IMPROVEMENT OF POWER QUALITY IN SOLAR PHOTOVOLTAIC WATER PUMP DRIVEN BY BLDC MOTOR WITH GRID USING ANN, pp. 200-210
Bibliographic record
Abstract
The integration of renewable energy sources into the grid offers a promising avenue for reducing losses and addressing power factor issues.However, the presence of non-linear loads introduces harmonics into the current waveform.In this study, we propose the integration of Solar Photovoltaic (PV) with Maximum Power Point Tracking (MPPT) and a Boost Converter into the DC link of a three-phase inverter powering a three-phase Brushless DC (BLDC) motor, with the primary objective of enhancing reliability in water pumping systems under both grid-connected and islanding conditions.We achieve bidirectional power flow between the single-phase grid source and the solar PV system using a Unit Vector Template (UVT).Additionally, we employ an Artificial Intelligencebased Controller, specifically Artificial Neural Networks (ANNs), to reduce Total Harmonic Distortion (THD) and improve power factor.Through comparative analysis with existing methods in the literature, we demonstrate the superior performance of our proposed techniques.By using the proposed ANN method, the THD is reduced to 1.68 and 1.87 and the harmonic spectrum is improved to10.19 and 9.93 compared to PI controller.The entire system is simulated using MATLAB software, providing a comprehensive evaluation of its functionality and efficacy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".