Implementation and control of small-scale hybrid standalone power generation system employing wind and solar energy
Bibliographic record
Abstract
This paper presents an implementation and control of a hybrid standalone power generating system (HSPGS) based on a wind turbine (WT) and a solar-photovoltaic (PV) array. A squirrel cage induction generator (SCIG) is coupled with a WT for electromechanical energy conversion. The frequency and amplitude of voltage at AC bus, as well as, power quality improvement at the point common coupling (PCC) are achieved by controlling the three-leg voltage source converter (VSC) using decoupled nonlinear control algorithm. A battery energy storage system (BESS) is reinforced in the hybrid system to ensure power leveling under wind, solar and load fluctuations. For obtaining the maximum power from a solar PV array and to regulate the output DC voltage, a DC-DC boost converter is controlled using perturbation and observation technique. Moreover, a dump load is tied at DC bus to ensure stability of control and to protect the BESS and solid state switches from overvoltage at DC bus during the period of fully charged battery. The proposed HSPGS and its control algorithms are modeled in MATLAB/Simulink and its performance is tested in real time under presence of balanced/unbalanced, linear/nonlinear loads and change in climate conditions.
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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 | 0.001 | 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".