Machine Learning-Based Cyber-Attack Detection in Photovoltaic Farms-Reference-Cited by-同舟云学术

Machine Learning-Based Cyber-Attack Detection in Photovoltaic Farms

Published:2023 Issue: Volume:4 Page:658-673
ISSN:2644-1314
Container-title:IEEE Open Journal of Power Electronics
language:
Short-container-title:IEEE Open J. Power Electron.

Author:

Zhang Jinan¹^ORCID,Guo Lulu²^ORCID,Ye Jin¹^ORCID,Giani Annarita³,Elasser Ahmed³,Song Wenzhan¹^ORCID,Liu Jianzhe⁴^ORCID,Chen Bo⁵^ORCID,Mantooth H. Alan⁶^ORCID

Affiliation:

1. School of Electrical and Computer Engineering, University of Georgia, Athens, GA, USA

2. College of Electronics and Information Engineering, Tongji University, Shanghai, China

3. General Electric Research Center, Niskayuna, NY, USA

4. Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

5. Smart Grid - Emerging Technology, ComEd, IL, USA

6. Department of Electrical Engineering and Computer Science, University of Arkansas, Fayetteville, AR, USA

Funder

U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy

Solar Energy Technologies Office

National Science Foundation

Georgia Research Alliance

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

https://ieeexplore.ieee.org/ielam/8782709/10004734/10234181-aam.pdf

Reference55 articles.

1. Fault Prognostics for Photovoltaic Inverter Based on Fast Clustering Algorithm and Gaussian Mixture Model

2. A Novel Convolutional Neural Network-Based Approach for Fault Classification in Photovoltaic Arrays

3. Vulnerability Assessments for Power-Electronics-Based Smart Grids

4. Detecting System Fault/Cyberattack within a Photovoltaic System Connected to the Grid: A Neural Network-Based Solution

5. Development of flexible active power control strategies for grid-connected photovoltaic inverters by modifying MPPT algorithms

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