A Critical Review on Smart Control Techniques for Load Frequency Control in an Interconnected Power System
Bibliographic record
Abstract
Load Frequency Control (LFC) is a critical aspect of power system control that ensures the balance between the generation and load demand.The demand for efficient LFC has increased because to the growing integration of dispersed power and renewable energy sources.Smart control techniques have emerged as a promising solution to enhance the performance of LFC in an interconnected power system.Despite the fact that various studies and approaches on load frequency control have been presented previously, no research concentrated on reviewing the approaches and limitations in the control techniques for load frequency control.Hence, this review paper presents a comprehensive overview of the recent developments in smart control techniques for LFC, focusing on Energy Storage Systems (ESS), conventional controllers, filters, optimization techniques, Machine Learning (ML)/Deep Learning (DL) techniques, and Deep Reinforcement Learning (DRL).The advantages and limitations of each technique are discussed, and a comparison of their performance is presented.The review also highlights the future research directions and challenges in implementing smart control techniques for LFC in an interconnected power system and provides some suggestions for further improvements to be done in the future for better load frequency control in an interconnected power system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".