Artificial Intelligence Based Control Strategy of a Three-Phase Neutral-Point Clamped Back-to-Back Power Converter with Ensured Power Quality for WECS
Bibliographic record
Abstract
This paper provides an in-depth investigation into the state-of-the-art power converter control approaches utilizing artificial intelligence that ensure the power quality for wind energy conversion systems (WECS). The most promising and feasible wind energy conversion configuration has been elected to be evaluated in an attempt to reduce the computing cost and time, as well as meet the grid code requirements. For this purpose, in this work, a back-to-back neutral-point clamped power converter is controlled with high precision using a machine learning algorithm. The machine is trained offline by data acquired from the wellknown voltage-oriented control (VOC) technique. The majority of the computational load is moved from online to offline mode. Thus, there is no need for accurate development of the PI controller parameters and bandwidth, and hence, the cost and time of calculation will be considerably reduced. As a consequence, the recommended machine learning-based technique can take over the conventional VOC responsibilities. To accomplish this, the training dataset is applied to learn the behavior of the system using a locally weighted lasso regression approach. The cost function is then minimized using stochastic gradient descent, batch gradient descent, Broyden-Fletcher-Goldfarb-Shanno (BFGS), and limited-memory BFGS optimizers, successively. The comparative analysis reveals that the BFGS family of optimizers outperforms the counterparts in terms of computation time, accuracy, and THD performance of WECS.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".