An Optimization-based Load Frequency Control in an Interconnected Multi-Area Power System Using Linear Quadratic Gaussian Tuned via PSO
Bibliographic record
Abstract
Mismatch between the generation and consumption results in deviation in the frequency of the power system, which negatively influences its operation, reliability, and efficiency. Secondary/load frequency controllers are used for compensating the power mismatch in the time-scale of up to several minutes. The Linear Quadratic Gaussian (LQG) control has been applied for regulating the frequency. However, the parameters in the LQG method are conventionally determined using trial and error methods. This makes the selection process challenging for large power system and cannot guarantee satisfactory response. In this paper, an optimal load frequency control (LFC) method is proposed where the Particle Swarm Optimization (PSO) method is exploited to optimize the selection of LQG parameters for a multi-area system. The performance of the proposed LQG+PSO method is verified on a test-bench three-area system using simulations. It is demonstrated that the proposed LQG+PSO method achieves superior frequency regulation compared to the conventional LQG method.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".