Mitigation of Power Losses in Solar Photovoltaic Systems Under Partial Shading Using Optimization-Based MPPT
Bibliographic record
Abstract
Partial shading is one of the crucial bottlenecks in solar photovoltaic (PV) system.The performance of a PV system is affected due to partial shading.This paper highlights the impact of partial shading condition (PSC) on the performance of PV systems with an experimental analysis using a PV emulator.A reduction of 37% in maximum power, 38% in fill factor, and 60% in efficiency as a result of PSC was observed in the experimentation work.PSC also results into multiple peaks on power-voltage (P-V) curve.One of these peaks is the Global Maximum Power Point (GMPP) and other peaks are local MPPs.The GMPP cannot be tracked using conventional MPPT algorithms.This paper proposes a new optimization method called as Firefly Algorithm (FA) built on a metaheuristic approach for Maximum Power Point Tracking (MPPT).Results obtained through the simulation show the enhancement in the tracking efficiency and tracking time over the conventional MPPT methods by achieving the tracking efficiency of 98.12% with a response time of less than 1ms.The proposed system is also able to reduce the oscillations around MPP and achieve stable performance under dynamically varying environmental conditions.
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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 | 0.000 | 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 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".