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Improving frequency stability in grid-forming inverters with adaptive model predictive control and novel COA-jDE
Muhammad Zubair Yameen1,2, Zhigang Lu3,4, Fayez F M El-Sousy5
1School of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, China. zubbairyamin@gmail.com.
Scientific Reports
|May 13, 2025
Summary
Adaptive Model Predictive Control (AMPC) enhances grid-forming inverter (GFI) performance in low-inertia power systems. This advanced control strategy ensures robust frequency stability by adaptively adjusting virtual inertia and damping, outperforming traditional methods.
Area of Science:
- Electrical Engineering
- Renewable Energy Systems
- Control Theory
Background:
- Low-inertia power systems with increasing renewable energy integration require advanced control for grid-forming inverters (GFIs).
- Conventional Model Predictive Control (MPC) struggles with dynamic grid conditions and frequency stability due to reliance on static models.
- Virtual Synchronous Machine (VSM) mode is crucial for GFI operation but faces challenges in adapting to grid uncertainties.
Purpose of the Study:
- To develop an Adaptive Model Predictive Control (AMPC) framework for enhancing GFM performance in VSM mode.
- To ensure robust frequency stability in low-inertia power systems under dynamic and uncertain grid conditions.
- To address the limitations of traditional MPC in adapting to rapidly changing grid environments.
Main Methods:
- Implemented an AMPC framework combining offline reinforcement learning with online MPC utilizing soft constraints.
- Employed a novel Hybrid Crayfish Optimization and Self-Adaptive Differential Evolution Algorithm (COA-jDE) for offline cost function minimization and optimal parameter (Q, R) derivation.
- Utilized simulations on a 16MW wind-powered DFIG microgrid to evaluate the AMPC framework's performance.
Main Results:
- AMPC demonstrated superior performance compared to traditional MPC and VSM methods during grid disturbances, faults, islanding, and load shifts.
- The AMPC framework achieved adaptive adjustment of virtual inertia and damping, enhancing GFM performance.
- Simulations confirmed the computational efficiency of AMPC due to restricted offline tuning.
Conclusions:
- The proposed AMPC framework offers a flexible and resilient control strategy for modern low-inertia grids.
- AMPC significantly improves frequency stability and compliance with grid codes (e.g., GC0137, IEEE 1547).
- This adaptive control approach is vital for the reliable integration of renewable energy sources.
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