TY - GEN
T1 - Parameter Identification and Model Predictive Torque Control for Flywheel Energy Storage Systems with Permanent Magnet Synchronous Motors
AU - Yu, Yan
AU - Zhang, Jianwei
AU - Tian, Guizhen
AU - Liu, Guangchen
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - This paper presents a parameter identification technique and a model predictive torque control (MPTC) approach for the flywheel energy storage system (FESS) using a permanent magnet synchronous motor (PMSM). The study addresses the challenges of parameter uncertainties and mismatches, which can impact control performance. A two-step identification method is utilized to overcome the rank-deficient problem in the PMSM model. An adaptive MPTC strategy based on the Recursive Least Squares (RLS) algorithm with a forgetting factor is proposed to enhance system robustness. Simulation results demonstrate the effectiveness of the RLS-MTPC approach in achieving accurate control and mitigating parameter uncertainties.
AB - This paper presents a parameter identification technique and a model predictive torque control (MPTC) approach for the flywheel energy storage system (FESS) using a permanent magnet synchronous motor (PMSM). The study addresses the challenges of parameter uncertainties and mismatches, which can impact control performance. A two-step identification method is utilized to overcome the rank-deficient problem in the PMSM model. An adaptive MPTC strategy based on the Recursive Least Squares (RLS) algorithm with a forgetting factor is proposed to enhance system robustness. Simulation results demonstrate the effectiveness of the RLS-MTPC approach in achieving accurate control and mitigating parameter uncertainties.
KW - Flywheel Energy Storage System
KW - Model Predictive Torque Control
KW - Parameter Identification
KW - Permanent Magnet Synchronous Motor
KW - Recursive Least Squares
UR - https://www.scopus.com/pages/publications/85205863613
U2 - 10.1007/978-981-97-7047-2_47
DO - 10.1007/978-981-97-7047-2_47
M3 - Conference contribution
AN - SCOPUS:85205863613
SN - 9789819770465
T3 - Lecture Notes in Electrical Engineering
SP - 416
EP - 421
BT - Proceedings of the 4th International Symposium on New Energy and Electrical Technology
A2 - Wen, Fushuan
A2 - Aris, Ishak Bin
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Symposium on New Energy and Electrical Technology, ISNEET 2023
Y2 - 20 October 2023 through 22 October 2023
ER -