Data-Driven Modeling and Prediction of Transient Dynamics of Microgrids through Dynamic Mode Decomposition with Control
- Resource Type
- Conference
- Authors
- Jiang, Xinyuan; Li, Yan; Huang, Daning
- Source
- 2024 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2024 IEEE. :1-5 Feb, 2024
- Subject
- Power, Energy and Industry Applications
Data-driven modeling
Microgrids
Predictive models
Mathematical models
Numerical models
Transient analysis
Load modeling
Distributed Energy Resources (DERs)
transient dynamics
Dynamic Mode Decomposition with Control (DMDc)
data-driven
nonlinear
- Language
- ISSN
- 2472-8152
Dynamic Mode Decomposition with Control (DMDc) is presented to develop a data-driven modeling approach for microgrids when the accurate mathematical model is unavailable. As a system identification method, it can precisely model and predict the transient response of microgrids subject to large disturbances. A modified DMDc method is introduced to handle the piecewise constant inputs issue. Precautions on applying the method on measurement data are also discussed. Numerical examples on a typical islanded microgrid have demonstrated that local dynamics can be accurately captured through the DMDc-based data-driven model. By requesting a small amount of data, the data-driven modeling is powerful for performing system forecast and data-driven control.