A robust wide area measurement based controller for networks with embedded HVDC links
Bibliographic record
Abstract
The advent of Wide-Area measurement Systems has spurred interest in the use of non-local feedback signals for power swing damping control. Although damping can be improved through generator excitation systems, dc links and other grid connected power electronic converters, the full potential of wide-area measurements can be realized by coordinating the strategies used for multiple controllable devices in a grid. These strategies also need to be robust to partial or complete loss of communication, changes in operating points, topology and equipment outages, improve damping of all the controllable swing modes, and have adequate stability margins to avoid destabilization of untargeted modes. This thesis investigates a control strategy for multi-infeed and multi-terminal (also referred to as multiple embedded dc links in this thesis) dc links using local frequency difference signals as well as the frequency difference signals obtained from other dc links. This strategy combines the advantages of the local frequency difference signal with the additional degrees of freedom provided by the use of non-local frequency difference signals, to achieve targeted and enhanced swing mode damping for the poorly damped modes. Since the strategy uses only a limited set of non-local signals, the signals may be directly communicated to the dc links without having to be centrally collated with other system-wide measurements. The key aspect of the proposed strategy is the use of a symmetric positive definite (spd) gain matrix. This results in enhanced damping for all controllable swing modes. Furthermore, loss of communication between the dc links does not destroy the symmetric positive definiteness and the gain elements can be tuned to selectively enhance damping of poorly damped modes. Eigenvalue sensitivity analysis and case studies on a 3 machine 9 bus and 16 machine 68 bus system with multiple HVDC links are presented to demonstrate the key attributes and the effectiveness of this strategy.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".