We would like to seek your advice on optimizing the model architecture and improving offline simulation performance for an SST (Solid-State Transformer) system.
System Description:
The SST system consists of a three phase 35 kV AC input and an 800 V DC output. Each phase contains 24 cascaded power cells, resulting in a total of 72 cells across the three phases.
Each cell consists of an HBridge and a CLLLC converter, with 12 semiconductor switches in total per cell. Within each phase, the 24 HBridge cells are connected in series on the AC input side, while their DC outputs are connected in parallel.
Simulation Issue:
I am currently performing offline simulations but has encountered significant simulation performance issues.We have tried setting the variable-step solver to Auto and selecting a non-stiff/stiff solver. However, neither approach has resulted in a significant improvement in simulation speed.
One important observation is that the simulation runs successfully when only the 72 cascaded H-Bridge topology is included. However, once the CLLLC converters are added to the model, the simulation performance deteriorates significantly, and the simulation becomes impractical to run.
Request for Technical Advice:
Given the current simulation performance, we would greatly appreciate your recommendations on the following aspects:
1.How to optimize the overall model architecture for such a large-scale SST system.
2.Whether there are recommended modeling approaches or techniques to improve offline simulation efficiency when integrating a large number of CLLLC converters.
3.Whether any specific solver settings, model simplifications, or PLECS features could help improve simulation performance.
4.Whether alternative modeling approaches could be considered to reduce computational complexity while maintaining sufficient simulation accuracy.
5.We would be happy to provide screenshots of the current solver settings, an overview of the model architecture, or any additional information required for further investigation.
Thank you very much for your support and recommendations.