Digital Real-Time Harmonic Estimator for Power Converters in Future Micro-Grids
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One of the main challenges in future micro-grids is the presence of transients and fluctuations inevitably imposed by nonlinear loads. Therefore, real time identification of transients and fluctuations of the grid is essential for the future micro-grids. This identification can be used in different applications of “grid-connected” and “island” micro-grid such as grid synchronization, harmonic current injection for islanded mode micro-grids, etc. This paper focuses on developing a method to detect and monitor the disturbances and fluctuations in the utility grid. A novel real-time harmonic estimation technique is presented, which is able to quickly and precisely estimate dc component, and synchronous and asynchronous harmonic contents of the grid voltage despite of their unknown nature. The proposed technique can be used in the control system of power converters to potentially enhance the reliability and resiliency of future micro-grids. As a case study, the performance of the proposed harmonic estimator is evaluated in grid synchronization of the power converters. Simulation and experimental results from the case study show the ability of the converter to inject a smooth sinusoidal current in presence of disturbance and fluctuations in the grid voltage. These results verify the fast dynamic and accuracy of the proposed harmonic estimation technique.
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.
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 it