Publication detail

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

KURUKURU, V. KHAN, M. SAHOO, S.

Original Title

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

Type

journal article in Web of Science

Language

English

Original 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.

Keywords

Splines (mathematics); Cyberattack; Voltage measurement; Mathematical models; Physics; Circuit faults; Current measurement; Anomaly diagnosis; artificial intelligence; cyberattacks; photovoltaic inverters

Authors

KURUKURU, V.; KHAN, M.; SAHOO, S.

Released

8. 6. 2022

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Location

PISCATAWAY

ISBN

0885-8993

Periodical

IEEE TRANSACTIONS ON POWER ELECTRONICS

Year of study

37

Number

11

State

United States of America

Pages from

12938

Pages to

12943

Pages count

6

URL

BibTex

@article{BUT178406,
  author="V S Bharath {Kurukuru} and Mohammed Ali {Khan} and Subham {Sahoo}",
  title="Cybersecurity in Power Electronics Using Minimal Data - A Physics-Informed Spline Learning Approach",
  journal="IEEE TRANSACTIONS ON POWER ELECTRONICS",
  year="2022",
  volume="37",
  number="11",
  pages="12938--12943",
  doi="10.1109/TPEL.2022.3180943",
  issn="0885-8993",
  url="https://ieeexplore.ieee.org/abstract/document/9791853"
}