Sensorless Wind Speed Estimation Using PSO for Multi-Objective Finite-State Predictive Torque Control in Grid-Connected Wind Turbines
Bibliographic record
Abstract
This paper introduces a novel approach to improving the performance of grid-connected wind turbines by combining Particle Swarm Optimization (PSO)-based sensorless wind speed estimation with Multi-Objective Finite State Predictive Torque Control (FSPTC).Accurate wind speed estimation is crucial for efficient turbine operation, especially in sensorless systems where traditional sensors may be expensive or unreliable.The proposed method uses PSO to estimate wind speed.In traditional FS-PTC, fine-tuning these factors is necessary to balance torque and flux dynamics, which can complicate the control process and reduce efficiency.Our new method simplifies things by using a multi-objective finitestate predictive control strategy that simultaneously optimizes torque and flux without relying on these weighting factors, by substituting the singular cost function with two distinct cost functions.Thus, simplifying the control architecture.To evaluate our approach, we performed extensive MATLAB simulations and experimental validation with real-time control implemented through ControlDesk, dSPACE DS1104 and the DSP LAUNCHXL-F28379D under various wind conditions.The results were promising, showing significant improvements in how quickly the system stabilizes torque and flux outputs.Furthermore, our method reduces the computational load compared to conventional approaches, making it more practical for real-time applications in wind energy systems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".