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In power systems, the entire setup is divided into protective zones to isolate faults and protect the rest of the network. These zones include generators, transformers, buses, transmission lines, distribution lines, and motors. Each zone can be visualized as a separate room in a house, with each room protected by its own circuit breaker.
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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Artificial Intelligence-Based Secured Power Grid Protocol for Smart City.

Adel Sulaiman1, Bharathiraja Nagu2, Gaganpreet Kaur2

  • 1Department of Computer Science, College of Computer Science, and Information Systems, Najran University, Najran 61441, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces an AI-based approach using Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) to enhance smart grid security and efficiency. The method effectively detects cyber threats in power grids, improving data management and privacy.

Keywords:
cyber Securityedge-cloud-assistedpower gridsrecurrent neural networksecured gridsmart city

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Modern power systems are increasingly complex, integrating numerous smart grid components that generate vast data volumes.
  • Traditional computing struggles with smart grid data, necessitating AI-driven solutions for efficient management and security.
  • Cyberattacks pose a significant threat to smart grid stability and efficiency.

Purpose of the Study:

  • To develop an AI-based model using LSTM and RNN to enhance the dynamic properties of smart grids.
  • To differentiate between normal system changes and real-time cyber threats, including those from Revised Encoding Schemes (RES).
  • To propose a federated learning strategy for secure and efficient consumer power data sharing.

Main Methods:

  • Development of LSTM and RNN models tailored to adaptively capture time-varying energy system attributes.
  • Implementation of a federated learning strategy with edge cloud support for consumer data sharing, prioritizing privacy and communication efficiency.
  • Design of optimization problems for Energy Data Owners (EDO) and energy service operations, considering non-independent and identically distributed (IID) data effects.

Main Results:

  • The proposed LSTM RNN-based structure effectively identifies False Data Injection Attacks (FDIA) and other cyber threats.
  • Simulations indicate the approach successfully encourages EDOs to use high-quality local models, boosting energy service provider payouts and reducing latency.
  • LSTM models demonstrated longer training durations and higher training loss compared to other tested models, but achieved effective threat detection.

Conclusions:

  • The developed AI-based method, particularly using LSTM RNN, provides a robust solution for detecting sophisticated cyber threats in smart grids.
  • The federated learning strategy ensures consumer privacy while enabling efficient data utilization for improved grid operations.
  • The approach enhances smart grid security, efficiency, and data management capabilities in the face of evolving threats and data complexities.