Name
Abstract | ||
|---|---|---|
This work proposes an optimal wind farm layout framework to obtain the optimal placement of wind turbines in a wind farm. The optimization objective is to minimize the average cost per net electric power generated by a wind farm with a fixed number of turbines while the distance between turbines is no less than the allowed minimal distance in the far wake region. The wind farm micro-siting problem with Frandsen's Gaussian wake model based on the conservation of momentum is formulated as a constrained optimization problem and solved by real-coded Genetic Algorithm. Simulation results demonstrate that the Frandsen's Gaussain wake model is more consistent with real wakes and thus the optimization result is more accurate than the existing practices. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICIT.2019.8754976 | 2019 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT) |
Keywords | Field | DocType |
wind farm planning, wake effect, real-coded Genetic Algorithm, Frandsen's model, Gaussian distribution model | Electric power,Wake,Control theory,Average cost,Gaussian,Momentum,Engineering,Constrained optimization problem,Genetic algorithm,Wind power | Conference |
ISSN | Citations | PageRank |
2643-2978 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siyu Tao | 1 | 0 | 0.68 |
| Qingshan Xu | 2 | 8 | 5.87 |
| Changcheng Zhou | 3 | 1 | 5.12 |
| Jiemin Zhou | 4 | 0 | 0.34 |
| Gang Zheng | 5 | 109 | 19.51 |