On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case

Autor: Fatemeh Dehrouyeh, Li Yang, Firouz Badrkhani Ajaei, Abdallah Shami

Publikováno v: IEEE Access, Vol 12, Pp 108703-108730 (2024)

As technology advances, the use of Machine Learning (ML) in cybersecurity is becoming increasingly crucial to tackle the growing complexity of cyber threats. While traditional ML models can enhance cybersecurity, their high energy and resource demand

Externí odkaz: https://doaj.org/article/c9679fe1fd1f4576b4cf2f2d1ff90367

Improved Mode-Adaptive Droop Control Strategy for the DC Microgrid

Autor: Jafar Mohammadi, Firouz Badrkhani Ajaei

Publikováno v: IEEE Access, Vol 7, Pp 86421-86435 (2019)

The mode-adaptive droop control (MADC) strategy enables bus voltage regulation and power sharing between the distributed energy resources (DERs) in the direct current (dc) microgrid without communication systems. The conventional MADC strategy may fa

Externí odkaz: https://doaj.org/article/b6f14b76653b488cbe2adddcd7769317

Non-Pilot Protection of the Inverter- Dominated Microgrid

Autor: Houman Lahiji, Firouz Badrkhani Ajaei, Ryan E. Boudreau

Publikováno v: IEEE Access, Vol 7, Pp 142190-142202 (2019)

Without utilizing costly communication systems, the existing protection strategies fail to reliably detect the occurrence and direction of faults in the inverter-dominated microgrid. To address this issue, this paper introduces a selective and reliab

Externí odkaz: https://doaj.org/article/527cb887db1f45cdada2bbe3d16a16d0

Versatile decentralised control of the DC microgrid

Autor: Jafar Mohammadi, Firouz Badrkhani Ajaei

Publikováno v: IET Smart Grid (2018)

This study proposes a versatile decentralised control strategy for the direct current (DC) microgrid based on the DC bus signalling method. Performance of the proposed control strategy is investigated under various generation and load disturbances in

Externí odkaz: https://doaj.org/article/973080748ef6468db2fd55893ab30bb1

Deep learning for high-impedance fault detection and classification: transformer-CNN

Autor: Khushwant Rai, Farnam Hojatpanah, Firouz Badrkhani Ajaei, Josep M. Guerrero, Katarina Grolinger

Publikováno v: Rai, K, Hojatpanah, F, Ajaei, F B, Guerrero, J M & Grolinger, K 2022, ' Deep learning for high-impedance fault detection and classification : transformer-CNN ', Neural Computing and Applications, vol. 34, no. 16, pp. 14067-14084 . https://doi.org/10.1007/s00521-022-07219-z

High-impedance faults (HIFs) exhibit low current amplitude and highly diverse characteristics, which make them difficult to be detected by conventional overcurrent relays. Various machine learning (ML) techniques have been proposed to detect and clas

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d3325d640137a2c51e1502bfc4ecdcad
https://doi.org/10.1007/s00521-022-07219-z