Adaptive Discrete-Time Grid-Voltage Sensorless Interfacing Scheme for Grid-Connected DG-Inverters Based on Neural-Network Identification and Deadbeat Current Regulation
Bibliographic record
Abstract
This paper presents an adaptive discrete-time grid-voltage sensorless interfacing scheme for grid-connected distributed generation inverters, based on neural network identification and deadbeat current regulation. First, a novel neural network-based estimation unit is designed with low computational demand to estimate, in real-time, the interfacing parameters and the grid voltage vector simultaneously. A reliable solution to the present nonlinear estimation problem is presented by combining a neural network interfacing-parameters identifier with a neural network grid-voltage estimator. Second, an adaptive deadbeat current controller is designed with high bandwidth characteristics by adopting a delay compensation method. The delay compensation method utilizes the predictive nature of the estimated quantities to compensate for total system delays and to enable real-time design of the deadbeat controller. Third, the estimated grid voltage is utilized to realize a grid-voltage sensorless average-power control loop, which guarantees high power quality injection. Theoretical analysis and comparative evaluation results are presented to demonstrate the effectiveness of the proposed control scheme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".