A Novel Control Strategy to Integrate DSTATCOM Functionalities in a Grid-Connected AC Microgrid, Featuring SPV-BES and SyRG-Wind-Based DGs
Bibliographic record
Abstract
Harmonics drawn by loads degrade power quality performance of a microgrid (MG). Equipping MG with distribution static compensator (DSTATCOM) functionalities can mitigate this issue. The effectiveness of DSTATCOM relies heavily on method used to filter load currents, and an optimal algorithm offering superior filtering, dynamic response, and dc offset rejection is still under research. This work introduces a novel fourth-order generalized integrator (NFOGI)-based harmonic decoupling network (HDN) for extracting load current and grid voltage sequence components. These components are utilized to integrate DSTATCOM functionalities into an MG, comprising a solar photovoltaic array (SPVA), battery energy storage (BES), and a synchronous reluctance generator-based wind generator (WG). SPVA and BES form a solar photovoltaic system (SPVS) sharing a grid-side power converter (GSC), while WG connects to ac grid through a separate back-to-back GSC, creating a wind energy system (WES). DSTATCOM features in both SPVS and WES are meticulously controlled by developed novel control strategy to ensure balanced current injection by SPVS, even under unbalanced loads. This approach keeps SPVS’ dc link free from 2nd harmonics and enables direct BES integration at dc link without compromising its lifespan. Results demonstrate that the proposed algorithm provides a superior response, ensuring MG compliance with the IEEE standard power quality indices (PQIs), such as THD and unbalance factor.
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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) | 0.001 | 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".