Adaptive harmonic-based protection coordination for inverter-dominated isolated microgrids considering N-1 contingency
Bibliographic record
Abstract
Protection coordination is typically approached considering the main network configuration. Nevertheless, contingencies such as generator or line failures may disrupt power grids. Low fault currents in inverter-based isolated microgrids adversely impact the conventional overcurrent protection schemes. This paper proposes a sensitive and selective protection scheme for isolated microgrids using an adaptive third harmonic voltage generated by inverter-based distributed generators (IBDGs) and adapted to fault severity. The generated harmonic voltage results in a harmonic layer established during faults and is decoupled from the fundamental fault current, which is limited by IBDGs. Harmonic directional overcurrent relays sense the generated harmonic currents and voltages at the relay location to ensure optimal protection coordination (OPC). The OPC problem is formulated as a constrained nonlinear program to determine the optimal relays’ settings. The constraints are defined for the isolated configuration and each potential configuration resulting from an N-1 contingency. A radial microgrid that is part of a Canadian urban distribution network is used to ensure the successful operation of the proposed protection scheme. The results demonstrate the ability of the proposed scheme to protect isolated microgrids and preserve protection coordination without communication.
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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) | 0.001 | 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".