An artificial insurance framework for a hydrogen-based microgrid to detect the advanced cyberattack model

Abstract Microgrid systems have evolved based on renewable energies including wind, solar, and hydrogen to make the satisfaction of loads far from the main grid more flexible and controllable using both island- and grid-connected modes. Albeit microgrids can gain beneficial results in cost and energ...

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Main Authors: Mahan Fakhrooeian, Ali Basem, Mohammad Mahdi Gholami, Nahal Iliaee, Alireza Mohammadi Amidi, Amin Heydarian Hamzehkanloo, Akbar Karimipouya
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
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Online Access:https://doi.org/10.1038/s41598-025-88090-5
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author Mahan Fakhrooeian
Ali Basem
Mohammad Mahdi Gholami
Nahal Iliaee
Alireza Mohammadi Amidi
Amin Heydarian Hamzehkanloo
Akbar Karimipouya
author_facet Mahan Fakhrooeian
Ali Basem
Mohammad Mahdi Gholami
Nahal Iliaee
Alireza Mohammadi Amidi
Amin Heydarian Hamzehkanloo
Akbar Karimipouya
author_sort Mahan Fakhrooeian
collection DOAJ
description Abstract Microgrid systems have evolved based on renewable energies including wind, solar, and hydrogen to make the satisfaction of loads far from the main grid more flexible and controllable using both island- and grid-connected modes. Albeit microgrids can gain beneficial results in cost and energy schedules once operating in grid-connected mode, such systems are vulnerable to malicious attacks from the viewpoint of cybersecurity. With this in mind, this paper explores a novel advanced attack model named the false transferred data injection (FTDI) attack aiming to manipulatively alter the power flowing from the microgrid to the upstream grid to raise voltage usability probability. One crucial piece of information that the model uses to change the system and cause the greatest amount of damage while concealing the attacker’s view is the voltage stability index. Saying that the power transaction between the microgrid and the upstream grid is within the broad scope of bilateral exchange at any given moment is noteworthy. Put otherwise, with respect to the FTDI assault, the microgrid’s power direction is just as significant to the detection system as the transferred power value. Therefore, once the microgrid is running in the grid-connected mode, the false data detector needs to concurrently detect changes in the value and direction of power. To overcome this problem, the paper presents a learning generative network model, based on the generative adversarial network (GAN) paradigm, to recognize the change in probability values that is maliciously aimed. To this end, a studied microgrid system including the wind turbine, photovoltaic, storage, tidal turbine, and fuel cell units is performed on the tested 24-bus IEEE grid to satisfy the local load demands. Comparative analysis indicates notable gains, such as scores of 0.95%, 0.92%, 0.7%, and 10% for the Hit rate, C.R. rate, F.A. rate, and Miss rate in order to evaluate the GAN-based detection model within the microgrid.
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spelling doaj-art-a12708ee93254a8eb88a8f8439c70e9f2025-02-02T12:19:26ZengNature PortfolioScientific Reports2045-23222025-01-0115113110.1038/s41598-025-88090-5An artificial insurance framework for a hydrogen-based microgrid to detect the advanced cyberattack modelMahan Fakhrooeian0Ali Basem1Mohammad Mahdi Gholami2Nahal Iliaee3Alireza Mohammadi Amidi4Amin Heydarian Hamzehkanloo5Akbar Karimipouya6Institute for Electrical Machines, Traction and Drives, Technische Universität BraunschweigAir Conditioning Engineering Department, College of Engineering, University of Warith Al-AnbiyaaFaculty of Electrical Engineering, Shahid Beheshti UniversityDepartment of Electronics, Carleton UniversityDepartment of Electrical Engineering, Razi University of KermanshahDepartment of Mechanical Engineering, Mashhad Branch, Islamic Azad UniversityKhuzestan Water & Power Authority (KWPA)Abstract Microgrid systems have evolved based on renewable energies including wind, solar, and hydrogen to make the satisfaction of loads far from the main grid more flexible and controllable using both island- and grid-connected modes. Albeit microgrids can gain beneficial results in cost and energy schedules once operating in grid-connected mode, such systems are vulnerable to malicious attacks from the viewpoint of cybersecurity. With this in mind, this paper explores a novel advanced attack model named the false transferred data injection (FTDI) attack aiming to manipulatively alter the power flowing from the microgrid to the upstream grid to raise voltage usability probability. One crucial piece of information that the model uses to change the system and cause the greatest amount of damage while concealing the attacker’s view is the voltage stability index. Saying that the power transaction between the microgrid and the upstream grid is within the broad scope of bilateral exchange at any given moment is noteworthy. Put otherwise, with respect to the FTDI assault, the microgrid’s power direction is just as significant to the detection system as the transferred power value. Therefore, once the microgrid is running in the grid-connected mode, the false data detector needs to concurrently detect changes in the value and direction of power. To overcome this problem, the paper presents a learning generative network model, based on the generative adversarial network (GAN) paradigm, to recognize the change in probability values that is maliciously aimed. To this end, a studied microgrid system including the wind turbine, photovoltaic, storage, tidal turbine, and fuel cell units is performed on the tested 24-bus IEEE grid to satisfy the local load demands. Comparative analysis indicates notable gains, such as scores of 0.95%, 0.92%, 0.7%, and 10% for the Hit rate, C.R. rate, F.A. rate, and Miss rate in order to evaluate the GAN-based detection model within the microgrid.https://doi.org/10.1038/s41598-025-88090-5MicrogridCyberattackDetection modelGAN MethodRenewable energy unit
spellingShingle Mahan Fakhrooeian
Ali Basem
Mohammad Mahdi Gholami
Nahal Iliaee
Alireza Mohammadi Amidi
Amin Heydarian Hamzehkanloo
Akbar Karimipouya
An artificial insurance framework for a hydrogen-based microgrid to detect the advanced cyberattack model
Scientific Reports
Microgrid
Cyberattack
Detection model
GAN Method
Renewable energy unit
title An artificial insurance framework for a hydrogen-based microgrid to detect the advanced cyberattack model
title_full An artificial insurance framework for a hydrogen-based microgrid to detect the advanced cyberattack model
title_fullStr An artificial insurance framework for a hydrogen-based microgrid to detect the advanced cyberattack model
title_full_unstemmed An artificial insurance framework for a hydrogen-based microgrid to detect the advanced cyberattack model
title_short An artificial insurance framework for a hydrogen-based microgrid to detect the advanced cyberattack model
title_sort artificial insurance framework for a hydrogen based microgrid to detect the advanced cyberattack model
topic Microgrid
Cyberattack
Detection model
GAN Method
Renewable energy unit
url https://doi.org/10.1038/s41598-025-88090-5
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