A Low Computational Framework for Testing Wind Farm Controllers
Creators: name: Novais, Felipe
name: Schlipf, David
name: Raach, Steffen
Publisher: name: Zenodo
Year: 2021
Subjects: wind farm control
wind energy

description: As the global installed capacity of wind energy expands, the interest towards improving the efficiency of wind farms also grows. The idea of wind turbines acting greedily aiming to maximize their own power production has given place to coordinate control strategies, in which each turbine of the wind farm acts collectively to achieve a common objective. The concept of wind farm control lies on the assumption that neglecting the dynamic interactions between turbines might lead to a sub-optimal state. Wind farm control could be achieved through different techniques, however, this work will be limited to wake steering, which consists in applying an intentional yaw misalignment to the upfront turbine in order to divert the disturbed flow to minimize its impact over the downstream turbine, increasing the power production of the wind farm as whole whilst decreasing structural loads over waked turbines. In order to achieve the optimal wake steering setting, it is necessary that each turbine has the correct set of demanded yaw misalignment assigned. The standard approach utilized in recent field tests is to estimate the flow with low-fidelity surrogate wake models and obtain the optimal set of yaw misalignment [3][4]. The main issue consists in the idea that if the model is inherently unable, due to the simplification of the physics involved, to satisfactorily approximate the wind flow, then the wake steering effectiveness might be impaired. Therefore, different means to obtain the optimal yaw misalignment are worth investigating. This work presents the development of a closed-loop modular framework, which allows to test with ease and low-computational cost different strategies to reach the optimal wind farm control wake steering settings. The framework utilizes surrogate models and a Gaussian distributed deviation from the demanded yaw misalignment to emulate wind farm data and applies for each optimization process an algorithm to optimize the control settings. Further, three algorithms are presented and investigated for different level of complexities, including varying inflow conditions and number of turbines, in order to explore its efficacy. Firstly, a model fitting approach consists in estimating the tuning parameters out of the wake surrogate low-fidelity models. Secondly a purely data-driven strategy which aims to track the set of yaw misalignment observed which returns the highest generated wind farm power. Thirdly, an approach based on fitting a surrogate machine learning model to a set of data in order to predict under a defined optimization boundary what is the optimal setting. Overall, it was observed that data-driven approaches rely heavily on data quality and quantity and require data processing steps in order to make different data points comparable in varying inflow conditions. Furthermore, they might need several iterations to reach the optimal. Whereas, model fitting algorithms based on the low-fidelity models are capable of extrapolating optimal control inputs to unseen flow conditions and reach the optimal demanded yaw misalignment on the first iteration, yet their reliability and capability to approximate the flow in real conditions is still limited. The framework demonstrated to be a valuable tool due to its low computational effort required to simulate potential gains of different algorithms over a year or even the lifetime of a wind farm. Such tool could be utilized as a first step, for tuning and verification, of algorithms which could then be later validated with high-fidelity simulations.