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Tohid Behdadnia - IEEE Xplore Author Profile
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Tohid Behdadnia

Also published under: T. Behdadnia

Publications
5
Citations
9
Publications by Year
20212024
Co-Authors:
Show All Co-Authors (8)
Image of author Tohid Behdadnia

Tohid Behdadnia

Also published under: T. Behdadnia

Affiliation

Department of Electrical Engineering (ESAT)
KU Leuven
EnergyVille, Leuven, Belgium

Publication Topics

Biography

Tohid Behdadnia (Student Member, IEEE) is currently a Ph.D. candidate in the Electrical Engineering Department at KU Leuven, Belgium, where he began his doctoral studies in 2021. Since then, he has been contributing to the CYPRESS project, aimed at advancing cybersecurity measures for bulk electric energy supply systems. His research focuses on leveraging deep learning methodologies to identify vulnerabilities and detect anomalies in cyber-physical power systems. He also collaborates with the EnergyVille Research Center, where he is involved in the (co-) simulation of power and ICT systems, aiming to enhance the security and resilience of energy systems through innovative technology integration.
Publications
5
Citations
9
Publications by Year
20212024
Co-Authors:
Show All Co-Authors (8)
Author's Published Works

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Model Order Reduction for Waste Heat Recovery: An Integrated Autoencoder-LSTM Neural Network Approach

Sina Toghranegar;Tohid Behdadnia;Geert Deconinck;Ruth V. Sabariego

2024 International Workshop on Artificial Intelligence and Machine Learning for Energy Transformation (AIE)
Year: 2024 | Conference Paper |
An evaporator is a critical component in waste heat recovery systems, particularly within organic Rankine cycle technologies. In this paper, an approach for the simulation of two-phase evaporators is presented. This method aims at dimension reduction and prediction of temporal measurements, leveraging an autoencoder integrated with long short-term memory neural networks. The autoencoder-LSTM integ...Show More

Model Order Reduction for Waste Heat Recovery: An Integrated Autoencoder-LSTM Neural Network Approach

Sina Toghranegar;Tohid Behdadnia;Geert Deconinck;Ruth V. Sabariego

Year: 2024 | Conference Paper |

Leveraging Deep Learning to Increase the Success Rate of DoS Attacks in PMU-Based Automatic Generation Control Systems

Tohid Behdadnia;Klaas Thoelen;Fairouz Zobiri;Geert Deconinck

IEEE Transactions on Industrial Informatics
Year: 2024 | Volume: 20, Issue: 4 | Journal Article |
Cited by: Papers (5)
The stability of modern power grids relies heavily on effective frequency control, mainly achieved through automatic generation control (AGC) systems. However, AGC systems have become increasingly vulnerable to cyber-physical attacks due to their dependence on communication infrastructure and cyber-physical devices. In this article, we present a novel attack strategy that leverages the learning ca...Show More

Leveraging Deep Learning to Increase the Success Rate of DoS Attacks in PMU-Based Automatic Generation Control Systems

Tohid Behdadnia;Klaas Thoelen;Fairouz Zobiri;Geert Deconinck

Year: 2024 | Volume: 20, Issue: 4 | Journal Article |

Encrypted Traffic Classification for Early-Stage Anomaly Detection in Power Grid Communication Network

Tohid Behdadnia;Geert Deconinck;Can Ozkan;Dave Singelee

2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE)
Year: 2023 | Conference Paper |
Secure communication networks are crucial for the proper functioning of power grid infrastructure. Anomaly detection is essential for maintaining network security, but most existing methods rely on power system measurement anomalies that appear only after an attack has been effectively executed. Identifying attacks in their early stages, primarily through communication network-based anomalies, is ...Show More

Encrypted Traffic Classification for Early-Stage Anomaly Detection in Power Grid Communication Network

Tohid Behdadnia;Geert Deconinck;Can Ozkan;Dave Singelee

Year: 2023 | Conference Paper |

A New Deep Learning-Based Strategy for Launching Timely DoS Attacks in PMU-Based Cyber-Physical Power Systems

Tohid Behdadnia;Geert Deconinck

2022 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
Malicious attacks in the cyber-physical power systems (CPPS) can eventually result in cascading failure and even widespread blackout, if not rectified in a timely manner. The probability of success of most of these attacks mainly depends on their timeliness, as the degree of system vulnerabilities varies from time to time by changing its operating state. In this paper, we propose a new denial of s...Show More

A New Deep Learning-Based Strategy for Launching Timely DoS Attacks in PMU-Based Cyber-Physical Power Systems

Tohid Behdadnia;Geert Deconinck

Year: 2022 | Conference Paper |

EV-Integrated Power System Transient Stability Prediction Based on Imaging Time Series and Deep Neural Network

T. Behdadnia;M. Parlak

2021 IEEE International Intelligent Transportation Systems Conference (ITSC)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
The market penetration of electric vehicles (EVs) has increased drastically. However, the high integration of EV fast-charging stations (EVFCS) into the power systems makes them more vulnerable to severe grid disturbances. In case of a disturbance driving the power system to instability, a fast prediction of stability status is vital for allowing sufficient time to take intelligent emergency contr...Show More

EV-Integrated Power System Transient Stability Prediction Based on Imaging Time Series and Deep Neural Network

T. Behdadnia;M. Parlak

Year: 2021 | Conference Paper |

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