Low-frequency Disturbance Injection for Active Islanding Detection of Multiple Electronically-interfaced Distributed Generation Units
Bibliographic record
Abstract
This thesis proposes and evaluates the application of a low-frequency disturbance injection, as an active islanding detection method, in a microgrid with multiple electronically-interfaced Distributed Generation (DG) units. Each DG unit is interfaced to the microgrid through a two-level Voltage-Sourced Converter (VSC). The low-frequency disturbance signal for islanding detection is injected through the q-axis control of each VSC unit. The low-frequency signal is at 1 Hz with an amplitude of up to 2.5 % of the rated VA of the VSC unit and augments the reference signal of the q-axis control.\n\nThe effectiveness of the low-frequency injection for islanding detection is examined under two distinct VSC control scenarios. In the first scenario, each VSC only injects pre-determined real- and reactive-power components in the system and does not participate in frequency/voltage control. In the second scenario, the VSC controls are also equipped with frequency/real-power and voltage/reactive-power droop characteristics and thus share power and participate in frequency and voltage control of the microgrid, specifically in the islanded mode.\n\nThe investigations reported in this thesis show that the proposed islanding detection method can effectively detect an islanding event under both VSC control strategies, subject to the conditions that UL and/or IEEE anti-islanding standards impose. The studies show that an islanding event can be detected within 536 ms subsequent to the instant of islanding.\n\nAs part of this thesis, an eigen analysis software tool has been developed that can systematically investigate the impact of low-frequency disturbance injection on the small-signal stability and dynamic performance of the microgrid, prior and subsequent to an islanding event.\n\nThis thesis concludes that the low-frequency disturbance injection-based method can be successfully applied to a multi-DG system, since (i) islanding detection is achieved within applicable standards requirements by all DG units in the system, and (ii) the low-frequency disturbance injection signal has no noticeable impact on the dynamics nor the small-signal stability of the system if its magnitude is kept below a pre specified limit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".