Critic-Based Self-Tuning PI Structure for Active and Reactive Power Control of VSCs in Microgrid Systems
Bibliographic record
Abstract
Traditional proportional integral (PI) control has been extensively used for power control of voltage source converters in microgrid systems. Previous studies show that fixed-gain PI controllers cannot easily adapt to power changes, disturbances, and parameters variation, especially in large microgrids; hence the need for continuous algorithms to adjust the controller gains over the transients cannot be neglected. In this paper, a novel online tuning algorithm for PI controllers is proposed and implemented in a microgrid system. In this algorithm, which is based on the neuro-dynamic programming concept, a fuzzy critic is employed to evaluate the credibility of the control system performance and provide an evaluation signal, which is then used in the gain-tuning process. The PI controller gains are updated in an optimization process based on steepest decent rule so that the evaluation signal produced by the critic is minimized. The developed control structure, which is named critic-based self-tuning PI controller, is tested in a microgrid system with different penetrations of distributed generators and operational scenarios. The simulation results verify that implementation of a heuristic gain-tuning algorithm results in a model-independent controller with increased adaptivity compared with conventional PI control. Furthermore, due to simple learning rules, the convergence time is significantly reduced and the transient response is improved. The proposed gain-tuning algorithm can also be applied to PI controllers in other applications of controllable systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".