Deadtime Compensation Method for Synchronous Optimal Pulsewidth Modulation
Bibliographic record
Abstract
Synchronous optimal pulse-width modulation (SOPWM) techniques are widely recognized for their superior total harmonic distortion (THD) performance compared to other PWM techniques. However, their effectiveness is highly reliant on the precise switching of semiconductor devices in the voltage source inverters (VSIs). In practice, VSIs require a deadtime between the switching of upper and lower semiconductor switches to prevent short-circuiting the DC-link, which introduces a non-negligible difference between the optimal pulse pattern (OPP) and the actual output voltage waveforms. These errors result in increased THD as well as magnitude and phase shift errors in the output voltage, leading to a noticeable deterioration in the overall performance in SOPWM-controlled VSIs. To address these issues, this paper presents a novel deadtime compensation method specifically designed for SOPWM techniques used in VSIs. The proposed method manipulates the reference voltage angle in real-time, modifying the OPP to eliminate errors caused by the deadtime, turn-on and turn-off delays of switches. Simulation and experimental results demonstrate that the proposed method substantially mitigates the adverse effects of deadtime on SOPWM-controlled two-level VSIs, ensuring that the output voltage waveforms closely match the expected output voltage waveform. Experimental results show up to 14% lower current THD and up to 8% lower voltage THD compared to SOPWM without compensation. Furthermore, the proposed method is versatile, offering compatibility with both open-loop and closed-loop control strategies, thereby enhancing the reliability and efficiency of SOPWM across a wide range of applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".