An efficient photovoltaic DC village electricity scheme using a sliding mode controller
Bibliographic record
Abstract
The paper presents a novel dynamic maximum photovoltaic (PV) power tracking controller using an error driven, error scaled self-adjusting sliding mode controller for maximum PV energy utilization in a low cost stand-alone photovoltaic resort/village electric energy supply scheme. The novel dynamic controller utilizes motor speed trajectory tracking loop with additional dynamic photovoltaic power tracking loop. This ensures both good dynamic speed tracking and near maximum photovoltaic energy utilization at all times, especially under varying ambient temperature (Tx) and solar irradiation (Sx) conditions. The proposed novel dynamic controller requires only the PV array output voltage and current signals and the DC motor speed signals that can be easily measured. Satisfactory results are obtained with the proposed dynamic dual loop controller for the PV array feeding resistive loads (lighting and heating) as well as PMDC motor. The proposed stand-alone (photovoltaic renewable energy supply scheme is suitable for low cost resort/village electricity applications in the range of (150 watts to 15000 watts), mostly for water pumping, water heating, lighting and village irrigation use in arid developing countries
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".