Optimal Controllers to Improve Transient Recovery of Grid-Following Inverters Connected to Weak Power Grids
Bibliographic record
Abstract
Leading to the enormous growth in renewable and power electronics technologies and the global drive towards environmental friendliness and sustainability, a significant number of renewable energy sources are being connected to the power system via inverter-based systems. The inverter-based generations (IBG) have no stored energy and less fault current injection capability compared to the conventional synchronous machines. Consequently, a large penetration of IBG creates challenges to maintaining the stability of the power system, especially the transient stability. The weaker the power system, the higher the significance of instability. Few solutions exist in the literature to improve the fault recovery of IBG connected to weak power systems. This paper considers the method of storing energy in sub-module capacitors of the Modular-Multi-level Converter (MMC) along with temporarily boosting the inverter’s current limit. Conversely, increasing the ratings of the inverter will result in high manufacturing costs. Hence an optimization strategy is proposed in this paper, for obtaining a robust set of inverter control parameters that enhances fault recovery without excessively increasing the manufacturing cost of MMC. A frequency scanning technique supplemented with Generalized Nyquist criteria is incorporated into the optimization methodology to constrain the search space for the optimization algorithm. This enables the optimization algorithm to converge to an acceptable solution with a reasonable computing time. Furthermore, validation of the resultant set of parameters for different system conditions is presented. Finally, IBG with optimized fault recovery controllers is integrated into a simplified real-world power system, and the applicability of the proposed optimized controllers is illustrated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".