Online Grid Support Inverter Parameters Identification Using Extended Kalman Filters
Bibliographic record
Abstract
Given the increasing integration of renewable energy sources into existing grids, power injection using Grid Supporting Inverters (GSIs) is gaining in popularity. This paper addresses the issue of dynamically identifying key parameters of a GSI used for power injection into a microgrid. It is shown that the dynamics of the system is based on two essential parts that can be assimilated to simple first-order filters: The DC-bus and AC-line filtering. A simple implementation of an Extended Kalman Filter (EKF) used for estimating in real-time both filter's output and key parameters in this noisy environment is proposed. The design was tested using a DSP-accurate implementation using the Matlab/Simulink environment and presented results show that predefined AC-line filter's parameters were successfully retrieved as the state of the system. The proposed design is suitable for progressive failure indication.
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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".