| Autor: |
Fernando Perez, Airan Frances, Javier Uceda |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 12, Pp 172832-172840 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3499325 |
| Popis: |
Due to the increasing presence of DC-DC power electronic converters in power distribution systems, having accurate models is essential for the analysis and design process. Black-box modeling approaches offer a convenient solution when the available information is limited, which is typically the case for commercial converters. Moreover, converters are known for their non-linear nature, which limits the applicability of linear models in applications where the operating point changes substantially, like in DC microgrids with renewable sources. This work proposes a model that improves on limitations of previous black-box non-linear models, particularly, the accuracy at capturing strong dynamic nonlinearities and a simple implementation. The model is based on performing a dynamic selection of local Wiener-Hammerstein models according to the state variables of the converter. The switching among models is accompanied by a state transfer to achieve a continuity in the response. Procedures for implementing local models of different order and identifying them are also addressed. The techniques are validated with experimental tests carried out on asynchronous buck and boost converters. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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