Embedded fault location in DC microgrid systems based on a Lock-In Amplifier
Bibliographic record
Abstract
Advancements in microgrids and distributed generated systems have created an impetus to distribute protection and management functions throughout a grid. Instead of using a centralized scheduling and protection system, each converter in the grid is expected to contribute to these functions. Many techniques based on impedance detection have been proposed as means to locate faults on a microgrid; however, many of them are not suitable or are too intrusive for a DC microgrid. An accurate detection of the grid impedance (both in magnitude and phase) allows for the accurate detection of the fault, and reduces the effort needed to clear it, in the event that it is necessary to do so. In this work, a novel fault location technique based on a Lock-In Amplifier (LIA) is introduced. This technique makes use of advanced digital algorithms to allow for the accurate detection of the fault location with minimal perturbation of the system's operations. By using both the magnitude and phase of the impedance, the algorithm is able to determine a location based on resistance and inductance, thereby mitigating the effects of errors from any single source. The proposed technique provides three key benefits: 1) high noise immunity, 2) low computational cost, and 3) low perturbation size; moreover, it provides these benefits while also maintaining a high level of accuracy. Simulations of the proposed measurement technique are presented in order to illustrate its behavior, along with experimental validation under different faults.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".