Predictive control of multilevel converters for megawatt wind energy conversion systems
Bibliographic record
Abstract
This dissertation proposes two novel medium voltage (MV) multilevel converter configurations for use with permanent magnet synchronous generator (PMSG) based megawatt (MW) wind energy conversion systems (WECS). The classical control techniques, based on linear PI regulators and low band-width modulation, present several technical issues during lower switching frequency operation. To overcome these issues, a high performance finite control-set model predictive control (FCS-MPC) strategy is proposed to control the power converters employed in the MW-PMSG-WECS. The proposed three-level and four-level converters combine the advantages of proven wind turbine technologies, such as low-cost generator-side passive converters, and efficient gridside multilevel converters. The intermediate dc-dc multilevel converters ensure balancing of the capacitor voltages during all operating conditions. With this feature, the grid-side multilevel converters produce better grid current waveforms compared to the back-to-back connected converters. A generalized approach for the predictive control of an n-level diode-clamped converter was investigated. The FCS-MPC strategy for current control and decoupled active/reactive power regulation of grid-connected multilevel converters was also analyzed. The major WECS requirements such as maximum power point tracking, balancing of dc-link capacitor voltages, switching frequency minimization, common-mode voltage mitigation, regulation of net dc-bus voltage, and grid reactive power control have been modeled in terms of power converter switching states. These control objectives have been accomplished during each sampling interval by selecting the switching states which minimize the generator- and grid-side cost functions. Issues related to the weighting factors selection, control delay compensation, accurate extrapolation of references, control of variable switching frequency nature, prediction of variables over two samples with reduced computational burden, and robustness analysis, are also addressed in this dissertation. To keep the dc-bus voltage constant during low voltage ride-through operation, predictive control scheme is proposed for the power converters while storing surplus energy in the turbine-generator rotor inertia. The generation and exchange of reference control variables during symmetrical grid voltage dips is suggested to meet the grid code requirements. The proposed solution is efficient as no energy is dissipated in the dc-link crowbar. The simulation and experimental results validate the proposed MV converters and predictive control schemes.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".