Advanced Control And Development of Hydro and Diesel Generator Hybrid Power System Models for Renewable Energy Microgrids
Bibliographic record
Abstract
The Nigerian power problem resulted to incessant and erratic supply of electricity and this has destroyed many industrial processes in the country. It has reduced productivity and has increased unemployment rate in the country to over 50million (this figure is over 70% of Nigerian youths). This has led many of the youths in the country to crime. It has led to the deaths of many innocent people in the country. As of 2016, the electricity energy consumption in the world from the world fact book revealed that the average power per capita (watts per person) in the United States is 1,377 Watts. In Canada, it is 1704 Watts per person and in South Africa; it is 445 Watts per person and in Australia, average power per capita (watts per person) is as high as 1,112 Watts. Whereas, the average electricity consumed in watts per person in Nigeria is just 14 Watts. Unfortunately, this has put the country in a rank of 189 out of 219 countries estimated. In this research work, a Hybrid Electric Power System (HEPS) which comprises Hydro Electric Power Plant (HEPP) and Diesel Generator (DG) was modelled and a control algorithm was established to improve the performance of the system. Hybrid power system mathematical and Simulink models were developed. The output power of the developed Simulink model was be optimized using optimum power point optimization techniques and control algorithms. Simulink models of the two components of the Hybrid Electric Power System were produced using MATLAB/Simulink software. The develop Simulink models was interconnected and final model was developed. The results obtained revealed that the problems associated with conventional methods of power generation was overcomed by the development of this renewable and non-renewable energy resources Hybrid Electric Power System (HEPS) models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".