Black Widow Optimization-Based PV Array Reconfiguration for Maximized Water Flow in Agricultural Systems Under Partial Shading
Bibliographic record
Abstract
In regions where conventional energy sources are inaccessible, unreliable, or expensive, using photovoltaic (PV) technology in agricultural water pumping systems is a viable solution for sustainable irrigation.However, the efficiency of PV systems may be significantly affected by partial shading conditions (PSCs), which can lead to degrading PV system performance, hence reducing power production.In this paper, the challenge of partial shading (PS) is addressed by proposing a new approach that combines Black Widow optimization (BWO) algorithm-based dynamic PV array reconfiguration, Kalman filter (KF)-based maximum power point tracking (MPPT), and direct torque control (DTC) for a pumping induction motor (IM).The dynamic reconfiguration algorithm exploits realtime irradiance data to optimize the output power of the PV array by adjusting module configurations, resulting in smoother power-voltage (P-V) curves and system efficiency improvement.Simulation studies are conducted and the results demonstrated the effectiveness of the proposed approach in increasing water flow and ensuring reliable operation under various PSCs.The most significant result of this research is the remarkable increase in water flow, with a gain of 432 l/h observed in one of the case studies.Considering the performance improvement the of PV water pumping system, this research may contribute to the promotion of sustainable agricultural practices, particularly in regions where access to conventional energy sources is limited.
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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) | 0.001 | 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".