Fossa Optimization Technique based MPPT of PV System under Partial Shading Conditions
Bibliographic record
Abstract
Electricity generation from Photovoltaic (PV) panels for various applications is increasing day by day. Many technologies are introduced and implemented recently on PV panels or PV systems. Among many Maximum Power Point Tracking (MPPT) strategies are needs to be improving to harvest more energy during Partial Shading Conditions (PSCs). Due to existence of multiple peaks during the operation of PV system under PSCs, an optimization method must be utilized for identifying the global maximum among all existing peaks. Efficient optimization methods are introducing day by day based on their ability to track the objective function accurately with less tracking time. Among them, Fossa Optimization Algorithm (FOA) is recently introduced to solve many engineering problems. The FOA method is implemented in this paper on PV system to track MPPT location under PSCs. A double loop control method on boost converter is developed with the help of Fractional-Order Proportional-Integral-Derivative (FOPID) controller to track the maximum power quickly and efficiently. A six arrays connected PV system is considered in this paper for making the analysis on the proposed methodology. The proposed FOA based MPPT methodology is compared with existing methodologies: Modified Invasive Weed Optimization Method (MIWOM), Whale Optimization Method (WOM), Osprey Optimization Method (OOM), Hippopotamus Optimization Method (HOM) and Botox Optimization Method (BOM). The Hardware – in the – Loop (HIL) based results are presented to demonstrate various results on OPAL-RT platform.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".