Brain Emotional Learning-Based Intelligent Controller for Frequency Regulation of Uncertain Islanded Microgrid Considering Renewable Energy Sources
Bibliographic record
Abstract
In this paper, an intelligent brain emotional learning-based intelligent controller (BELBIC) technique is presented for the secondary frequency control of microgrid.The effects of renewable energy sources and load changes, power fluctuations and dynamic disturbances along with uncertainty affecting the microgrid frequency are all considered in the studied islanded microgrid.In the intelligent method for tuning and stabilizing the microgrid frequency, the learning of BELBIC technique is based on emotional factors and is able to adjust the microgrid frequency by including nonlinear terms and overcoming the effects of model uncertainty, disturbances, environmental changes and low inertia due to renewable energy sources.The behavior of the system against various changes and disturbances is investigated and compared with the optimal PID control methods.The advantages of the proposed method include low overshoot / undershoot, short settling time and minimization of frequency deviation.According to the simulation results obtained in different scenarios, the proposed control method shows good performance for network frequency stabilization.
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".