Fuzzy Logic Scheduling of the Duty Cycle Perturbation for Optimized MPPT Controller of PV/Wind Hybrid System
Bibliographic record
Abstract
Efficient hybrid PV/Wind energy generation is a challenge against fluctuating solar and wind speed conditions. The paper aims to analyze and improve the performance of an optimized and restructured hill-climbing Maximum Power Point Tracking (MPPT) method, called dP-P&O (Perturb and Observe), for fast-changing environmental conditions of a Hybrid PV/Wind Energy Conversion System (HPVWECS). In the first part of the paper, this technique is restructured and adapted for application in PV Systems (PVS) and Wind Energy Conversion Systems (WECS) where the duty cycle is the control action instead of the reference voltage. The experimental implementation of this technique, for a developed HPVWECS emulator, shows the performance limitation of this technique. To overcome these drawbacks, a proposed method simplified the schemes of the algorithm by considering the novel optimized dP-P&O scheme only, with the integration of a fuzzy logic scheduling controller for the duty cycle perturbation step size based on the power change and the previous duty cycle variation. The proposed MPPT controller is tested in the HPVWECS emulator experimental test bench, to evaluate its performance and robustness. The experimental results prove that the second proposed approach gives higher precision, which leads to an ameliorated energy quality and better performance and robustness, compared to the novel hybrid dP-P&O algorithm (first proposed approach), against different solar and wind environmental conditions and load change.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".