Deep Reinforcement Learning-Based Governor for Pumped Storage Hydropower
Why this work is in the frame
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Bibliographic record
Abstract
To tackle the geographic drawbacks of pumped storage hydropower (PSH) plants, they often employ the use of closed-loop reservoirs.This reservoir setup always experiences changes in its net head while operating.The conventional proportional, integral, and derivative (PID) controller of the governor is optimized to handle a fixed system and is unable to handle the changing system dynamics due to the change in the net head of the turbine.Current approaches to tackle this include tuning and retuning the PID parameters or employing adaptive control strategies.This paper proposes the use of deep deterministic policy gradient (DDPG) to train an agent in place of the PID controller in the governor of a Pumped Storage Hydropower plant.The DDPG agent observes the state of the net available head and the deviation from reference speed to successfully track the optimal reference for the turbine by controlling the turbine's gate through the servomotor.
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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 | 0.001 |
| 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 it