Testing a Unit Commitment Based Controller for Grid-Connected PMG-Based WECSs With Generator-Charged Battery Units
Bibliographic record
Abstract
This paper presents the performance of a controller for adjusting the power generated by the permanent magnet generator (PMG) in a wind energy conversion system (WECS), which has generated-charged battery units (BUs). The proposed controller is based on the Lagrangian relaxation unit commitment (LRUC) method to determine a command value for the PMG electromagnetic torque (Te*) at each wind speed. The determined value of Te* is set to calculate command values for q-axis currents that are required by the controllers operating the generator-side and charging power electronic converters (PECs). The performance of the LRUC-based controller is experimentally evaluated for a grid connected 7.5-kW PMG-based WECSs with 4.44-kW BUs, when operated under various wind speeds and/or charging and discharging modes of the BUs. Performance results show that the proposed controller is capable of initiating fast and accurate responses to adjust the power generation of the PMG to meet the demands of the generator-side and charging PECs for different wind speeds, and/or charging and discharging of the BUs. These performance features are found to have minor sensitivity to the changes in wind speed, and/or levels of charging the BUs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".