Gradient-Based Black-Box Method for Improving the Stability Margin of Power Station Constructed by Inverters Without Exposing Controller Details
Bibliographic record
Abstract
Inverters in a renewable-energy-generation based power station (PS) may be produced by different manufacturers whose control schemes cannot be exposed to each other. Hence, the system-level stability prediction and stability-margin-improvement-oriented parameter tuning should be completed in a black-box manner. Based on the Nyquist theorem and the Routh–Hurwitz criterion, a gradient-based black-box (GBBB) modeling method is proposed for the inverters in the PS. The GBBB model is a black-box encrypted function with tunable control parameters as inputs. Its outputs contain impedance values, open-loop-stability factors, and parameter-participation-factors of the impedance for Nyquist-based stability judgment and margin-improvement-oriented parameter tuning of the PS. Based on the GBBB models of the inverters, the PS's stability margin is described by black-box cost functions. Then, a gradient-descent based parameter tuning method is proposed where the gradients are calculated using the outputs of the GBBB models according to the chain rule. All the inverters’ parameters can be optimized in theory iteratively to improve the PS's stability margin. Under the given workflow, only the GBBB models and the PS topology are needed, which means the control details of the inverters are unexposed, i.e., the intellectual property would not be infringed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".