Design of Distributed State Estimation for Droop-Controlled Islanded Microgrids
Bibliographic record
Abstract
This paper proposes a distributed algorithm for the dynamic state estimation of droop-controlled Islanded Microgrids (IMGs). In this regard, the IMG is clustered into a number of zones. Each zone has its own dynamic state estimation unit called Zone State Estimator (ZSE). Each ZSE updates the states of its zone using local measurements and exchange of information with adjacent zones. The proposed distributed state estimation approach adopts the particle filter state estimation technique in order to obtain the state estimation process in a distributed environment. The objective of the proposed design of distributed state estimation is to study the impacts of selecting the number and boundaries of zones on the performance of the proposed distributed state estimation algorithm. Case studies are simulated to evaluate the effectiveness of the proposed algorithm under different operating conditions. Monte Carlo simulation method was applied to verify the accuracy and robustness of the proposed distributed state estimator on tracking the ground truth.
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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".