Advances in control of power electronic converter connected drive and generation systems
Bibliographic record
Abstract
Advances in control of power electronic converter connected drive and generation systemsNowadays, a large number of different power electronic conversion systems are being designed.Most of them are based on power electronic converters.These converters are widely used to create highly efficient conversion systems.Among others, these converters are used in microgrids, distribution grids, and transportation infrastructure, generating new technology applicable to renewable grid integration, efficient power transmission and distribution, electrical recharging systems for EV/ HEV vehicles, energy management, etc.The performance of power electronic converters, and the systems containing them, are strongly influenced by the type of control algorithm and the hardware used to execute it.Then, the design of control strategies that enables the achievement of high performance systems is an important issue for industrial applications.This special issue presents five papers introducing new control algorithms for attaining more efficient and/or reliable power electronic conversion systems.In the first paper, "The Output Performance of High Power Quality Three-Phase to Single-Phase AC Power Generation System Based on Open-Winding PMSG for Standalone Power System", a three-phase to single-phase direct AC power generation system based on the openwinding permanent magnet synchronous generator (PMSG) with the dual-inverter is proposed for the standalone power system.A cascade control is proposed and in the inner-loop currents are fed back and compared with current references.These references are obtained from DC bias voltage and root mean square values of the single-phase power errors, where references are calculated for satisfying a power balance.In the second paper, "A Novel Digital-Signal-Processor Based Maximum-Power-Point Tracking Control Design for a Vertical-Axis Wind-Turbine Generation System Using Neural Network Compensator", a novel algorithm based on neural network technique is introduced for maximum-power-point tracking algorithm of a vertical-axis wind-turbine generation system.This algorithm is implemented in a digital signal processing chip.A robust grid voltage sensor fault-tolerant control for single-phase two-level rectifiers is introduced in the third
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".