An algorithm for accurate switching representation in fixed-step simulation of power electronics
Bibliographic record
Abstract
Fixed-step integration methods are widely used in simulation of large power systems. Unfortunately they have some limitations, especially in the presence of hard nonlinearity as produced by switching of power electronic devices. Switching occurs most frequently between time steps and rarely coincide with them. This introduces an error on the switching instant. Without corrections, this error causes jitter and noncharacteristic harmonics. This article presents a simple yet effective method to achieve a very accurate switching representation in fixed-step simulations of power systems. A piecewise integration is performed and interpolated to the exact switching instant. From that point on, a second integration is performed with the usual fixed step using the final conditions present before the discontinuity as initial conditions. Finally, a second interpolation yields the current step results. The method has given very satisfactory results in various configurations such as HVDC converters, PWM inverters and STATCOM devices. Some of these results are presented and compared with those obtained with a precise simulation software based on variable-step integration methods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".