Artificial Intelligence Inspired Model Predictive Control for Frequency Regulation in Power Electronics Dominated Grids
- Resource Type
- Conference
- Authors
- Hosseinzadehtaher, Mohsen; Fard, Amin Y.; Shadmand, Mohammad B.; Fajri, Poria
- Source
- 2021 IEEE 12th International Symposium on Power Electronics for Distributed Generation Systems (PEDG) Power Electronics for Distributed Generation Systems (PEDG), 2021 IEEE 12th International Symposium on. :1-6 Jun, 2021
- Subject
- Components, Circuits, Devices and Systems
Power, Energy and Industry Applications
Transportation
Training data
Predictive models
Power electronics
Distributed power generation
Mathematical model
Voltage control
Artificial intelligence
Power electronic dominated grids
frequency regulation
model-based predictive control
- Language
- ISSN
- 2329-5767
This paper proposes an artificial intelligence (AI)based technique to realize event-triggered control solutions for power electronics dominated grid (PEDG). The proposed approach integrates the features of data-driven and model-based control schemes to realize a unified predictive control solution for enhancing frequency restoration in PEDG or an islanded cluster of the grid. The control scheme estimates the inertia of the grid during disturbances by AI techniques and provides a feedback for the controller to regulate the frequency of the grid. The proposed integrated data-driven and model-based predictive control (IDMPC) is implemented on power electronics interfaces of the distributed energy resources (DERs) at the grid-edge and enables the control of islanded grid-cluster frequency feature such as rate of change of frequency (ROCOF) and nadir frequency. The presented IDMPC scheme has fast dynamic response and is robust to system disturbances. The functionality of the proposed method for supporting the voltage and frequency of the grid is verified by several case studies.