Ant-based optimal tuning of PID controllers for load frequency control in power systems
Bibliographic record
Abstract
Frequency fluctuations in power system result from high penetration of distributed generation as well as sudden load changes, system uncertainties, and parameters variations. If adequate control actions are not put into place, the fluctuations in power system frequency may deteriorate the normal operation of the system. This paper proposes a robust, intelligent control technique using Ant Colony Optimization algorithms for optimal tuning of proportional, integral and derivative controllers. The goal is to enhance load frequency control capabilities in smart power systems. The designed algorithm is applied to a power system consisting of a coal thermal plant, photovoltaic power generation as a renewable energy source, as well as heat pump water heaters and electric vehicles as controllable loads. Simulation results obtained using Matlab under various practical operating conditions confirm the correctness of system analysis and superior performance of the proposed scheme. The results of the simulation illustrate that the system with the proposed control scheme is more stable, and can achieve a fast response in the face of system uncertainties, parameter variations and fluctuations from distributed energy sources, as compared to the conventional PID controller and the model predictive control scheme.
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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 | 0.000 | 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".