Abstract
A significant amount of converter-based generation is being integrated into the bulk electric power grid to fulfill the future electric demand through renewable energy sources, such as wind and photovoltaic. The dynamics of converter systems in the overall stability of the power system can no longer be neglected as in the past. Numerous efforts have been made in the literature to derive detailed dynamic models, but using detailed models becomes complicated and computationally prohibitive in large system level studies. In this paper, we use a data-driven, black-box approach to model the dynamics of a power electronic converter. System identification tools are used to identify the dynamic models, while a power amplifier controlled by a real-time digital simulator is used to perturb and control the converter. A set of linear dynamic models for the converter are derived, which can be employed for system level studies of converter-dominated electric grids.
| Original language | English |
|---|---|
| Title of host publication | 2020 IEEE Power and Energy Society General Meeting, PESGM 2020 |
| Publisher | IEEE Computer Society |
| ISBN (Electronic) | 9781728155081 |
| DOIs | |
| State | Published - 2 Aug 2020 |
| Externally published | Yes |
| Event | 2020 IEEE Power and Energy Society General Meeting, PESGM 2020 - Montreal, Canada Duration: 2 Aug 2020 → 6 Aug 2020 |
Publication series
| Name | IEEE Power and Energy Society General Meeting |
|---|---|
| Volume | 2020-August |
| ISSN (Print) | 1944-9925 |
| ISSN (Electronic) | 1944-9933 |
Conference
| Conference | 2020 IEEE Power and Energy Society General Meeting, PESGM 2020 |
|---|---|
| Country/Territory | Canada |
| City | Montreal |
| Period | 2/08/20 → 6/08/20 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Converter-dominated electric power systems
- Data-driven modeling
- Grid-connected converters
- System identification
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