Intelligent Control of Grid-Connected Microgrids: An Adaptive Critic-Based Approach
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Bibliographic record
Abstract
This paper presents an adaptive and intelligent power control approach for microgrid systems in the grid-connected operation mode. The proposed critic-based adaptive control system contains a neuro-fuzzy controller and a fuzzy critic agent. The fuzzy critic agent employs a reinforcement learning algorithm based on neuro-dynamic programming. The system feedback is made available to the critic agent's input as the controller's action in the previous state. The evaluation or reinforcement signal produced by the critic agent together with the back-propagation of error is then used for online tuning of the output layer weights of the neuro-fuzzy controller. The proposed controller shows superior results compared with the traditional PI control. The transient response time is significantly reduced, power oscillations are eliminated, and fast convergence is achieved. The simple design and improved dynamic behavior of the proposed controller make it a promising nominee for power control of microgrid systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it