On-Policy Machine Learning Based-Disturbance Rejection Control for Grid-Tied PEC9 Inverter Under Parameters Mismatch and Distorted Grid Voltage
Bibliographic record
Abstract
Thanks to higher power quality and performance efficiency, multilevel grid-tied inverters are the right choice for DC-to-AC conversion like the PV systems to the main power grid. However, the complexity of controlling the switching devices and capacitor voltages in these inverters presents significant stability challenges, particularly during grid-tied operation and when dealing with parameter mismatches. This paper proposes an optimized adaptive Active Disturbance Rejection Controller (ADRC) to stabilize the current of the grid-tied PEC9, serving as a multilevel inverter for PV applications. For this purpose, the PV system, connected to PEC9 as a main DC source to be integrated into the grid. The tunable coefficients of the ADRC controller are automatically adjusted using the on-policy reinforcement learning (RL) technique to effectively stabilize the grid-tied PEC9 with a PV inverter. In this approach, a reward function tailored to the inverter requirements guides the RL-agent in determining the optimal policy. Through maximizing the reward signal, the on-policy algorithm generates regulatory signals to adjust control gains accordingly. A laboratory prototype of PEC9 inverter is constructed by implementing OPAL-RT simulator to investigate the feasibility and applicability of suggested adaptive data-driven scheme. The experimental responses of grid-tied PEC9 equipped with the proposed adaptive ADRC demonstrate the effective performance under various operating conditions of grid-tied PV inverters, including change in the system’s references and parameter mismatches.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".