A fast and non-parameter-dependent grid-current-control method to ride through dangerous unbalanced gird condition is proposed in this paper. The grid-current references are calculated from an artificial intelligence (AI) surrogate model in order to keep the capacitor voltage at a safe level under a two phases short circuit to ground condition. And also, the circulating current reference are determined when the power factor is different when the grid fault is not serious. This machine learning network represents the relation between grid-current references and submodule capacitor voltages. The results show that this method prevents capacitor-overvoltage trips under completely short-circuited grid.
|Conference||IEEE 11th International Symposium on Power Electronics for Distributed Generation Systems |
|Period||28/09/2020 → 01/10/2020|
|Series||2020 Ieee 11th International Symposium on Power Electronics for Distributed Generation Systems (pedg)|
- Modular Multilevel Converters
- Submodule capacitor voltage, machine learning
- Grid current control