Maximum power point tracking of single‐ended primary‐inductor converter employing a novel optimisation technique for proportional‐integral‐derivative controller
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it