Session: 09-01-12 Wind Energy: System Optimization 1

Paper Number: 125060

125060 - A Comparison of Optimization Methods Applied to Surrogate-Based Optimization in Wind Farm Yaw Control 

In wind farms, wake effects among turbines hinder optimal power generation, making collective yaw control crucial for efficiency. The accurate calculation of power generation for large-scale wind farm is time consuming and computationally expensive. To address this challenge, Surrogate-Based Optimization (SBO) emerges as an effective tool in the yaw optimization. In the design of experiment, wake effects are simulated by Gauss-Curl Hybrid (GCH) model in FLORIS. Based on the limited dataset, SBO utilizes surrogate models to approximate the relationship between yaw angles and total power in the entire design space, enabling a more efficient approach to yaw optimization. The selection of optimization algorithms within the SBO framework becomes pivotal, with the recognition that gradient-free methods excel in scenarios with multiple local minima, while gradient-based methods offer advantages in high-dimensional problems inherent in large-scale wind farms. This paper compares three optimization algorithms—Sequential Least Squares Quadratic Programming (SLSQP), Differential Evolution (DE), and Particle Swarm Optimization (PSO)—in the context of wind farm yaw optimization. Three yaw control case studies are presented, offering a comprehensive comparison of the strengths and limitations of the optimization algorithms. Results show that the gradient-free methods, balancing computational cost and accuracy, stand out as the preferred choice for wind farm yaw optimization. Despite the low-fidelity engineering wake model used in the current study, the SBO framework is also compatible with the higher-fidelity wind turbine models. This capability lays the foundation for optimizing yaw control under more realistic conditions.

Presenting Author: Yu Tu Shanghai Jiao Tong University

Presenting Author Biography: Yu TU earned her Bachelor's degree in Civil Engineering from Tianjin University in 2020. She is currently pursuing her Ph.D. at Shanghai Jiao Tong University and is also engaged in a research exchange program at The Hong Kong Polytechnic University.
Her research focus lies within the realm of large-scale wind energy farms, specifically, the control and optimization of wake flows in wind turbine clusters.
Her publication includes: Tu, Yu, et al. "Aerodynamic characterization of two tandem wind turbines under yaw misalignment control using actuator line model." Ocean Engineering 281 (2023): 114992.

Authors:

Yu Tu Shanghai Jiao Tong University
Kai Zhang Shanghai Jiao Tong University
Yaoran Chen Shanghai University
Zhaolong Han Shanghai Jiao Tong University
Yong Cao Shanghai Jiao Tong University
Dai Zhou Shanghai Jiao Tong University