and the total losses.…”

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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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