Hybrid Adaptive Learning-Based Control for Grid-Forming Inverters: Real-Time Adaptive Voltage Regulation, Multi-Level Disturbance Rejection, and Lyapunov-Based Stability
Bibliographic record
Abstract
This paper proposes a Hybrid Adaptive Learning-Based Control (HALC) algorithm for voltage regulation in grid-forming inverters (GFIs), addressing the challenges posed by voltage sags and swells. The HALC algorithm integrates two key control strategies: Model Predictive Control (MPC) for short-term optimization, and reinforcement learning (RL) for long-term self-improvement for immediate response to grid disturbances. MPC is modeled to predict and adjust control actions based on short-term voltage fluctuations while RL continuously refines the inverter’s response by learning from historical grid conditions, enhancing overall system stability and resilience. The proposed multi-stage control framework is modeled based on a mathematical representation using a control feedback model with dynamic optimal control. To enhance voltage stability, Lyapunov is used to operate across different time scales: milliseconds for immediate response, seconds for short-term optimization, and minutes to hours for long-term learning. The HALC framework offers a scalable solution for dynamically improving voltage regulation, reducing power losses, and optimizing grid resilience over time. Simulation is conducted and the results are compared with other existing methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".