Model Predictive Control of Hybrid Active Filter integrated with high power low frequency VSCs
Bibliographic record
Abstract
This paper presents an innovative control method based on Finite Set Model Predictive Control (FS-MPC) to integrate a Hybrid Active Power Filter (HAPF) into the output filter of high-power grid-connected converters. The proposed method aims to effectively mitigate current harmonics at the grid side across a wide range of grid inductance. Importantly, this method allows for the seamless installation of HAPF on existing converters, enabling a decrease in the switching frequency of the main converter without requiring communication between the two converters. The current reference generation relies on prediction, eliminating the need for low Signal-to-Noise Ratio (SNR) current sensors to extract harmonic content of the main converter’s current. Additionally, this approach eliminates the delay introduced by notch and band-pass filters by predicting the next step current instead of measurement or filtering, thereby reducing the required sampling frequency for MPC methods. Real-time simulations have been conducted on the RTDS platform to demonstrate the effectiveness of the proposed method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".