A new MPPT method for photovoltaic systems used for hydrogen production
Bibliographic record
Abstract
Purpose In most maximum power point tracking (MPPT) methods described in the literature, the optimal operating point of the PV systems is estimated by linear approximations. These approximations can reduce considerably the performances of the PV systems. This paper seeks to provide comparative analyses of different MPPT methods used in photovoltaic (PV) systems and proposes a new approach that uses a nonlinear expression of the optimal voltage in combination with perturbation and observation (P&O) methods. Design/methodology/approach First, an analytical model for determining the nonlinear PV optimal operating point is detailed and each equation is explained. Second, a combination of the new method with P&O method is proposed to reduce the PV losses. Findings The simulation results showed that the approach improves clearly the tracking efficiency of the maximum power available at the PV modules output. The implementation of this new method will improve PV systems energy production rate and its long‐term storage in hydrogen form. Practical implications The simulation results showed that the new approach improves the MPP's tracking efficiency of the PV system on average at 92 percent. The implementation of the developed approach in a PV system with hydrogen storage increased the energy transfer from PV modules to the electrolyzer. Originality/value This paper proposes a new approach to determine the maximum power point (MPP) from the measurement of the open circuit voltage of PV modules. A nonlinear expression of the optimal voltage was developed and is used in combination with P&O methods. The proposed approach largely improves the performance of the MPP tracking of the PV systems.
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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) | 0.000 | 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 | 0.000 |
| 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".