Optimal Remote control of FOPID based Adaptive DMOA for Solar PV Water Pumping System
Bibliographic record
Abstract
Solar water pumping systems are a crucial application of renewable energy, especially in rural areas where traditional electricity infrastructure may be limited or nonexistent. These systems utilize solar energy to drive water pumps, offering a sustainable and economical solution for water provision. Remote controllers further enhance the convenience and efficiency of solar water pumping systems by enabling remote monitoring and control. This article introduces a solar water pumping system that incorporates an optimized Fractional-Order Proportional-Integral-Derivative (FOPID) controller. By fine-tuning the FOPID parameters, the system can achieve superior performance and reliability, making it well-suited for operation under diverse environmental conditions. The photovoltaic (PV) panel data is transmitted to a remote controller via the Internet of Things (IoT). The remote controller employs the Adaptive Weighted Dwarf Mongoose Optimization Algorithm (ADMOA) to optimize the and parameters of the FOPID controller of solar PV panel. These optimized parameters are then transmitted to the FOPID controller to ensure optimal operation of the solar water pumping system. To evaluate the effectiveness of the ADMOA method, it was compared to traditional trial-and-error tuning methods based on output power, stator current, rotor speed dynamics, and torque. Thus, the simulated findings consistently reveal the superiority of the ADMOA algorithm in terms of convergence analysis and solution quality compared to other reported techniques.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".