Cyber Security in Power Electronics Using Minimal Data - A Physics-Informed Spline Learning Approach

K. V.S. Bharath, Mohammed Ali Khan, Subham Sahoo*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Cyberattacks can be strategically counterfeited to replicate grid faults, thereby manipulating the protection system and leading to accidental disconnection of grid-tied converters. To prevent such setbacks, we propose a physics-informed spline learning approach-based anomaly diagnosis mechanism to distinguish between both events using minimal data for the first time in the realm of power electronics. This methodology not only provides compelling accuracy with limited data, but also reduces the training and computational resources significantly. We validate its effectiveness and accuracy under experimental conditions to conclude how data availability problem can be handled.

Original languageEnglish
JournalI E E E Transactions on Power Electronics
Volume37
Issue number11
Pages (from-to)12938-12943
Number of pages6
ISSN0885-8993
DOIs
Publication statusPublished - 1 Nov 2022

Keywords

  • Cyber attacks
  • Machine Learning
  • Physics informed machine learning
  • Physics informed spline learning
  • Cybersecurity
  • Grid connected inverter
  • PV systems
  • Generative adversarial network
  • Data quality
  • Power electronics
  • Anomaly diagnosis
  • artificial intelligence
  • photovoltaic inverters
  • cyberattacks

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