Current Injection for Active Islanding Detection of Electronically-Interfaced Distributed Resources
Bibliographic record
Abstract
This paper presents an active islanding detection method for electronically-interfaced distributed resource units at the distribution voltage level. The proposed method is based on injecting a disturbance signal into the system through either the direct axis (d-axis) or the quadrature axis (q-axis) current controllers of the interface voltage-sourced converter. Signal injection through the d-axis controller modulates the amplitude of the voltage at the point of common coupling (PCC), whereas signal injection through the q-axis controller causes a frequency deviation at PCC, under islanded conditions. Monitoring strategies to detect islanding are also presented for the proposed injection method. The feasibility of the proposed method is evaluated under the UL1741 anti-islanding test configuration. The studies reported in this paper are based on time-domain simulations in the PSCAD/EMTDC environment. The studies show that the proposed islanding detection method succeeds in detecting the islanding phenomenon as fast as 33.3 ms for the parameter setting of the test system, and always meets the two-second UL detection requirement. The paper also concludes that the proposed method has the salient feature of offering a potential application in a micro-grid scenario, where fast "islanding detection" and not necessarily the "anti-islanding function" is required.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".