摘要
考虑风电机组控制参数相互耦合,为解决控制参数不精确以及控制目标难以兼顾问题,提出一种基于塔架主动阻尼控制的自适应变速控制参数优化方法。首先,基于转矩-转速系统和塔架侧向主动阻尼控制完成数学和动力学建模,并计算主动阻尼增益初始值。其次,为便于整定PI控制参数和分析计算,针对惯性较大的转矩-转速系统采用Routh法将其辨识为低阶惯性系统,进而采用时间加权绝对误差积分准则整定变速系统PI控制参数初始值。最后,为保持最优控制状态,基于灾变遗传算法优化各风速点变速系统PI控制参数和主动阻尼增益,并将其分别与风速拟合构建自适应控制。算例分析通过比较频域特性、控制精度、塔架振动和载荷情况验证了所提方法的有效性。
Considering the mutual coupling of control parameters of wind turbines,in order to solve the problems of inaccurate control parameters and difficulty taking into consideration control objectives,an adaptive variable speed control parameter optimization method based on active damping control of the tower is proposed.Firstly,the mathematical and dynamic model is established based on the torque-speed system and the tower side active damping control,and the initial value of the active damping gain is calculated.Secondly,in order to facilitate the tuning of PI control parameters and analysis and calculation,the Routh method is used to identify the torque-speed system with large inertia as a low-order inertial system,and then the Integrated Time and Absolute Error criterion is used to set the initial value of the PI control parameters of the variable speed system.Finally,in order to maintain the optimal control state,the PI control parameters and active damping gain of the variable speed system at each wind speed point are optimized based on the catastrophic genetic algorithm,and the adaptive control is constructed by fitting them with the wind speed respectively.An example analysis verifies the effectiveness of the proposed method by comparing the frequency domain characteristics,control accuracy,tower vibration,and load conditions.
作者
刘颖明
张书源
王晓东
Liu Yingming;Zhang Shuyuan;Wang Xiaodong(College of Electrical Engineering,Shenyang University of Technology,Shenyang 110870,China)
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2023年第10期296-305,共10页
Acta Energiae Solaris Sinica
基金
国家自然科学基金(51677121)
辽宁省中央引导地方科技发展资金计划(2021JH6/10500166)
揭榜挂帅科技攻关专项(2021020545-JH1/104)。
关键词
风电机组
变速控制
阻尼
参数优化
灾变遗传算法
wind turbines
speed control
damping
parameter optimization
catastrophe genetic algorithm
