Grey Wolf Optimization based MPPT of PV System Powered Water Pumping System Driven by SRM under Partial Shading Conditions
Bibliographic record
Abstract
The WPS plays a crucial role in various daily human activities, including agriculture, drinking water supply, and industrial applications. In particular, the agricultural sector is heavily dependent on WPS, as it serves as the main source of irrigation for crops. To reduce the strain on the electrical grid and ensure a consistent power supply for WPS in rural areas, a standalone PV powered WPS installed locally offers a practical solution. An electrical motor is essential for operating the pump, which draws water from underground sources to irrigate fields. Among the different types of motors available, SRMs have attracted considerable attention from researchers for their potential use in WPS due to their many benefits. This paper examines an 8/6 pole SRM designed to deliver the maximum torque required to lift water from significant depths. However, incorporating batteries into WPS systems can result in higher costs and increased maintenance, which may be a barrier for farmers. Therefore, this study emphasizes a PVpowered SRM based WPS that functions without batteries, utilizing a speed sensorless DTC in conjunction with a SMC. The GWO based P&O algorithm is employed and integrated with a dc-link voltage controller to optimize power extraction from the PV system even operating efficiently during PSCs by managing the proposed converter. As a result, an additional DC-DC converter for MPPT is rendered unnecessary. Detailed results from HIL testing are presented to validate the proposed system’s performance under both transient and steady-state 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) | 0.001 | 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".