Electricity Theft Detection Using Machine Learning Techniques to Secure Smart Grid

2020 ◽  
pp. 233-243
Author(s):  
Muhammad Adil ◽  
Nadeem Javaid ◽  
Zia Ullah ◽  
Mahad Maqsood ◽  
Salman Ali ◽  
...  
Keyword(s):  
Machine Learning ◽  
Smart Grid ◽  
Machine Learning Techniques ◽  
Electricity Theft ◽  
Learning Techniques

Detecting false data attacks using machine learning techniques in smart grid: A survey

2020 ◽  
Vol 170 ◽  
pp. 102808 ◽  
Author(s):  
Lei Cui ◽  
Youyang Qu ◽  
Longxiang Gao ◽  
Gang Xie ◽  
Shui Yu
Keyword(s):  
Machine Learning ◽  
Smart Grid ◽  
Machine Learning Techniques ◽  
Learning Techniques

Detection of Electricity Theft by Applying Deep Learning Techniques in Electrical Distribution Network: A Comprehensive Review

2021 ◽  
Vol 14 ◽  
Author(s):  
Rakhi Yadav ◽  
Yogendra Kumar
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Power Plants ◽  
Distribution Networks ◽  
Research Area ◽  
Machine Learning Techniques ◽  
Electricity Theft ◽  
Learning Techniques ◽  
Electrical Distribution Network ◽  
The Impact

Introduction: Non-Technical Losses (NTL) occur up to 40 % of the total electric transmission and distribution power. Hence, across the world, the power system is facing many challenges. The occurrence of such large amounts of losses cannot be ignored. These losses have severe impacts on distribution utilities. The performance of electric distribution networks adversely affects due to these losses. The reduction of these NTL consequently reduces the requirement of new power plants to fulfil the demand-supply gap. Hence, NTL is an emerging research area for electrical engineers. This paper has covered various deep learning and machine learning models used to detect non-technical losses. Discussion: There is a lack of research in this field so far. The existing literature only shows the detection of non-technical losses using a machine and deep learning. This paper also provides the causes of NTL followed by an impact on economies, a variation of NTL in different countries. Further, we have provided a comparative analysis based on several essential parameters. We have also discussed various simulation tools. Moreover, several challenges occur during machine and deep learning-based detection of NTL, and its possible solutions are also discussed. Conclusion: In the present paper, we have reviewed the impact of NTLs on economies, potential revenue losses, and electricity provider's profit. Further, it provides a detailed review of deep learning and machine learning techniques used to detect the NTL. This survey has also discussed challenges in machine learning-based detection of NTL, followed by their possible solutions. In addition, this paper also provides details about various tools and simulation environments used to detect the NTL. We are confident that this comprehensive survey will help the researchers to research this thrust area.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques
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