A Comparative Study of MPC and Economic MPC of Wind Energy Conversion Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this work, we perform a comprehensive comparative study of two advanced control algorithms—the classical tracking model predictive control (MPC) and economic MPC (EMPC)—in the optimal operation of wind energy conversion systems (WECSs). A typical 5 MW wind turbine is considered in this work. The tracking MPC is designed to track steady-state optimal operating reference trajectories determined using a maximum power point tracking (MPPT) algorithm. In the design of the tracking MPC, the entire operating region of the wind turbine is divided into four subregions depending on the wind speed. The tracking MPC tracks different optimal reference trajectories determined by the MPPT algorithm in these subregions. In the designed EMPC, a uniform economic cost function is used for the entire operating region and the division of the operating region into subregions is not needed. Two common economic performance indices of WECSs are considered in the design of the economic cost function for EMPC. The relation between the two economic performance indices and the implications of the relation on EMPC performance are also investigated. Extensive simulations are performed to show the advantages and disadvantages of the two control algorithms under different conditions. It is found that when the near future wind speed can be predicted and used in control, EMPC can improve the energy utilization by about 2% and reduce the operating cost by about 30% compared to classical tracking MPC, especially when the wind speed varies such that the tracking MPC switches between operating subregions. It is also found that uncertainty in information (e.g., future wind speed, measurement noise in wind speed) may deteriorate the performance of EMPC.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it