Maximum power point tracking of single‐ended primary‐inductor converter employing a novel optimisation technique for proportional‐integral‐derivative controller
Bibliographic record
Abstract
This study presents an optimisation technique for proportional‐integral‐derivative (PID) controller to achieve maximum‐power‐point tracking (MPPT) of single‐ended primary‐inductor converter (SEPIC). A new weight function is developed to optimise the PID parameters based on gradient‐descent (GD) method by adding low‐pass filter term. The proposed optimisation method does not stick in the local minima, which happens frequently with the traditional weight function used in GD method, where the low‐pass filter term suppresses the noise and smooths the iteration process. The prototype of the proposed optimised PID‐based SEPIC converter for photovoltaic inverter applications is built using DSP‐based TMS320F28335. The performance of the proposed optimised PID‐based MPPT scheme is tested in both simulation and experiment at different operating conditions. A performance comparison of the proposed GD method with the conventional GD PID is also made in real‐time. It is found that the proposed optimised PID‐based SEPIC converter is superior to the conventional GD PID controller in terms of power transfer and efficiency. Furthermore, the proposed optimised PID controller for two‐level inverter can achieve a better total harmonic distortion (THD) level as compared to the multi‐level inverter frequently used by researchers for the same purpose.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".