Suggested Smart Adaptive Load Shedding of an Islanded Microgrid Containing Renewable Resources
Bibliographic record
Abstract
In recent years, long-duration outages which have happened globally have challenged the reliability of typical recovery methods and system frequency protections that are used in order to prevent these outages. In order to handle this problem and improve the performance of methods such as under-frequency load shedding to maintain system frequency, there is a need to revise existing methods fundamentally to perform accurate and fast load shedding to prevent through outage of power system. Adaptive methods, due to their tolerance and flexibility against complicated non-linear systems and also high reliability, can be appropriate options for handling this problem. This paper presents an adaptive load shedding algorithm by applying modifications in adaptive load shedding methods and combining it with metaheuristic Imperialist Competitive Algorithm (ICA). The presented algorithm improves the reliability of older methods and decreases the probability of system collapse by decreasing the convergence time of previous algorithm and improving the system frequency drop. Besides, in order to verify and prove the advantages of the presented method comparing to previous methods, these methods are simulated in PSCAD/EMTDC connected to MATLAB and are compared to each other.
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 | 0.000 |
| 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".