An Improved PSO-Based MPPT Technique Using Stability and Steady State Analyses Under Partial Shading Conditions
Bibliographic record
Abstract
Partial shading (PS) conditions create challenging issues for the maximum power point tracking (MPPT) algorithms in photovoltaic (PV) systems such as getting stuck in the local maximum power points (LMPP), slow tracking time and fluctuations in the generated power during tracking the global maximum power point (GMPP). To address these issues, this article brings contributions by proposing an improved PSO-based MPPT technique tailored for PS conditions. First, to highlight the weakness of the traditional PSO-MPPT technique, its stability and steady state behavior in the PS conditions is analyzed in depth. Second, the required criteria to achieve a stable response are obtained. Finally, a novel technique to estimate the convex area of the power versus voltage (P-V) curve is presented where the GMPP is located using two voltage boundaries. The performance of the proposed MPPT technique is experimentally validated. The results highlight the capabilities of the proposed technique in finding the GMPP with a rapid convergence and small fluctuations. The proposed MPPT is applicable to most PV inverters under any PS conditions.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".