Design and Dynamic Modelling of a Hybrid Power System for a House in Nigeria
Bibliographic record
Abstract
This paper presents the design and dynamic modelling of a hybrid power system for a house in Nigeria. Thermal modelling of the house under consideration is carried out using BEopt software to accurately study the heat loss through the walls, windows, doors, and roof of the house. The analysis of this thermal model is used to determine hourly load data. Design of an optimum hybrid power system for the house is done with HOMER Pro software. The hybrid power system is made up of a diesel generator and a stand-alone PV system. The proposed PV system consists of PV arrays, DC–DC boost converter, MPPT controller, single-phase full-bridge inverter, inverter voltage mode controller (PI controller), and single-phase step-up transformer. Dynamic simulation of the proposed PV system component of the hybrid power system is carried out in MATLAB/Simulink environment to study the power quality, harmonics, load impact, voltage transients, etc. of the system, and the simulation results are presented in the paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".