Wind farm power optimization using system identification

• Published in: Computers & chemical engineering 2025-01, Vol.192, p.108877, Article 108877
• Main Authors: Zhu, Yun, Zhu, Yucai, Yang, Chao
• Format: Article
• Language: English
• Subjects: Power de-rating; System identification; Wake effect; Wind farm control; Yaw-based steering
• ISSN: 0098-1354
• DOI: 10.1016/j.compchemeng.2024.108877

•A method of wind farm power optimization is developed using system identification.•A fast convergent gradient-ascend optimization procedure is proposed using large initial step size based on process knowledge and a variable step size scheme.•The method is verified using the well-known wind farm model FLORIS of National Renewable Energy Lab of United States. 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 de-rating 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.