FPGA implementation of impedance‐compensated phase‐locked loop for HVDC converters
Bibliographic record
Abstract
The phase‐locked loop (PLL) plays a key role in HVDC systems. Recently, a new type of PLL called the impedance‐compensated phase‐locked loop (IC‐PLL) was introduced to compensate for the voltage drop across the AC network's Thevenin impedance, making the phase locking more robust against transients and harmonics. The IC‐PLL has an improved dynamic response as compared with the traditional approaches. However, earlier studies on the IC‐PLL are mainly based on off‐line simulations. In this study, an actual IC‐PLL is constructed in hardware and its performance is investigated by connecting it to a real‐time model of a line‐commutated converter‐based HVDC system on a real‐time digital simulator. The proposed IC‐PLL is constructed using a field‐programmable gate array platform. Paralleled and pipelined structures are implemented on the FPGA to achieve low latency and high speed. The performance of the IC‐PLL is tested by exposing it to different type of system disturbances such as sudden step change in power, voltage magnitude change and voltage distortion. Results are compared with the traditional trans‐vector PLL. The results show the performance of the IC‐PLL is superior.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".