Resumen
Deep reinforcement learning (DRL) offers outstanding algorithms to develop optimal controllers for power converters with uncertainties and non-linear dynamics. This work comprehensively analyses a model-free control algorithm for three-phase inverters using DRL agents. To this end, different deep deterministic policy gradient (DDPG) agents with variable hyperparameters were conceptualized, designed, and tested. On average, DDPG agents were shown to have excellent performance in the control of power inverters. Indeed, DDPG agents reduce the impact of model uncertainties and non-linear dynamics. To validate the proposed control policy, the two-level voltage source power inverter is simulated. Also, the main features of the control strategy are analyzed in terms of computational cost, root medium square error (RMSE), and total harmonic distortion (THD). Simulated results reveal that the proposed control strategy exhibits strong performance in the current control task, achieving a maximum RMSE of 0.78 A and a THD of 3.17% for a 6 kHz sampling frequency.
Idioma original | Inglés |
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Título de la publicación alojada | COBEP 2023 - 17th Brazilian Power Electronics Conference and SPEC 2023 - 8th IEEE Southern Power Electronics Conference, Proceedings |
Editorial | Institute of Electrical and Electronics Engineers Inc. |
ISBN (versión digital) | 9798350321128 |
DOI | |
Estado | Publicada - 2023 |
Evento | 8th Southern Power Electronics Conference and the 17th Brazilian Power Electronics Conference, SPEC / COBEP 2023 - Florianopolis, Brasil Duración: 2023 → 2023 |
Serie de la publicación
Nombre | COBEP 2023 - 17th Brazilian Power Electronics Conference and SPEC 2023 - 8th IEEE Southern Power Electronics Conference, Proceedings |
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Conferencia
Conferencia | 8th Southern Power Electronics Conference and the 17th Brazilian Power Electronics Conference, SPEC / COBEP 2023 |
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País/Territorio | Brasil |
Ciudad | Florianopolis |
Período | 26/11/23 → 29/11/23 |
Nota bibliográfica
Publisher Copyright:© 2023 IEEE.
Áreas temáticas de ASJC Scopus
- Ingeniería energética y tecnologías de la energía
- Ingeniería eléctrica y electrónica
- Ingeniería mecánica
- Control y optimización