Metaheuristic-Based Optimization of Nonlinear PI Controllers for Maximum Power Extraction in PV Systems
Bibliographic record
Abstract
Despite the promising potential of photovoltaic energy and its wide range of uses, it still has shortcomings today, mainly due to its highly sensitive nature to environmental factors, resulting in low efficiency and energy loss.Therefore, the implementation of a robust control strategy for photovoltaic systems becomes essential in order to efficiently track the maximum power point (MPP) and deliver the best possible performance.This study proposes the implementation of a non-linear Proportional-Integral (NPI) controller in a PV system with a resistive load.The NPI controller is designed by integrating a non-linear gain function based on Popov's stability criterion into the classical PI structure, aligning with the nonlinear characteristics of PV systems.Furthermore, intelligent control techniques, in particular these metaheuristic algorithms: Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Grey Wolf optimizer (GWO), were utilized to finetune both non-linear PI and classic PI controllers.The performance of the proposed approach is assessed using key metrics such as Mean Square Error (MSE), overshoot, settling time, and efficiency, demonstrating its effectiveness in enhancing PV system operation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".