Identification and detection of electricity customer behaviour irregularities

Nizar, A.H.; Zhang, Dong

doi:10.1109/psce.2009.4840253

Cited by 57 publications

(28 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Also, irregularities in the measurement of consumption/billing and collusion with utility employees are considered to be sources of NTLs [1,6,24]. 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].…”

Section: Non-technical Lossesmentioning

confidence: 99%

“…In [20] an overview of the types of techniques is presented, but in limited way only covering data-based solutions. The authors believe that the growing amount of literature and the wide range of techniques and solutions justify the need for a review on the state-of-the-art for abstraction 60 of the main lines pursued by researchers.…”

mentioning

confidence: 99%

“…In [20] fraud detection techniques are classified as unsupervised, supervised and semi-supervised. In [12] techniques for theft detection are classified as classification-based, state-based and game theory-based.…”

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

144

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%

mentioning

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

144

View full text Add to dashboard Cite

show abstract

“…Reference [3,4] introduced support vector machine (SVM), and fuzzy inference systems to combine professional knowledge into the model to find abnormal electricity using. Reference [5] using extreme learning machine (ELM) and OS-ELM algorithm to improve the precision of abnormal electricity identification and compared the result with support vector machine. Reference [6] used neural network to classify power load curve.…”

Section: Introductionmentioning

confidence: 99%

Research on Abnormal Power Consumption Model Based on User Multidimensional Compound Features

Xiong¹,

Chen²,

Wu³

et al. 2017

dtcse

View full text Add to dashboard Cite

Abstract. Power loss is a serious problem for all power companies. To find effective means of abnormal electricity iidentification is a popular research field in recent years. This paper puts forward an anomalous electricity use model based on multi-dimensional compound features of electricity users. The support vector machine, local outlier factor, correlation measurement based on the similar user power load, and correlation change rate measurement based on the most relevant users-these four algorithms are adopted to extract four-dimensional compound features of anomalous electricity use from the perspective of global anomaly, local anomaly, regional space, and time sequence. Next, the logistic regression (LR) model is trained based on the compound features. After training, the LR model is adopted as the final anomalous electricity use identification model. Analysis of the practical power load data of users suggests that the LR model combine respective advantages of the four-dimensional compound features. Detection of anomalies using the LR model is an effective approach, which can reliably and accurately identify residents' anomalous electricity use. From the accuracy rate, recall rate, precision rate and scores of F1, it can be seen that the LR model is significantly superior to SVM.

show abstract

“…Os resultados apresentados nas Tabelas 6.1, 6.2 e 6.3 com os classificadores SVM e ANN foram próximos dos resultados de exatidão encontrados na literatura. A utilização do classificador OPF neste trabalho de pesquisa se destaca por apresentar uma exatidão mais alta quando comparado aos resultados dos outros classificadores mais tradicionais já utilizados em pesquisas internacionais relacionadas com as perdas comerciais [45], [46], [51], [52].…”

Section: Observaçõesunclassified

Desenvolvimento de ferramentas computacionais inteligentes para identificação de perdas comerciais em sistemas de energia

Ramos¹

View full text Add to dashboard Cite

show abstract

Identification and detection of electricity customer behaviour irregularities

Cited by 57 publications

References 51 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

Research on Abnormal Power Consumption Model Based on User Multidimensional Compound Features

Desenvolvimento de ferramentas computacionais inteligentes para identificação de perdas comerciais em sistemas de energia

Contact Info

Product

Resources

About