A Comparative Study of Supercapacitor-Based STATCOM in a Grid-Connected Photovoltaic System for Regulating Power Quality Issues
Bibliographic record
Abstract
Renewable energy resources (RERs) play a vital role in reducing greenhouse gases, as well as balancing the power generation demand in daily life. Due to the high penetration of RERs and non-linear loads into utility power systems, various power quality issues arise, i.e., voltage drop, harmonic distortion, reactive power demand, etc. In order to handle these power quality issues, there is a need for smart flexible alternating current transmission system (FACTS) devices. In this paper, a super capacitor energy storage system (SCESS)-based static synchronous compensator (STATCOM) is designed in order for the grid-connected photovoltaic (PV) system to overcome the abovementioned power quality issues. A voltage controller and a d-q axis controller are used for the efficient performance of the STATCOM. In order to show the superiority of the supercapacitor, a detailed comparison is made between a battery energy storage system (BESS)-based STATCOM and a SCESS-based STATCOM. Four scenarios are studied to evaluate the performance of the proposed STATCOM design. The proposed SCESS-based STATCOM not only boosts the voltage but also stabilizes it from 368 V to 385 V (Ph-Phrms). The simulated results have confirmed that the proposed design is not only superior to a BESS-based STATCOM but also has the capability to overcome the power quality issues as well.
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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 | 0.000 | 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".