摘要
针对人为选取热工系统稳态分量不准确,造成建模精度下降的问题,提出一种基于IATLBO(improved adaptive teaching-learning-based optimization algorithm)与稳态初值寻优的波动热工系统建模方法。方法将系统稳态分量与模型参数均看作未知量,依据智能优化算法寻优过程中所分配的系统稳态分量,动态的对系统输入输出数据进行稳态分量的剔除,从而有效的避免人为选取所产生的误差;寻优过程选取智能优化算法为教学优化算法,结合真实教学过程,对算法进行了改进,并应用标准测试函数进行仿真,验证了算法的高效性。对某波动热工系统进行建模,表明了上述方法的有效性。
Aiming at the problem of inaccurate selection of steady-state components of thermal system,which results in the decline of modeling accuracy,a modeling method for fluctuating thermal system based on IATLBO and steady-state initial value optimization is proposed.In this method,the steady-state components and model parameters of the system are regarded as unknown variables.According to the steady-state components allocated in the optimization process of intelligent optimization algorithm,the steady-state components of the input and output data of the system are dynamically eliminated,thus effectively avoiding the errors caused by artificial selection.In the optimization process,teaching-learning-based optimization algorithm(TLBO)is selected as the optimization algorithm in the process of modeling,and combined with the real teaching process.The algorithm was improved and the superiority of the improved algorithm was verified through simulation with standard test function.Modeling of a fluctuating thermal system was carried out.The results show the effectiveness of the method.
作者
尹二新
张同卫
董泽
杨建辉
YIN Er-xin;ZHANG Tong-wei;DONG Ze;YANG Jian-hui(Beijing Guodian Longyuan Environmental Engineering Co.Ltd.,Beijing 100039,China;Hebei Engineering Research Center of Simulation&Optimized Control for Power Generation,North China Electric Power University,Baoding Hebei 071003,China)
出处
《计算机仿真》
北大核心
2021年第1期76-81,共6页
Computer Simulation
关键词
教学优化算法
稳态初值寻优
波动热工系统
传递函数
建模
Teaching-learning-based optimization algorithm
Steady-state initial value optimization
Fluctuating thermal system
Transfer function
Modeling
