基于有效风速估计与预测的风电机组自适应最大风能跟踪控制

针对如何在有效风速未知情况下实现风电机组最大风能跟踪(MPPT)的问题,本文使用支持向量回归(SVR)和自适应控制原理,提出基于有效风速估计与预测的自适应MPPT控制方案.首先,使用机组的历史运行数据,训练得到基于SVR的风速估计与预测模型,为MPPT控制提供实时参考输入.其次,结合在线学习估计器(OLA)和减小转矩增益(DTG)控制原理,设计自适应MPPT控制器,该控制器能够较好应对系统未知动态特性和干扰,且能降低传动链载荷.最后,使用李雅普诺夫原理证明闭环系统所有信号都是有界的.仿真结果表明本文提出的方法能够获得良好的MPPT效果,进而提高机组产能.

Adaptive maximum power point tracking control for wind turbines with effective wind speed estimation & prediction

With the rapid development of wind power generation technology, maximum power point tracking (MPPT) control of wind turbines is still a challenging problem due to the unavailable effective wind speed. In this paper, an adaptive MPPT controller for wind turbines based on effective wind speed estimation \& prediction is presented, without requiring the knowledge of system parameters, rotor or wind acceleration. Firstly, support vector regression (SVR) is utilized to develop the wind speed estimation \& prediction models. The wind speed information is delivered to the MPPT controller in a real-time manner. Further, an online learning approximator (OLA) is employed in the controller to cope with the unknown dynamics of the wind turbines. Thus, the proposed OLA-based adaptive controller is parameter-free and can be readily extended to other types. Moreover, decreased torque gain control (DTG) is integrated to mitigate the mechanical loads on the driven train. Meanwhile, all signals in the closed-loop system are proven to be bounded via Lyapunov theory. Finally, the effectiveness of the proposed controller are validated with WP 1.5MW wind turbines on the platform of FAST (Fatigue, Aerodynamics, Structures, and Turbulence) code and Simulink.