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Related Concept Videos

Transformers in Distribution System01:27

Transformers in Distribution System

Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
Energy Losses in Transformers01:21

Energy Losses in Transformers

In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the copper windings...
Types Of Transformers01:16

Types Of Transformers

Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...

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Related Experiment Videos

An edge-AI framework with graph transformer learning for resilient microgrid topology attack identification.

D Gowtham Chakravarthy1, R Gopi2, K B Bhaskar3

  • 1Faculty of Computer Science & Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, 641202, India.

Scientific Reports
|July 10, 2026
PubMed
Summary

This study introduces a Lightweight Transformer-Based Edge AI (LTBEA) framework for detecting Line Outage Masking (LOM) attacks in wireless microgrids. The LTBEA framework achieves high accuracy and low latency, enhancing cybersecurity for modern power systems.

Keywords:
Cyber-physical securityEdge AILine outage maskingReal-time detectionTransformerWireless microgrid

Related Experiment Videos

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Cybersecurity

Background:

  • Wireless communication in microgrids enhances flexibility but introduces cybersecurity risks like Line Outage Masking (LOM) attacks.
  • LOM attacks manipulate system topology, potentially leading to incorrect control decisions and grid instability.
  • Existing detection methods may struggle with the complexity and real-time demands of wireless microgrid environments.

Purpose of the Study:

  • To propose and evaluate a Lightweight Transformer-Based Edge AI (LTBEA) framework for accurate and low-latency LOM attack detection.
  • To enhance the cybersecurity and operational resilience of wireless microgrid systems.
  • To provide a scalable and efficient solution deployable on edge computing devices.

Main Methods:

  • Developed a novel LTBEA framework integrating graph-based spatial feature extraction and transformer-based temporal attention.
  • Employed a lightweight CNN-1D for local feature extraction and a GRU-lite unit for temporal sequence modeling.
  • Deployed the framework on edge computing devices with adaptive filtering and normalization techniques.

Main Results:

  • Achieved 97.8% detection accuracy with a 1.9% false positive rate and 12ms average detection latency in a noisy wireless environment.
  • Demonstrated superior performance compared to traditional methods across various microgrid sizes and attack types.
  • Successfully detected Low Rate of False Alarm (LRFA) LOM attacks reliably with minimal computation.

Conclusions:

  • The LTBEA framework is a feasible and effective cybersecurity solution for wireless microgrids.
  • The proposed approach significantly boosts operational security, reliability, and resilience.
  • Edge deployment of LTBEA ensures real-time analytics and scalability in resource-constrained environments.