基于先进优化算法的LADRC风力发电过程控制研究

发布时间：2018-03-24 23:23

本文选题：线性自抗扰控制　切入点：双馈风电机组　出处：《华北电力大学》2015年硕士论文

【摘要】：新能源逐步代替煤、石油、天然气等化石能源是能源可持续发展战略的关键,而同时风能在多种新能源中有着举足轻重的地位。由于变速恒频双馈风力发电机具有风能转换效率高、控制方式灵活、功率因数实时可控等优点,现已成为当今风电行业内的主流机型,针对其的交流励磁的控制方式也成为风电控制技术中的关键技术。大多数情况下,针对双馈风力发电机组的励磁控制,采用的是在矢量变换的基础上进行经典控制。但是这种控制方法需要精确的系统模型,也需要系统具有稳定的参数,但风电系统往往工作于环境易变的区域,当外部环境改变时(温度、湿度等),机组的参数往往随之改变,这就会大大降低系统的控制效果。本文在大量研究分析国内外风电行业文献的基础上,重点研究了双馈风电机组的运行控制技术。具体包括风电用网侧PWM的控制、变桨距恒功率控制、最大风能捕获控制以及空载并网控制。在掌握各个阶段控制原理的基础上,针对风电机组实际运行中经常遇到的问题,诸如调节速度慢、动态响应时间长、风电机组参数的突变、风速突变等问题,利用线性自抗扰控制器善于处理系统出现内部扰动、外部扰动的优点,将其应用于双馈机组网侧PWM、变桨距恒功率运行、最大风能追踪、空载并网等运行阶段,以加快系统动态响应速度,提高系统的抗干扰能力。但是线性自抗扰控制器的参数寻优往往通过经验法或者试凑法来实现,这样不仅很难找到系统最优参数,同时也会浪费大量的时间。本文采用自适应免疫粒子群优化算法对线性自抗扰控制器的参数进行参数寻优。免疫粒子群算法在结合粒子群算法与免疫算法的优点的同时,克服了相互的缺点,从而使得算法在具有快速性的同时,避免其陷入局部极小值。同时在算法中,针对惯性因子,变异概率、交叉概率的选取,采用自适应调节的方式,同样会加快算法的寻优速度。最后在Matlab/Simulink中仿真研究,将基于自适应免疫粒子群优化的线性自抗扰控制器应用于风电机组运行的各个阶段。通过与传统控制以及手动寻优的线性自抗扰控制相对比,验证了所设计方法的准确、高效。
[Abstract]:Gradually replacing fossil energy such as coal, oil and natural gas with new energy is the key to the strategy of sustainable energy development. At the same time, wind energy plays an important role in a variety of new energy sources. Because variable speed constant frequency doubly-fed wind turbine has the advantages of high wind energy conversion efficiency, flexible control mode, real-time controllable power factor, and so on. Nowadays, it has become the mainstream type in wind power industry, and the control mode of AC excitation is also the key technology in wind power control technology. In most cases, the excitation control of doubly-fed wind turbine is aimed at the excitation control of doubly-fed wind turbine. The classical control is based on vector transformation, but this control method needs accurate system model and stable parameters. However, wind power system often works in an area where the environment is changeable. When the external environment changes (temperature, humidity, etc.), the parameters of the unit often change, which will greatly reduce the control effect of the system. The operation control technology of doubly-fed wind turbine is studied in detail, including the control of grid-side PWM, variable pitch constant power control, maximum wind power capture control and no-load grid-connected control. In view of the problems often encountered in the actual operation of wind turbine, such as slow regulating speed, long dynamic response time, sudden change of wind turbine parameters and sudden change of wind speed, the linear ADRC controller is used to deal with the internal disturbance of the system. In order to speed up the dynamic response of the system, the advantages of external disturbance are applied to the operation stages such as PWM, variable pitch constant power operation, maximum wind energy tracking, no-load grid-connected operation, etc, in order to speed up the dynamic response of the system. But the parameter optimization of linear ADRC is usually realized by empirical method or trial and error method, which is not only difficult to find the optimal parameters of the system, but also to improve the anti-jamming ability of the system. At the same time, it will also waste a lot of time. In this paper, the parameters of linear ADRC are optimized by using adaptive immune particle swarm optimization algorithm. The immune particle swarm optimization algorithm combines the advantages of particle swarm optimization and immune algorithm at the same time. It overcomes the shortcoming of each other and makes the algorithm avoid falling into local minima while it is fast. At the same time, aiming at the selection of inertia factor, mutation probability and crossover probability, the algorithm adopts adaptive adjustment method. It will also speed up the optimization of the algorithm. Finally, the simulation in Matlab/Simulink, The linear active disturbance rejection controller based on adaptive immune particle swarm optimization is applied to every stage of wind turbine operation. Compared with the traditional control and manual optimization linear active disturbance rejection control, the proposed method is proved to be accurate and efficient.
【学位授予单位】：华北电力大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TM614

【相似文献】