A connectionist based approach for reducing the number of voltage sensors in modular multilevel converter
Bibliographic record
Abstract
Intensive research is being directed to renewable energy due to their significant features. The converter is the core of the renewable energy system. Recently, a converter topology called Modular Multilevel Converter (MMC) has been introduced whose main challenge is the balance of its Submodules (SMs) capacitors voltages. Hence, a voltage sensor is required for each SM to measure its capacitor voltage; which increase the system cost. Recently, soft computing techniques have been successfully applied as regression tools to deal with many challenges characterized by complex dynamical representations. In this paper, a soft computing approach based on connectionist modeling (artificial neural networks) is used to predict the SM capacitor voltages. Moreover, each arm employs only one voltage sensor to reduce the system cost; whose function is to measure the output voltage of a set of a series connected SMs and updates the predictors when there is only one SM activated within the set.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".