TY - GEN
T1 - Assessment of Deep Reinforcement Learning Algorithms for Three-Phase Inverter Control
AU - Menéndez, Oswaldo
AU - López-Caiza, Diana
AU - Tarisciotti, Luca
AU - Ruiz, Felipe
AU - Auat-Cheein, Fernando
AU - Rodríguez, José
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - deep reinforcement learning
KW - Neural network
KW - non-linear control
KW - power converter
KW - voltage source inverter
UR - http://www.scopus.com/inward/record.url?scp=85185803100&partnerID=8YFLogxK
U2 - 10.1109/SPEC56436.2023.10407331
DO - 10.1109/SPEC56436.2023.10407331
M3 - Conference contribution
AN - SCOPUS:85185803100
T3 - COBEP 2023 - 17th Brazilian Power Electronics Conference and SPEC 2023 - 8th IEEE Southern Power Electronics Conference, Proceedings
BT - COBEP 2023 - 17th Brazilian Power Electronics Conference and SPEC 2023 - 8th IEEE Southern Power Electronics Conference, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th Southern Power Electronics Conference and the 17th Brazilian Power Electronics Conference, SPEC / COBEP 2023
Y2 - 26 November 2023 through 29 November 2023
ER -