Field tests validation of a mechanical sensor-less MPPT control strategy for tidal turbines
Bibliographic record
Abstract
The main goal of this paper is to present the experimental evaluation of a novel mechanical sensor-less MPPT control strategy for tidal stream turbines via field tests in a relevant environment. Fluctuations in mechanical loads and generated power, caused by surface waves or turbulence in the water column, pose significant challenges for power conditioning and control systems of instream turbines. In the present work, a simple and robust Maximum Power Point Tracking (MPPT) control method based on an optimal linear relationship between the current and the square of the voltage of the generator outputs is proposed. The MPPT control strategy was developed by a digital model and then implemented on the 1.5 m diameter Tidal Turbine Testing (TTT) device developed at the Queen's University Belfast (QUB). System validation was performed at the highly energetic QUB tidal test site in the Strangford Narrows, Northern Ireland. Turbine performance results by the proposed methodology were compared with two conventional control strategies: constant speed (RPM) and constant torque control. Field testing in the unsteady inflow environment allowed to investigate hydrodynamic power and Power Take-Off response to the adopted control strategy. The performance of the MPPT control strategy was able to maximize the power coefficient of the turbine and maintain the turbine operation close to its optimal Tip Speed Ratio (TSR) under fluctuations in the stream flow, with improved performance compared to conventional control strategies.
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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) | 0.002 | 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".