Dropout Kolmogorov–Arnold Networks: A Novel Data-Driven Impedance Modeling Approach for Voltage-Source Converters
Bibliographic record
Abstract
The extensive integration of voltage-source converters (VSCs) as interfaces for renewable energy sources in power systems increases stability concerns and demands accurate VSC impedance models to characterize grid-converter interactions at various operating points. However, analytical impedance models require detailed knowledge of the VSC parameters, which are frequently inaccessible due to manufacturer confidentiality. Further, existing neural network data-driven VSC impedance identification methods adopt conventional multi-layer perceptrons, yielding complex models and demanding abundant high-quality data. This paper presents a data-driven VSC impedance identification method using Dropout Kolmogorov-Arnold Networks (DropKANs) to address these challenges effectively. The hyperparameters of the proposed DropKAN model are optimized using Optuna, outperforming the Scikit-learn, Hyperopt, and GPyOpt optimizers, and the training is optimized using the Adam optimizer and compared with Nadam and RMSprop. Comprehensive and comparative evaluation tests showed 1) the superiority of the proposed DropKAN model over the feedforward neural network, long short-term memory, and KAN models in terms of accuracy, training and prediction times, and neural network structure simplicity, even with a 50% reduction in the training data size, and 2) the versatility and robustness of the proposed DropKAN model when applied to a different VSC system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".