Hierarchical Linking-Domain Extraction Decomposition Method for Fast and Parallel Power System Electromagnetic Transient Simulation
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Bibliographic record
Abstract
The linking-domain extraction (LDE) decomposition method is a new non-overlapping domain decomposition method for parallel circuit simulation. However, the original LDE method is inefficient in both the computational procedure and storage cost. In this work, a novel hierarchical LDE (H-LDE) method is proposed to further improve the LDE method, which leverages all the hidden features of LDE that are not exploited in the original work to perform a multi-level decomposition of power systems. The LDE-based matrix equation solution computation procedure is first proposed to eliminate the necessity of computing the entire matrix inversion, and then the multi-level computation structure is proposed for fast matrix inversion of the decomposed sub-matrices. The mathematical complexity of the H-LDE method is analyzed, which is used to derive the two principles for decomposing a power system. These principles can be applied on both parallel and sequential compute architecture. The 4-level LDE decomposition is applied on the IEEE 118-bus test power system and implemented in both sequential and parallel, which is used to verify the validity and efficiency of the proposed H-LDE decomposition method. The simulation results of various benchmark test power systems show that the proposed H-LDE method can achieve better performance than the classical LU factorization and sparse KLU method within a certain system scale.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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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 it