Qashqai, Pouria, Babaie, Mohammad, Zgheib, Rawad et Al-Haddad, Kamal.
2025.
« Implementation of deep reinforcement learning for model-free switching and control of a 23-level single DC source hybrid packed U-cell (HPUC) ».
IEEE Access.
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Résumé
This paper proposes a novel Deep Reinforcement Learning (DRL) method for controlling a 23-level Single DC Source Hybrid Packed U-Cell (HPUC) converter. The HPUC topology generates a high number of voltage levels with minimal components but presents control challenges due to its numerous switching states and dynamic charging behavior. Unlike conventional control methods, which require accurate models and are sensitive to noise and parameter mismatches, DRL offers a model-free and resilient approach to the non-linear control of such complex systems. A Deep Q-Network (DQN) agent which is inherently model-free and suited for high-dimensional state spaces and discrete action spaces, is employed to address these issues. To validate the proposed method, simulations were conducted in the MATLAB/Simulink environment. The obtained results demonstrated the satisfactory performance of the proposed DRL method, achieving a Total Harmonic Distortion (THD) of 2.71% in the output current under steady-state, maintaining stable capacitor voltage balancing, and exhibiting rapid dynamic response (e.g., settling within approximately 40 ms for current step changes). Furthermore, its resilience was highlighted by its ability to maintain control despite a 25dB SNR noise condition and up to 15% variations in capacitor values.
| Type de document: | Article publié dans une revue, révisé par les pairs |
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| Professeur: | Professeur Al Haddad, Kamal |
| Affiliation: | Génie électrique |
| Date de dépôt: | 18 sept. 2025 13:35 |
| Dernière modification: | 25 sept. 2025 00:28 |
| URI: | https://espace2.etsmtl.ca/id/eprint/31925 |
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