Power Quality Enhancement Using Artificial Neural Network-Proportional Integral Controller and Fuzzy Granular Controller for DSTATCOM Integrated with Renewable Energy and Battery Storage System
Bibliographic record
Abstract
In the power distribution network, spread of power electronic devices and nonlinear loads has been exacerbate the power quality (PQ) difficulties.D-STATCOMs plays a key role to serve as an active power filter which are commonly used to address these issues.The performance of the distribution network is increased by incorporating the renewable energy sources (RES) such as PMSG-based wind energy conversion system (WECS) with DC capacitor across the D-STATCOM.During the PQ disturbances without controller the THD of source and load currents are 7.46% and 15.32%, by using the PI controller THD's are reduced to 3.02% and 4.01%.The proposed controllers reduced THD with ANN-PI source and load current THD are 2.99% and 3.62% and Fuzzy granular controller are 1.56% and 2.37%.Whereas on the other side, the DC-link voltage with PI controller introduces more fluctuations and reduced voltage level with settling time about 0.1secs, by using ANN-PI voltage fluctuations get reduced and maintain constant voltage, in addition to that in fuzzy granular the magnitude of dc link voltage increased by 10% and settling time got reduced about 0.05secs.The nonsinusoidal source current and load current are tranformed to sinusiodal by the ANN-PI and Fuzzy granular controller and also the power factor is improved nearly to unity.The proposed controllers are designed and tested by using MATLAB/Simulink.
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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) | 0.001 | 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".