A review of non-technical loss attack models and detection methods in the smart grid

Chuwa, Maria Gabriel; Wang, Fei

doi:10.1016/j.epsr.2021.107415

Cited by 25 publications

(22 citation statements)

References 46 publications

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“…All malicious samples generated by the equations from previous works [11,21,23] and additional equations were used in [27] to create a more diverse case of energy theft. A multiclass classification problem was framed and several ML algorithms were used to solve it.…”

Section: Related Workmentioning

confidence: 99%

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Detection of Non-Technical Losses on a Smart Distribution Grid Based on Artificial Intelligence Models

Souza,

Gouveia,

Ferreira

et al. 2024

Energies

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Non-technical losses (NTL) have been a growing problem over the years, causing significant financial losses for electric utilities. Among the methods for detecting this type of loss, those based on Artificial Intelligence (AI) have been the most popular. Many works use the electricity consumption profile as an input for AI models, which may not be sufficient to develop a model that achieves a high detection rate for various types of energy fraud that may occur. In this paper, using actual electricity consumption data, additional statistical and temporal features based on these data are used to improve the detection rate of various types of NTL. Furthermore, a model that combines both the electricity consumption data and these features is developed, achieving a better detection rate for all types of fraud considered.

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Section: Related Workmentioning

confidence: 99%

“…Based on this, equations are needed to model the consumption behavior of fraudulent users. In this work, the same eleven equations used in [27] are used, with some modifications to generate more practical cases of energy theft, as they cover a large number of possibilities for these types of NTL. Let x j,m , j = 1, 2, .…”

Section: Energy Theft Attack Modelsmentioning

confidence: 99%

Detection of Non-Technical Losses on a Smart Distribution Grid Based on Artificial Intelligence Models

Souza,

Gouveia,

Ferreira

et al. 2024

Energies

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“…Specifically, two categories, non-technical loss (NTL) and loss from technical issues (TL), are the major electricity losses. TL happened due to power lines affected by joules and the transformer loss through the transportation of electrical power [7]. It is complex to calculate the TL because finding the point of loss is difficult.…”

Section: Introductionmentioning

confidence: 99%

“…Partial solutions are eliminated, followed by a new solution with the same number of cuckoos. A straightforward power-law equation may represent levy() with the random walk as in Equation (7).…”

mentioning

confidence: 99%

Predictive Data Analytics for Electricity Fraud Detection Using Tuned CNN Ensembler in Smart Grid

et al. 2022

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In the smart grid (SG), user consumption data are increasing very rapidly. Some users consume electricity legally, while others steal it. Electricity theft causes significant damage to power grids, affects power supply efficiency, and reduces utility revenues. This study helps utilities reduce the problems of electricity theft, inefficient electricity monitoring, and abnormal electricity consumption in smart grids. To this end, an electricity theft dataset from the state grid corporation of China (SGCC) is employed and this study develops a novel model, a mixture of convolutional neural network and gated recurrent unit (CNN-GRU), for automatic power theft detection. Moreover, the hyperparameters of the proposed model are tuned using a meta-heuristic method, the cuckoo search (CS) algorithm. The class imbalance problem is solved using the synthetic minority oversampling technique (SMOTE). The clean data are trained and then tested with the proposed classification. Extensive simulations are performed based on real energy consumption data. The simulated results show that the proposed theft detection model (CNN-GRU-CS) solved the theft classification problem better than other approaches in terms of effectiveness and accuracy by 10% on average. The calculated accuracy of the proposed method is 92% and the precision is 94%.

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“…Traditional methods for detecting electricity theft rely heavily on manual inspections and billing analysis, necessitating specialized personnel for on-site investigations. These inspections entail scrutinizing the connections of electrical devices and the status of electric meters, with anomalies identified through comparisons between historical electricity consumption and current usage patterns [4], [5].…”

mentioning

confidence: 99%

Dynamic Generative Residual Graph Convolutional Neural Networks for Electricity Theft Detection

Zhuang,

Jiang,

Xia

et al. 2024

IEEE Access

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Driven by economic incentives, illegal electricity consumers pose significant threats to the economic and security aspects of the power system by illicitly accessing or manipulating electrical resources. With the widespread adoption of Advanced Metering Infrastructure (AMI), researchers have turned to leveraging smart meter data for electricity theft detection. However, existing models rely on methods that model a single electricity load curve and cannot capture the temporal dependencies, periodicity, and underlying features between electricity consumption cycles. This paper proposes a dynamic generation algorithm to address these issues to construct a topological structure between periodic nodes, updated during training. Subsequently, residual graph convolution operations extract temporal and spatial dependencies among nodes. Additionally, to address the issue of model instability caused by scarce theft data, we employ the SMOTE (Synthetic Minority Over-sampling Technique) oversampling technique and enhance overall classification performance by modifying class weights in the loss function. We trained this network architecture on the real SGCC (State Grid Corporation of China) dataset, and the results demonstrate its superiority over other mainstream existing models.

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A review of non-technical loss attack models and detection methods in the smart grid

Cited by 25 publications

References 46 publications

Detection of Non-Technical Losses on a Smart Distribution Grid Based on Artificial Intelligence Models

Detection of Non-Technical Losses on a Smart Distribution Grid Based on Artificial Intelligence Models

Predictive Data Analytics for Electricity Fraud Detection Using Tuned CNN Ensembler in Smart Grid

Dynamic Generative Residual Graph Convolutional Neural Networks for Electricity Theft Detection

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