A Detection Method for Group Fixed Ratio Electricity Thieves Based on Correlation Analysis of Non-Technical Loss

Yang, Yining; Song, Runan; Xue, Yang; Zhang, Penghe; Xu, Yuejie; Kang, Jiannan; Zhao, Haisen

doi:10.1109/access.2022.3141610

Cited by 7 publications

(6 citation statements)

References 25 publications

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“…A series of tests via comparisons with other state-ofthe-art methods regarding accuracy, stability, and scalability is conducted. The results indicate that the proposed method has superior and stable performance in detecting FRETs, particularly when it comes to multiple FRETs in one area [5].…”

Section: Literature Reviewmentioning

confidence: 93%

Electricity Theft Detection in Power Consumption using Superiority of Machine Learning Algorithm

Khot¹,

Dhore²,

Thorat³

et al. 2023

IJSREM

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Electricity theft is the act of stealing electricity by deceptive techniques. Electricity theft represents a large chunk of NTL (Non- Technical Loss). These are the losses caused by misidentified, mis-allocated, or incorrect energy flows. Electricity theft is a major issue in India, as it is in most of the developing countries. Although the theft can be detected using machine learning techniques. In this study, the system is proposed that reads smart meter data (input) using OCR (Optimized Character Recognition). OCR is a technique for recognizing text characters in digital images that have been printed or handwritten. The smart meter data image which consists of electricity usage units is converted into machine- readable text by OCR. Furthermore, the SARIMAX (Seasonal Auto Regressive Integrated Moving Average with exogenous factors) algorithm is utilized to monitor customers electricity consumption and detect electricity theft. If theft is identified, an alert message and details of the theft are sent to an electrical board worker. The worker then manually verifies/checks and updates the status. If no theft is discovered, a bill is generated. Key Words: Electricity Theft, Machine Learning, Optical Character Recognition, SARIMAX

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Section: Literature Reviewmentioning

confidence: 93%

Electricity Theft Detection in Power Consumption using Superiority of Machine Learning Algorithm

Khot¹,

Dhore²,

Thorat³

et al. 2023

IJSREM

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“…Based on the revealed dependencies on the input data, the behavior of the model is predicted. Regressions were used in [27][28][29][30][31]; correlation analysis was used in [32][33][34][35][36][37][38][39].…”

Section: Methods Of Identification and Assessment Of Nontechnical Los...mentioning

confidence: 99%

Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review

Pazderin,

Kamalov,

Gubin

et al. 2023

Energies

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Nontechnical losses of electrical energy (NTLEE) have been a persistent issue in both the Russian and global electric power industries since the end of the 20th century. Every year, these losses result in tens of billions of dollars in damages. Promptly identifying unscrupulous consumers can prevent the onset of NTLEE sources, substantially reduce the amount of NTLEE and economic damages to network grids, and generally improve the economic climate. The contemporary advancements in machine learning and artificial intelligence facilitate the identification of NTLEE sources through anomaly detection in energy consumption data. This article aims to analyze the current efficacy of computational methods in locating, detecting, and identifying nontechnical losses and their origins, highlighting the application of neural network technologies. Our research indicates that nearly half of the recent studies on identifying NTLEE sources (41%) employ neural networks. The most utilized tools are convolutional networks and autoencoders, the latter being recognized for their high-speed performance. This paper discusses the main metrics and criteria for assessing the effectiveness of NTLEE identification utilized in training and testing phases. Additionally, it explores the sources of initial data, their composition, and their impact on the outcomes of various algorithms.

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“…Generally, NTL is caused by factors outside the power system, such as electricity thievery, one of the primary sources of NTL. In addition, electricity theft can seriously harm the economic performance of power providers and leads to safety problems like equipment damage, power outages, and injuries [6] Several countries have transformed their conventional grids into smart grids to avoid traditional electricity theft methods [8,9]. A smart grid is "an application of digital information technology to optimize electrical power generation, delivery, and use" [10].…”

Section: Electricity Fraud Detectionmentioning

confidence: 99%

Deep Learning Models for Efficient Detection of Electricity Fraud in Smart Grids

Nadjib,

ferial,

Moumene

et al. 2023

IJETAE

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—Electricity theft is one of the biggest problems facing energy companies. These companies eventually claimed that the conventional methods for preventing electricity fraud were insufficient, which led to the creation of systems based on artificial intelligence to identify thieves among electricity consumers. In this paper, we propose a system based on deep learning to identify customers who have engaged in fraudulent activity on smart grids. We selected one-dimensional (1D) and twodimensional (2D) convolutional neural network models from deep learning models to achieve our objective. Also, we proposed a new method to fill in missing values in the data set. Our findings show that our models enhance the performance of systems that identify electricity thieves.

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A Detection Method for Group Fixed Ratio Electricity Thieves Based on Correlation Analysis of Non-Technical Loss

Cited by 7 publications

References 25 publications

Electricity Theft Detection in Power Consumption using Superiority of Machine Learning Algorithm

Electricity Theft Detection in Power Consumption using Superiority of Machine Learning Algorithm

Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review

Deep Learning Models for Efficient Detection of Electricity Fraud in Smart Grids

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