Mitigation of Certain Power Quality Issues in Wind Energy Conversion System Using UPQC and IUPQC Devices
Bibliographic record
Abstract
Due to limited conventional energy sources and to meet the increase of load demand there is a need for utilization of renewable energy sources. Among the all renewable energy sources wind energy is widely used and is highly sustainable as compared to other sources of energy. Many wind energy conversion devices working with doubly fed induction generators and synchronous generators and it is integrated to the grid produces the power quality issues like as voltage sags, swells, harmonics, voltage imbalance and short interruptions etc. Many power electronic based Flexible AC Transmission Systems (FACTS) are designed to solve above problems and facilitate to meet the required power demand. In this paper, UPQC (Unified Power Quality Conditioner) and IUPQC (Improved Unified Power Quality Conditioner) models are designed to mitigate the above power quality issues. In this paper, mainly voltage sags, voltage swells and harmonics are considered as a power quality issues to analyze the UPQC and IUPQC devices. The conventional PID controller is employed in control circuit of both the devices. It also discusses the comparative analysis between UPQC and IUPQC devices. The MATLAB/SIMULINK Software is used for above analysis.
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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 | 0.001 |
| 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 | 0.001 |
| 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".