大型风力发电机组主控系统控制策略研究

发布时间：2018-02-07 10:19

本文关键词： 风力发电机组转矩控制变桨控制鲁棒-自适应控制算法反步式设计 Bladed　出处：《北京交通大学》2014年硕士论文　论文类型：学位论文

【摘要】：随着人类社会的发展,人类对于能源的需求急剧增加。然而,传统能源的减少,能源安全以及全球气候变暖等问题使得人类必须加大对新型能源的研究。在这样的背景下,风能,作为一种新型的清洁能源日益受到人们的关注。本课题研究内容是大型风力发电机组主控系统控制策略,在充分考虑大型风力发电机组结构、组成的基础上,设计出具有针对性的大型风电机组主控系统,包括转矩控制系统,统一变桨控制系统,独立变桨控制系统以及载荷优化系统等。并利用Bladed风力发电机设计仿真软件对控制策略进行了仿真。在此基础上,本文针对大型风机结构参数难以获取的特点,重点研究基于鲁棒.自适应算法。由于常规的控制方法是完全基于系统参数模型的控制策略,当系统参数发生变化或者负载不定时的控制效果并不理想。因此,本文针对风机模型的非线性提出了基于反步设计的鲁棒自适应控制算法。所设计控制策略不仅对系统模型参数的未知时变性有较强的鲁棒性,而且对外界环境引起负载的干扰有良好的控制效果。并且通过基于李雅普诺夫稳定性证明。对于风机系统非线性特性,本文还提出了鲁棒-神经网络算法的风机转矩控制策略。利用神经网络对非线性曲线的逼近特性,可以很好地抵消系统非线性特性对于控制器的不利影响。该方案同鲁棒-自适应算法相比,对于控制器输出的限制较低。该方案的稳定性同样通过基于李雅普诺夫稳定性证明
[Abstract]:With the development of human society, the demand for energy increases rapidly. However, the decrease of traditional energy, energy security and global warming make it necessary for human beings to increase the research on new energy. Wind energy, as a new clean energy, has been paid more and more attention. The main control system control strategy of large wind turbine is studied in this paper. On the basis of fully considering the structure and composition of large wind turbine, the main control system of large wind turbine is designed. It includes torque control system, unified variable propeller control system, independent variable propeller control system and load optimization system. The control strategy is simulated by Bladed wind turbine design simulation software. On this basis, aiming at the difficulty of obtaining the structural parameters of large fan, this paper focuses on the robust and adaptive algorithm. Because the conventional control method is based on the system parameter model, the control strategy is completely based on the system parameter model. The control effect is not ideal when the system parameters change or the load is unscheduled. In this paper, a robust adaptive control algorithm based on backstepping design is proposed for nonlinear fan model. The proposed control strategy is not only robust to unknown time-varying parameters of the system model, but also robust. Moreover, it has a good control effect on the disturbance caused by external environment, and it is proved based on Lyapunov stability. For the nonlinear characteristics of fan system, this paper also proposes a robust neural network algorithm for wind turbine torque control strategy. Compared with the robust adaptive algorithm, the proposed scheme has a lower limit on the controller output. The stability of the scheme is also proved based on Lyapunov stability.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM315;TP273

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