摘要
为了提升风电机组的控制精度并降低关键承载部件的载荷情况,通过遗传粒子群方法研究了变速与变桨系统比例积分(proportional integral,PI)控制器的设计。首先,采用泰勒级数将风电机组线性化展开为状态空间方程。然后,通过提取输入、输出和平衡点分别构建变速与变桨系统的数学模型。其次,分别采用Routh法和最小二乘法将变速与变桨系统辨识为低阶惯性系统和惯性时延系统。然后分别基于时间加权绝对误差积分准则和Chien-Hrones-Reswick法整定变速与变桨系统PI控制参数。最后,采用帕累托方法分配控制目标的权重关系并基于遗传粒子群算法优化变速与变桨系统PI控制参数,进而采用最小二乘法将其分别与风速和桨距角拟合构建自适应PI控制。结果表明:在变速控制中能够有效提升输出功率并抑制塔架顶部侧向振动;在变桨控制中能够有效平抑输出功率和发电机转速波动并降低叶片根部载荷情况。可见通过遗传粒子群方法设计的变速与变桨系统PI控制器具有良好的有效性和适用性。
In order to improve the control accuracy of wind turbine and reduce the load of key components,genetic particle swarm optimization algorithm was used to study the design of proportional integral(PI)controller for variable speed and variable pitch systems.First,the wind turbine was linearized expanded into a state space equation using Taylor series.Then,the mathematical models of the variable speed and variable pitch systems were constructed by extracting the input,output and equilibrium points,respectively.Secondly,the Routh method and the least squares method were used to identify the variable speed and variable pitch systems as low-order inertial systems and inertial time-delay systems,respectively.Then the PI control parameters of the variable speed and variable pitch systems were set based on the integration time absolute error criterion and the Chien-Hrones-Reswick method,respectively.Finally,the Pareto method was used to allocate the weight relationships of the control objectives and optimize the PI control parameters of the variable speed and variable pitch systems based on the genetic algorithm particle swarm optimization,and then the least squares method was used to fit them to the wind speed and pitch angle respectively to construct the adaptive PI control.The results show that the variable speed control can improve the output power and suppress the side-side at the tower top,and the variable pitch control can suppress the fluctuation of output power and generator speed and reduce the blade root load.It can be seen that the PI controller of variable speed and variable pitch systems designed by genetic algorithm particle swarm optimization has effectiveness and applicability.
作者
刘颖明
张书源
王晓东
柏文超
LIU Ying-ming;ZHANG Shu-yuan;WANG Xiao-dong;BAI Wen-chao(School of Electrical Engineering,Shenyang University of Technology,Shenyang 100870,China)
出处
《科学技术与工程》
北大核心
2023年第31期13375-13386,共12页
Science Technology and Engineering
基金
国家自然科学基金(52007124)
辽宁省中央引导地方科技发展计划(2021JH6/10500166)
揭榜挂帅科技攻关专项(2021JH1/10400009)。
关键词
风电机组
PI控制
变速控制
变桨控制
遗传粒子群算法
wind turbine
PI control
variable speed control
variable pitch control
genetic particle swarm optimization algorithm
