Improving frequency stability in grid-forming inverters with adaptive model predictive control and novel COA-jDE optimized reinforcement learning

The increasing utilization of renewable energy sources in low-inertia power systems demands advanced control strategies for grid-forming inverters (GFMs). Conventional Model Predictive Control (MPC) methods, which depend on static models and predefined boundaries, often struggle to preserve frequenc...

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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 16540 - 31
Main Authors: Yameen, Muhammad Zubair, Lu, Zhigang, El-Sousy, Fayez F. M., Younis, Waqar, Zardari, Baqar Ali, Junejo, Abdul Khalique
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 13.05.2025
Nature Publishing Group
Nature Portfolio
Subjects:
639/166
639/4077
Alternative energy
Alternative energy sources
Compliance
Crayfish Optimization algorithm
DFIG
Electrical engineering
Energy resources
Energy storage
Evolution
Flexibility
Frequency
Humanities and Social Sciences
Learning
Load
Microgrid
Model Predictive Control
multidisciplinary
Optimization algorithms
Parameter estimation
Reinforcement
Renewable energy sources
Renewable resources
Science
Science (multidisciplinary)
Virtual Synchronous Machine
Virtual Synchronous Machine
Crayfish Optimization algorithm
Microgrid
Differential Evolution algorithm
Frequency
Model Predictive Control
DFIG
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:The increasing utilization of renewable energy sources in low-inertia power systems demands advanced control strategies for grid-forming inverters (GFMs). Conventional Model Predictive Control (MPC) methods, which depend on static models and predefined boundaries, often struggle to preserve frequency stability in dynamic grid conditions. This research presents an Adaptive Model Predictive Control (AMPC) framework to enhance GFM performance in Virtual Synchronous Machine (VSM) mode, ensuring robust frequency stability under uncertainties. The primary issue addressed is the inefficiency of traditional MPC in adapting to dynamic grid conditions. To resolve this, the AMPC framework combines offline reinforcement learning for parameter tuning with online MPC using soft constraints. The offline phase employs a novel Hybrid Crayfish Optimization and Self-Adaptive Differential Evolution Algorithm (COA-jDE) to minimize the cost function , deriving optimal control parameters ( Q , R ) before real-time deployment. This process, termed cost function minimization using COA-jDE in a reinforcement learning framework, enhances GFM performance by adaptively adjusting virtual inertia and damping. Simulations on a 16MW wind-powered DFIG microgrid demonstrate that AMPC outperforms traditional MPC and VSM methods during grid disturbances, symmetrical faults, islanding, and load shifts. Furthermore, AMPC is computationally efficient compared to conventional reinforcement learning techniques, as adaptation is restricted to offline tuning. The framework not only improves compliance with grid codes (e.g., GC0137, IEEE 1547) but also provides a flexible, resilient control strategy for modern low-inertia grids.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-00896-5