Hybrid and parallel-computing methods for optimization of power systems with electromagnetic transient simulators
Bibliographic record
Abstract
This thesis introduces new methods for using electromagnetic transient (EMT) simulators to efficiently optimize controllers of the power electronic converters in power systems with complicated dynamic behavior. This work is motivated by several challenges that must be overcome during the design process, including high computational burden of simulating large switching systems, repetitive nature of the design cycle, the large number of variables that need to be handled, etc. These challenges are addressed in this research by combining an EMT simulator with optimization algorithms and by developing novel approaches to reduce the entire simulation time. Two screening methods are introduced in this thesis that can identify non-influential parameters so that the number of parameters to be optimized can be reduced, thus decreasing the computational burden of the process. Moreover, multi-algorithm and parallel processing techniques are developed to achieve additional computational benefits by making the design process faster. In this research, new pathways are created to solve simulation-based design problems with a large number of parameters by amalgamating all the above approaches. Several power system examples are simulated using PSCAD/EMTDC, and the accuracy and efficiency of the proposed methods are assessed and confirmed. The results show significant reductions in the time to design optimal systems without compromising the quality of the optimal performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".