- By:
- Subedi, Sunil ; Qiao, Liang; Xue, Yaosuo ; Gui, Yonghao ; Tuffner, Frank; Du, Wei
- Page Number:
- 4450-4456
- Book Title:
- 2024 IEEE Energy Conversion Congress and Exposition (ECCE)
- Publication Date:
- March 12, 2026
- Publisher Location:
- IEEE, New Jersey, United States of America
- Conference Name:
- IEEE Energy Conversion Conference and Exposition (ECCE)
- Conference Location:
- Phoenix, Arizona, United States of America
- Conference Sponsor:
- IEEE
- View DOI Listing:
- https://doi.org/10.1109/ECCE55643.2024.10861015
Abstract
Inverter-based resource (IBR) models are necessary to analyze modern power system stability and create effective control strategies. Modeling IBRs in converter-rich power systems is crucial, yet challenging due to the lack of commercial information on converter topologies and control parameters. This paper proposes novel convolutional neural network (CNN)–based data-driven techniques for modeling IBRs, addressing adaptability and proprietary concerns without requiring internal system physics knowledge. The proposed method is tested using real grid-tied commercial IBR transient data and demonstrates effectiveness and accuracy. Furthermore, the developed modeling approach is integrated and implemented in the open-source power distribution simulation and analysis tool, GridLAB-D, to illustrate the potentiality of dynamic analysis of large-scale power systems with high IBRs.
