Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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COAR Access Rights: search.filters.coarAccessRights.open accessSubject: search.filters.subject.Intelligent Systems

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  • Conference Object
    Citation - WoS: 5
    Citation - Scopus: 17
    Novel Hybrid Design for Microgrid Control
    (IEEE Computer Society, 2017-11) Bintoudi, Angelina D.; Zyglakis, Lampros; Apostolos, Tsolakis; Ioannidis, Dimosthenis K.; Al-Agtash, Salem Y.; Martinez-Ramos, J. L.; Martensen, Nis; Tzovaras, Dimitrios
    This paper proposes a new hybrid control system for an AC microgrid. The system uses both centralised and decentralised strategies to optimize the microgrid energy control while addressing the challenges introduced by current technologies and applied systems in real microgrid infrastructures. The well-known 3-level control (tertiary, secondary, primary) is employed with an enhanced hierarchical design using intelligent agent-based components in order to improve efficiency, diversity, modularity, and scalability. The main contribution of this paper is dual. During normal operation, the microgrid central controller (MGCC) is designed to undertake the management of the microgrid, while providing the local agents with the appropriate constraints for optimal power flow. During MGCC fault, a peer-to-peer communication is enabled between neighbouring agents in order to make their optimal decision locally. The initial design of the control structure and the detailed analysis of the different operating scenarios along with their requirements have shown the applicability of the new system in real microgrid environments. © 2023 Elsevier B.V., All rights reserved.
  • Conference Object
    Machine Learning Based Beamwidth Adaptation for mmWave Vehicular Communications
    (Institute of Electrical and Electronics Engineers Inc., 2023-12-10) Manic, Setinder; Heng Foh, Chuan; Köse, Abdulkadir; Lee, Haeyoung; Leow, Chee Yen; Chatzimisios, Periklis; Suthaputchakun, Chakkaphong; Foh, Chuan Heng
    The incorporation of mmWave technology in vehicular networks has unlocked a realm of possibilities, propelling the advancement of autonomous vehicles, enhancing interconnectedness, and facilitating communication for intelligent transportation systems (ITS). Despite these strides in connectivity, challenges such as high path-loss have arisen, impacting existing beam management procedures. This work aims to address this issue by improving beam management techniques, specifically focusing on enhancing the service time between vehicles and base stations through adaptive mmWave beamwidth adjustments, accomplished using a Contextual Multi-Armed Bandit Algorithm. By leveraging various conditions to train the ML agent of the Contextual Multi-Armed Bandit Algorithm, it seeks to learn about vehicle mobility profiles and optimize the usage of different antenna beamwidth settings to maximize seamless connection time. The extensive simulation results showcase the effectiveness of an adaptive beamwidth for mobility profiles, extending the connection time a vehicle experiences with a base station when compared to the existing strategies. © 2024 Elsevier B.V., All rights reserved.