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Summary
This summary is machine-generated.

This study introduces an autoencoder-based anomaly detection algorithm to safeguard Battery Electrical Storage Systems (BESS) against cyberattacks by monitoring their physical behavior for dangerous conditions.

Keywords:
anomaly detectionautoencodercybersecuritydistributed energy resourceselectrical battery storage systemsneural network

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Area of Science:

  • Power Systems Engineering
  • Cybersecurity in Energy Grids
  • Artificial Intelligence for Grid Stability

Background:

  • Distributed Energy Resources (DERs) are increasingly vital for modern power systems.
  • Battery Electrical Storage Systems (BESS) are crucial for integrating unpredictable Renewable Energy Sources (RES).
  • BESS are vulnerable to cyberattacks due to remote SCADA system control.

Purpose of the Study:

  • To analyze the cybersecurity vulnerabilities of BESS.
  • To propose an anomaly detection algorithm for BESS.
  • To enhance the detection of dangerous working conditions in BESS.

Main Methods:

  • Developed an anomaly detection algorithm utilizing an autoencoder neural network architecture.
  • Focused on observing the physical behavior of BESS for anomaly identification.
  • Compared the proposed autoencoder approach with the One Class Support Vector Machine (OCSVM) algorithm.

Main Results:

  • The autoencoder-based algorithm effectively detects anomalies in BESS operation.
  • Demonstrated superior performance compared to the traditional One Class Support Vector Machine (OCSVM).
  • The method successfully identifies dangerous working conditions indicative of cyber threats.

Conclusions:

  • The proposed autoencoder anomaly detection offers a robust solution for BESS cybersecurity.
  • Monitoring physical behavior is a viable strategy to detect cyberattacks on BESS.
  • This approach enhances the resilience and safety of power systems with integrated BESS.