基于改进遗传算法的水轮发电机振动荷载参数识别

IDENTIFICATION PROCEDURES OF VIBRATION LOADING PARAMETERS OF HYDRAULIC GENERATOR BASED ON IMPROVED GENETIC ALGORITHM

根据水轮发电机现场振动测试实验数据,采用改进的遗传算法研究了水轮发电机运行过程中振动荷载反演问题。与传统的参数反演方法相比,遗传算法并不是基于对目标函数梯度方向搜索,而是在解的整个区域随机搜索.将遗传算法与模拟退火算法相结合,提高了种群在进化过程中个体多样性,可以有效地防止简单遗传算法早熟问题。同时,将遗传算法与梯度优化方法相结合,使得混合型遗传算法有效地解决了梯度算法局部极小问题和简单遗传算法的收敛速度慢问题。工程实际应用表明,采用本文所建立改进遗传算法所反演的水轮发电机振动荷载参数,预报其它振动观测点的位移具有较高的预报精度。

Based on in-situ measured data of hydraulic generator vibration, an improved genetic algorithm is adopted to identify the vibration load of hydraulic generator. Unlike the traditional inverse algorithm, the genetic algorithm randomly searches the inverse problem domain by the objective function itself other than follows the gradient direction of objective function. Combined with simulated annealing, a mixed genetic algorithm is developed to enhance the diversity of populations in the evolution process and effectively protect from premature problem of simple genetic algorithm. Combined with the gradient search, a hybrid genetic algorithm is formed to overcome the local minimum of the gradient search and the slowness of convergence rate of simple genetic algorithms. The practical application of the proposed inverse algorithm shows that the prediction of vibration responses of hydraulic generator is highly accurate according to the identified loading parameters.