TY - JOUR
T1 - Tolerant Sequential Model Predictive Control Based on Lexicographic Optimization Method for T-Type Three-Phase Three-Level Inverters
AU - Chen, Shengwei
AU - Yang, Yong
AU - Chen, Rong
AU - Hu, Jiefeng
AU - Wen, Huiqing
AU - Wang, Yiwang
AU - Wu, Weimin
AU - Fang, Gang
AU - Rodriguez, Jose
N1 - Publisher Copyright:
© 1986-2012 IEEE.
PY - 2025
Y1 - 2025
N2 - Due to the existence of the neutral point (NP) voltage, controlling the three-level inverters has essentially become a multiobjective optimization problem (MOOP) that needs to provide stable output voltages for the load and maintain the NP voltage simultaneously. Traditionally, this MOOP is converted into a single-objective optimization problem by weighting factors. However, since the physical dimensions of the two control objectives are usually different, it is challenging to choose proper weighting factors to obtain a satisfactory performance according to a specific theory. To address this issue, a tolerant sequential model predictive control (TSMPC) utilizing a lexicographic optimization method is proposed in this article. This method establishes two distinct layers for the output voltage and NP voltage, arranging them in sequence according to the importance of control objectives to evaluate all voltage vectors. By using an explainable tolerance value rather than conventional weighting factors, the proposed TSMPC algorithm presents superior performance over traditional MPC approaches. Finally, the feasibility and effectiveness of the proposed TSMPC algorithm have been verified through relevant experiments and the stability of this algorithm has also been analyzed.
AB - Due to the existence of the neutral point (NP) voltage, controlling the three-level inverters has essentially become a multiobjective optimization problem (MOOP) that needs to provide stable output voltages for the load and maintain the NP voltage simultaneously. Traditionally, this MOOP is converted into a single-objective optimization problem by weighting factors. However, since the physical dimensions of the two control objectives are usually different, it is challenging to choose proper weighting factors to obtain a satisfactory performance according to a specific theory. To address this issue, a tolerant sequential model predictive control (TSMPC) utilizing a lexicographic optimization method is proposed in this article. This method establishes two distinct layers for the output voltage and NP voltage, arranging them in sequence according to the importance of control objectives to evaluate all voltage vectors. By using an explainable tolerance value rather than conventional weighting factors, the proposed TSMPC algorithm presents superior performance over traditional MPC approaches. Finally, the feasibility and effectiveness of the proposed TSMPC algorithm have been verified through relevant experiments and the stability of this algorithm has also been analyzed.
KW - Lexicographic optimization method
KW - model predictive control (MPC)
KW - multiobjective optimization problem (MOOP)
KW - weighting factors
UR - http://www.scopus.com/inward/record.url?scp=85209074600&partnerID=8YFLogxK
U2 - 10.1109/TPEL.2024.3490654
DO - 10.1109/TPEL.2024.3490654
M3 - Article
AN - SCOPUS:85209074600
SN - 0885-8993
VL - 40
SP - 3020
EP - 3032
JO - IEEE Transactions on Power Electronics
JF - IEEE Transactions on Power Electronics
IS - 2
ER -