Power fluctuation minimization in grid connected photovoltaic using supercapacitor energy storage system
Bibliographic record
Abstract
In this paper, an efficient control is proposed and implemented to minimize the power fluctuation of grid connected photovoltaic (PV) with supercapacitor energy storage system (SCESS). The SCESS is used to minimize the power fluctuation caused by changes in temperature and irradiation. The optimal size of the SCESS and its control strategy are developed for continuously charging and discharging SCESS to achieve its objectives. Adaptive Neuro-fuzzy Inference System is developed in real time using dSPACE to generate the maximum power from the PV system. The SCESS is integrated with the system through a bi-directional buck boost converter. The system model and the control strategy have been developed in Real Time Digital Simulator (RTDS) that consists of PV array, buck converter, buck-boost converter, and voltage source converter (VSC). To transfer the available DC power to the grid, an independent P-Q control is proposed and implemented for the VSC. The proposed controller is examined through hardware in the loop setup using RTDS and dSPACE 1104 controller. Furthermore, the superiority of the proposed approach has been confirmed by comparing the results with those reported in the literature.
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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) | 0.001 | 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".