Voltage Regulation in Grid-Forming Inverters: A State Observer-Driven Deep Reinforcement Learning Approach
Bibliographic record
Abstract
Modern power systems increasingly rely on grid-forming inverters (GFI) to ensure reliable and stable voltage regulation. This paper presents a novel voltage control strategy that integrates GFI with a State Observer and a Deep Reinforcement Learning (DRL) framework. First, the GFI is modeled using a state observer within a feedback control loop. The state observer estimates key grid parameters such as voltage, current, and frequency even under conditions of partial or noisy measurements, enabling robust and accurate state feedback. Next, a mathematical model is developed that combines the strengths of DRL and the state observer. DRL contributes by learning optimal control policies through continuous interaction with the environment, allowing the system to adapt and make intelligent decisions under dynamic grid conditions. The key finding of this research is that the integrated system achieves highly accurate voltage regulation, enhanced transient stability, and strong resilience to disturbances. Additionally, the proposed method reduces steady-state error, improves disturbance rejection, and minimizes response time. Simulation results validate the superior performance of the proposed system compared to conventional control approaches, underscoring its potential to enhance smart grid applications and ensure grid reliability in future energy networks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".