Analysis and Digital Implementation of Cascaded Delayed-Signal-Cancellation PLL
Bibliographic record
Abstract
Phase-locked loop (PLL) is usually required to detect grid phase angle in grid-tied converters. Conventional PLL schemes have to compromise between steady-state accuracy and transient dynamics when grid voltage is polluted by unbalance and harmonics. To overcome this challenge, a generalized delayed-signal-cancellation (DSC) operator is proposed recently to form cascaded DSC (CDSC) operator to eliminate arbitrary harmonics. With the CDSC operator, the conditioned voltage can be used in PLL loop with very high bandwidth for fast tracking. However, for digital implementation, the CDSC operator may subject to delay-time error, which subsequently leads to residual distortions in the conditioned voltage. In this paper, a thorough analysis of the CDSC operator in both synchronous and stationary reference frames is first conducted. The discretization error during digital implementation due to nonideal system sampling frequency and/or grid-frequency variation is quantified with the proposed concept of relative harmonic gain error. An effective improvement method is then developed that is based on linear interpolation and is effective for all delay-based PLL schemes. Finally, experimental results are obtained to verify the harmonic elimination ability of CDSC in various scenarios and the effectiveness of the interpolation-based digital implementation scheme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".