Sensorless IRFOC with ANN-Based Speed Optimization for Dual-Stator Induction Motor in Photovoltaic Water Pumping Systems
Bibliographic record
Abstract
The increasing demand for irrigation in rural areas has led to significant interest in photovoltaic water pumping systems (PVWPS), as grid connectivity in these regions is often unavailable or economically unfeasible.However, PVWPS face challenges related to system reliability and efficiency due to variations in environmental conditions.Building on recent advancements in the field and leveraging the notable benefits of six-phase induction motors, this study proposes a control strategy for PVWPS aimed at optimizing motor speed under variable irradiance conditions to enhance the overall performance of the water pumping system.The proposed control architecture consists of two main stages.In the first stage, maximum power is extracted from the photovoltaic generator (PVG) using a conventional Perturb and Observe (P&O) algorithm that controls a DC-DC boost converter.In the second stage, indirect rotor field-oriented control (IRFOC) is applied to a dual stator induction motor (DSIM) with a minimal sensor configuration.This approach eliminates the need for current sensors, thereby improving hardware reliability, reducing system complexity, and maintaining adaptability to solar power fluctuations.To achieve smooth and efficient operation, the motor speed is regulated using a novel speed optimizer based on an artificial neural network (ANN), which dynamically adjusts the speed reference in response to changes in solar irradiance.The system's performance was evaluated through comprehensive simulations under various operating 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 | 0.001 |
| 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".