Research Topic

Latest Techniques and Challenges in Detecting and Localizing of Non-technical Losses in Smart Grids

About this Research Topic

Smart Grids are playing an important role in the revamping of the traditional electrical power grids or dump grids. Artificial intelligence is playing a vital role in different applications, including Smart Grids. However, there are some technical issues which need to be addressed in Smart Grids. Among those issues, an important one is that of wastage of electricity due to line losses. These losses can be due to both Technical Losses (TL) and Non-technical Losses (NTL), NTL is commonly known as electricity theft. In Smart Grids, one of the biggest security problems is electricity theft, which can be either due to physical attacks, cyber attacks, or billing irregularities. Advanced metering infrastructure (AMI) is of the key features of Smart Grids. However, there are some disadvantages to AMI as well; a successful cyberattack such as a black hole or gray whole attack on AMI and Meter Data Management Systems (MDMS) can forge all the real entries in the database remotely, which is an advanced form of electricity theft.

Even after installing smart meters in millions, utility companies still generate less revenue every year because of the wastage of electricity due to line losses. After thorough review, it has been observed that the losses are in the form of NTL that includes cyber-attacks on AMI and MDMS. This is a growing issue in both developed and developing countries. Smart Grids are more prone to NTL as AMI and MDMS will be a target to hackers. A hacker can easily change entries in the MDMS if a proper cyber security mechanism is not in place. It is therefore concluded that NTL detection and localization is still a challenging issue in both traditional as well as smart grids.

This Research Topic solicits Original Research and Review papers, the topics of interest include, but are not limited to:

• NTL detection and localization in Smart Grids;
• NTL detection and localization in grid-tied Microgrids;
• Physical and Cyberattack detection on AMI;
• Cyberattacks detection in MDMS;
• Analysis and impact of NTL on utilities;
• Artificial intelligence (AI) based NTL detection in power grids.


Keywords: non-technical losses, advanced metering infrastructure, cyber attack detection, meter data management systems, MDMS, AMI


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Smart Grids are playing an important role in the revamping of the traditional electrical power grids or dump grids. Artificial intelligence is playing a vital role in different applications, including Smart Grids. However, there are some technical issues which need to be addressed in Smart Grids. Among those issues, an important one is that of wastage of electricity due to line losses. These losses can be due to both Technical Losses (TL) and Non-technical Losses (NTL), NTL is commonly known as electricity theft. In Smart Grids, one of the biggest security problems is electricity theft, which can be either due to physical attacks, cyber attacks, or billing irregularities. Advanced metering infrastructure (AMI) is of the key features of Smart Grids. However, there are some disadvantages to AMI as well; a successful cyberattack such as a black hole or gray whole attack on AMI and Meter Data Management Systems (MDMS) can forge all the real entries in the database remotely, which is an advanced form of electricity theft.

Even after installing smart meters in millions, utility companies still generate less revenue every year because of the wastage of electricity due to line losses. After thorough review, it has been observed that the losses are in the form of NTL that includes cyber-attacks on AMI and MDMS. This is a growing issue in both developed and developing countries. Smart Grids are more prone to NTL as AMI and MDMS will be a target to hackers. A hacker can easily change entries in the MDMS if a proper cyber security mechanism is not in place. It is therefore concluded that NTL detection and localization is still a challenging issue in both traditional as well as smart grids.

This Research Topic solicits Original Research and Review papers, the topics of interest include, but are not limited to:

• NTL detection and localization in Smart Grids;
• NTL detection and localization in grid-tied Microgrids;
• Physical and Cyberattack detection on AMI;
• Cyberattacks detection in MDMS;
• Analysis and impact of NTL on utilities;
• Artificial intelligence (AI) based NTL detection in power grids.


Keywords: non-technical losses, advanced metering infrastructure, cyber attack detection, meter data management systems, MDMS, AMI


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

29 January 2021 Manuscript
26 February 2021 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

29 January 2021 Manuscript
26 February 2021 Manuscript Extension

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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