Montero-Robina P. , Gordillo F. , GÓMEZ-ESTERN AGUILAR, FABIO, Cuesta F.
No
Int. J. Electr. Power Energy Syst.
Article
Científica
5
1.711
23/11/2023
001234821000001
2-s2.0-85179729588
This paper presents a method for the control design of five-level DCC converters based on mixed-integer optimization and machine learning. The resulting controller is computationally simple and can be easily implemented on low-resource control hardware using simple nested “if-else” statements. The optimization problem is recalled from previous work by modifying the cost function to further enhance the dynamic performance. Additionally, and in contrast to previous works, the online implementation accomplished in this paper allows the system to cover a wider range of operating points. For this, the optimization problem is solved offline for several operating conditions, and the results are gathered into a dataset to train classification and regression trees (CARTs), which are later used online. Due to the generalization capability of the CARTs, a more flexible and less resource-intensive implementation is achieved which is capable of operating at points outside the ones considered in the training dataset. The resulting control strategy is compared in simulation and experiments with several alternative approaches found in the literature. This approach can be extended to other power converter topologies, allowing the implementation of optimized modulations. © 2023 The Authors
Classification (of information); Cost functions; Integer programming; Linear programming; Power converters; Classification trees; Diode-clamped; Diode-clamped converter; Machine-learning; Mixed-integer linear optimizations; Multilevel converter; On-machines; Optimization problems; Regression trees; Simple++; Machine learning