Harmonic Dual-Setting Directional Overcurrent Protection for Inverter-Based Islanded Microgrids
Bibliographic record
Abstract
Limited fault currents in inverter-interfaced islanded microgrids impose immense challenges on conventional overcurrent protection schemes. This paper proposes a sensitive and selective protection scheme for islanded microgrids using a third harmonic voltage generated by inverter-interfaced distributed generators (IIDGs). The generated harmonic voltage results in a harmonic layer formed during short-circuit faults and is decoupled from the fundamental fault current, i.e., limited by IIDGs. Further, the generated harmonic voltage is adaptively adjusted based on fault severity to enhance protection sensitivity and obtain a universal set of relays’ settings. The proposed protection scheme utilizes harmonic directional overcurrent relays (HDOCRs) equipped with a dual-setting time-current-voltage setting that sense the generated harmonic voltages and currents at the relay location to ensure optimal protection coordination (OPC) of islanded microgrids. The OPC with the proposed dual-setting is formulated as a constrained nonlinear program to determine the optimal forward and reverse relays’ settings. The proposed scheme is tested on the Canadian benchmark urban distribution system and compared to the conventional protection scheme, which relies only on a single time-current-voltage trip characteristic. The results ensure the ability of the proposed scheme to protect islanded microgrids without communication and its capability to reduce relays’ operation times.
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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 | 0.000 |
| 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) | 0.001 | 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".