Abstract
Modern multi-megawatt wind turbines require powerful control algorithms which consider several control objectives at the same time and respect process constraints. Model predictive control (MPC) is a promising control method and has been a research topic for years. So far, very few studies evaluated MPC algorithms in field tests. This work aims to prepare a real-time MPC system for a full-scale field test in a 3 MW wind turbine. To this end, we introduce a weight-scheduling scheme for a linear time-variant MPC in order to ensure control operation over the entire operating range from the partial to the full load range. We use a rapid control prototyping process, in particular with comprehensive software-in-the-loop (SiL) tests, in order to design and validate the MPC system for the field test.In this contribution, we present the implementation of the linear time-variant MPC with weight-scheduling to be tested in the field test. With the weight-scheduling for the optimization problem inside the MPC, we achieved good performance over the entire operating range of the wind turbine. In the SiL tests, the proposed MPC algorithm achieved loads, comparable to the baseline controller of the wind turbine and improved the reference tracking of the power output and the rotational speed. The proposed linear time-variant MPC with weight-scheduling is fully validated in the presented software-in-the-loop tests and is ready for full-scale field test in the 3 MW wind turbine. We present the experimental field test results of the introduced MPC system in a separated contribution.
Highlights
In the last decades, wind turbines (WTs) have been steadily growing in size
We investigate how a linear time-variant (LTV) Model predictive control (MPC) [4] can be extended, to be suitable in all operational conditions
To answer the first research question, we propose a weight-scheduling scheme within the MPC algorithm to cope with the varying sensitivities of the control objectives to the control actions and analyze the loads occuring in the wind conditions in the partial and full load range
Summary
Wind turbines (WTs) have been steadily growing in size. This results in more flexible components that are increasingly sensitive to loads. Active load reduction already became a major control objective in WT controllers [1]. The complexity of the dynamic behavior increases, leading to a more difficult control behavior [1,2,3]. Multi objective control methods, like model predictive wind turbine control, have been studied throughout the past to further improve the control performance for new WT prototypes. The focus of studies about MPC for WTs was rather on simulation studies than on experimental tests [1]. In contrast to previously conducted research, this paper will show
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