.png){kind=icon}
返回
Wind farm power optimization using system identification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compchemeng.2024.108877.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The wake effect reduces the total power production of wind farms. This paper presents a method for wind farm power optimization through wake effect reduction. The proposed method optimizes the yaw angle offsets and derating settings of all turbines to maximize total power generation. The optimization approach is gradient-based, with gradients at each iteration obtained through system identification using field test data, eliminating the need for physical models. In system identification, test signal design, model estimation and model validation problems are solved in a systematic manner; in the gradient-based optimization, in order to achieve fast convergence, methods for initial value and initial step-size determination, variable step-size iteration and iteration termination are developed. The method is verified using the FLORIS wind farm model developed by National Renewable Energy Laboratory (NREL), USA. The studied wind farm consists of 80 wind turbines configured similarly to the Horns Rev I offshore wind farm in Denmark. The result of the developed optimization method is highly consistent with those obtained using FLORIS's built-in optimization tool.
Keyword:
Wind farm control
Wake effect
Power de-rating
Yaw-based steering
System identification
期刊
C
IF:
3.9
论文数:
8.1K
被引数:
1.7W
机构
引用论文
Development of a three-term MPC and its application to an ultra-supercritical coal fired power plant
PREAI
分享收藏Simulation comparison of wake mitigation control strategies for a two-turbine case.png)
WIND ENERGY
WIND ENERGYIF3.3
PREAI
分享收藏
查看更多内容