A 2.5MW Wind Turbine TL-EMPC Yaw Strategy Based on Ideal Wind Measurement By LiDAR

Abstract

This paper analyzes the yaw data of the 2.5MW wind turbine of XEMC Windpower Company, and the real wind information collected by the wind farm, and proposes a two-level economic model predictive control (TL-EMPC) yaw strategy based on ideal wind measurement by light detection and ranging (LiDAR). This strategy comprehensively considers the power loss caused by yaw misalignment and the structural loads of the yaw bearing during the yaw process, making the yaw system more efficient and economical: In the high wind speed range, the yaw system has a higher sensitivity by setting the threshold of the yaw error angle, so as to fully capture wind energy; in the low wind speed range, fully consider the fatigue load of the critical parts of the wind turbine during the yaw process, thus Improve the economy of the wind turbine yaw system. The fatigue load and limit load of the yaw actuator at different yaw speeds are analyzed, and the best yaw speed is obtained. The finite control set of the yaw speed is established, which is used as the constraint set of the second-level minimum objective function. Finally, an external controller was used to simulate the 2.5MW wind turbine model in Bladed, and the effectiveness of the control strategy was verified.

A state‐of‐the‐art comprehensive review of modern control techniques for grid‐connected wind turbines and photovoltaic arrays distributed generation systems

“…In recent years, some improved yaw control techniques [23][24][25][26][27][28] are presented to implement the YCB of PMSG-/DFIG-based WTs DGSs in grid-connected applications. In [23], a multi-step prediction model-based advanced MPC technique is proposed for the yaw control of a horizontal-axis WT.…”

“…It realizes a more economical and efficient yaw system by considering the yaw bearing structural loads and power loss happened due to the yaw misalignment. The suggested LIDAR-based TL-EMPC [25] obtains an average of 1.8% increased power, 1.2% decreased DEL of blade root and 0.5% decreased DEL of yaw bearing as compared to the conventional yaw control [22]. In [26], the adaptive yaw control technique is designed based on the control error angle of yaw start (CEAYS) and WT angle deviation.…”

“…In [25], an LIDAR‐based TL‐EMPC is proposed for the yaw control of WT using ideal wind measurement. It realizes a more economical and efficient yaw system by considering the yaw bearing structural loads and power loss happened due to the yaw misalignment.…”

“…The YCB adjusts the WT rotor direction to always face the wind in order to obtain an increased power, reduced loads under the yaw misalignment and a minimized wake effect [21,22]. In the grid-connected WTs DGSs, the YCB can be applied using the modern control techniques, such as model predictive control (MPC) [23], light detection and ranging (LIDAR)-based economic MPC (EMPC) [24], LIDAR-based two-level EMPC (TL-EMPC) [25], adaptive control [26], intelligent scenarios generation-based stochastic model predictive yaw control (ISG-SMPYC) [27] and non-linear MPC (NMPC) [28]. In the grid-connected PMSG-/DFIG-based WTs gen-eration systems, the MSC/RSC are used to control the active power (P) generated by the PMSG/DFIG, respectively [10,29].…”

Nonlinear Intelligent Predictive Control for the Yaw System of Large-Scale Wind Turbines

“…As the development of advanced prediction technologies like LiDAR [7], more recent research has concentrated on the advanced predictive controls [8]. Model predictive control (MPC) is a typical representative of predictive control, which has been proposed for the torque and yaw control of WTs, and has achieved good control performance [9][10][11].…”