Energy-Efficient Predictive Control and Reinforcement Learning Agent for Power Transformers Cooling Systems
Bibliographic record
Abstract
The dissipation of the heat duty induced by energy conversion in power transformers relies on coolants and actuation devices (fans, pumps) which combination showcases various cooling system types. The operation of the cooling actuation devices comes with the cost of increased energy consumption, layered-up with operation and regulation-related constraints. To cope with these issues, this paper introduces a concurrent cooling control strategy where an optimum load-dependent Model Predictive Controller (MPC) competes with a model-free Reinforcement Learning (RL) agent to devise a cost-effective course of actuation plan for the coolers. Performance evaluations conducted on a triple rated mineral oil-filled transformer cooling system reveal the similarity of both control schemes with respect to the established operation reference being tracked, and their ability to induce energy saving. Among the conclusions are the feasibility and great potential of the use of an RL agent to circumvent the need to design an accurate and computational-intensive optimal cooling control model.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".