Electronic load controller (ELC) design and simulation for remote rural communities: A powerhouse ELC compatible with household distributed-ELCs in Nepal
Bibliographic record
Abstract
Microhydro power is particularly suitable for Nepal's mountainous terrain and remote communities unconnected to a power grid. While Nepal's rural electrification rates have increased rapidly over recent decades, firewood remains the most prevalent cooking fuel - a significant WHO health concern. The electronic load controllers (ELCs) used in microhydro installations, which divert excess power to a dump load to regulate voltage and frequency, are among the components that most commonly fail. A previously proposed distributed electronic load controller (DELC) installed in each household can decrease system vulnerability to component failures while also diverting surplus power into household heaters to pasteurize water or slow-cook food, rather than into a single dump-load at the generating site as typical ELCs do. A three-phase generator supplying multiple homes is simulated with a powerhouse ELC to determine the range of load changes and DELC faults tolerable while maintaining Nepal standards for voltage and frequency regulation, and THD. Results show meeting voltage regulation standards does not confirm frequency and THD standards are met. Also, results suggest power ratings, cost, and weight of the ELC and dump load can be significantly reduced, and up to 2446% of a household's cooking can be done with dumped surplus power.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".