Efficient load frequency control in multi-source interconnected power systems using an innovative intelligent control framework
Bibliographic record
Abstract
The goal of this paper is to develop an innovative intelligent controller, called TID-IC, to improve the efficiency and stability of multi-area multi-source power systems. The paper represents the first integration of brain emotional learning with tilted integral derivative control in load frequency control applications, representing a revolutionary step towards intelligent power system management. The equilibrium optimizer is used to tune the parameters of the TID-IC. This optimization algorithm is chosen for its ability to navigate the TID-IC controller's complex parameter space, providing an appropriate balance between exploration and exploitation capabilities crucial for dynamic power system environments. The performance evaluation of the proposed controller focuses on a two-area multi-source interconnected power system. Within this system, each area includes conventional power generation units like thermal, gas, and hydraulic plants, alongside renewable energy sources such as wind and solar. Considering system nonlinearities, parameter uncertainties, physical constraints, communication time delays, load perturbations, and variations in renewable energy sources, simulation results demonstrate the effective management of the load frequency control problem by the proposed controller. The TID-IC's effectiveness and superiority are further supported by a comparison of its performance against several established control techniques. Our findings demonstrate the TID-IC controller's superior performance in reducing frequency deviations, improving system robustness, reducing performance index values, and offering better disturbance rejection capability, outperforming conventional PID and other advanced controllers in diverse operational scenarios. These achievements point to a promising application of the TID-IC in real-world multi-source interconnected power systems, representing significant progress in managing the complexities and uncertainties inherent in modern energy grids. Consequently, this research contributes to the development of more reliable and efficient energy grids, vital for integrating renewable energy sources and meeting the growing demands for sustainable power solutions.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".