Dynamic Security-Constrained Automatic Generation Control (AGC) of Integrated AC/DC Power Networks
Bibliographic record
Abstract
This paper introduces an efficient algorithm to adaptively determine the droop coefficients of generating units in hybrid ac-dc networks. These generating units include conventional synchronous generators and multiterminal high-voltage direct-current (MTDC) converters. The proposed algorithm relies mainly on trajectory sensitivity to reschedule power generation according to the new calculated droop coefficients to ensure and/or system stability margin for set of credible contingencies with different load conditions. The Newton shooting method is used to find the new steady-state values for the systems when load is changed. MTDC converters are modeled in such a way to emulate the inertia behavior of the synchronous generators. This virtual emulation is achieved by providing two additional control layers that link and control the converter powers through inertia constants similar to synchronous generators. Also, an expression for generated powers controlled by the droop coefficients is developed to be utilized in the algorithm. Finally, comprehensive simulation studies on the IEEE 68-bus benchmark system are carried out using PSCAD/EMTDC interfaced with MATLAB to validate the proposed algorithm.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".