@inproceedings{c12953f952354081b2e5cf47ce774098,
title = "Q-Learning-Based Frequency and Voltage Restoration for Parallel Grid-Forming Inverters",
abstract = "This study addresses the critical challenge of frequency and voltage restoration in parallel Inverter-Based Resources (IBRs) while maintaining precise control over active and reactive power distribution. The proposed approach integrates advanced voltage and frequency restoration technique to enhance system stability. To optimize controller parameters during dynamic state transitions, particularly during load connection and disconnection events, a Q-learning algorithm is implemented. Through the presented studies, the performance of the proposed Q-learning-based control strategy is evaluated and compared against conventional PI-based controllers and Particle Swarm Optimization (PSO) based approaches. The results demonstrate the effectiveness of the proposed Q-learning-based restoration strategy, achieving the most stable frequency response during different local load events.",
keywords = "Droop control, Frequency restoration, Q-Learning, Secondary control, Voltage restoration",
author = "Gurbuz, \{Fethi Batincan\} and Anas Karaki and Sertac Bayhan",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 19th IEEE International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2025 ; Conference date: 20-05-2025 Through 22-05-2025",
year = "2025",
month = may,
day = "22",
doi = "10.1109/CPE-POWERENG63314.2025.11027222",
language = "English",
isbn = "979-8-3315-1518-8",
series = "Compatibility Power Electronics And Power Engineering",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2025 Ieee 19th International Conference On Compatibility, Power Electronics And Power Engineering, Cpe-powereng",
address = "United States",
}