Power management supervisory control algorithm for standalone wind energy systems
Bibliographic record
Abstract
Being natural and renewable, the wind is a valuable energy resource. Standalone off-grid wind systems cannot simply extract the maximum power at all times due to energy storage limitations. As a result of the limited energy storage, wasted surplus wind power is directed to an energy `dump' known as a `dummy load' and the power is dissipated as heat. Therefore, the challenge for standalone wind systems is to efficiently extract the maximum power from the wind when necessary, and reduce the energy extraction - as dictated by the energy storage mechanism - to minimize surplus of energy. By regulating the power output as necessary, the amount of wasted energy and heat dissipation requirements of the energy dump mechanism will be reduced. This paper proposes a power management supervisory controller that autonomously switches between an adaptive MPPT mode and a power limit search (PLS) mode to regulate the wind energy extraction. MPPT is used when the wind speeds result in maximum power levels that are lower than the desired power level. The memory based adaptive MPPT uses the internally captured atmospheric information to detect wind speed change and extract an equivalent of the turbine's tip speed ratio (TSR) parameter. The PLS is used whenever the power level exceeds the desired power and the MPPT is activated when the wind speed has decreased and no surplus power is detected. The functionality of the power management controller has been verified through simulation. The controller was able to successfully regulated the output power and exhibited smooth transitions between the MPPT and PLS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".