A Data-Driven Observer for Wind Farm Power Gain Potential: A Sparse Koopman Operator Approach

Yue Chen, Bingchen Wang, Kaiyue Zeng, Lifu Ding (), Yingming Lin, Ying Chen and Qiuyu Lu
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Yue Chen: Power Dispatch Control Center, Guangdong Power Grid Company Ltd., Guangzhou 523000, China
Bingchen Wang: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Kaiyue Zeng: Power Dispatch Control Center, Guangdong Power Grid Company Ltd., Guangzhou 523000, China
Lifu Ding: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Yingming Lin: Power Dispatch Control Center, Guangdong Power Grid Company Ltd., Guangzhou 523000, China
Ying Chen: Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
Qiuyu Lu: Power Dispatch Control Center, Guangdong Power Grid Company Ltd., Guangzhou 523000, China

Energies, 2025, vol. 18, issue 14, 1-18

Abstract: Maximizing the power output of wind farms is critical for improving the economic viability and grid integration of renewable energy. Active wake control (AWC) strategies, such as yaw-based wake steering, offer significant potential for power generation increase but require predictive models that are both accurate and computationally efficient for real-time implementation. This paper proposes a data-driven observer to rapidly estimate the potential power gain achievable through AWC as a function of the ambient wind direction. The approach is rooted in Koopman operator theory, which allows a linear representation of nonlinear dynamics. Specifically, a model is developed using an Input–Output Extended Dynamic Mode Decomposition framework combined with Sparse Identification (IOEDMDSINDy). This method lifts the low-dimensional wind direction input into a high-dimensional space of observable functions and then employs iterative sparse regression to identify a minimal, interpretable linear model in this lifted space. By training on offline simulation data, the resulting observer serves as an ultra-fast surrogate model, capable of providing instantaneous predictions to inform online control decisions. The methodology is demonstrated and its performance is validated using two case studies: a 9-turbine and a 20-turbine wind farm. The results show that the observer accurately captures the complex, nonlinear relationship between wind direction and power gain, significantly outperforming simpler models. This work provides a key enabling technology for advanced, real-time wind farm control systems.

Keywords: wind farm control; active wake control; Koopman operator; dynamic mode decomposition; sparse identification; data-driven observer; surrogate model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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