基于IOLBBO算法的水轮机非线性模型参数辨识

Parameter Identification of Hydroturbine Nonlinear Model Based on Improved Orthogonal Learning Biogeography-based Optimization

为提高考虑弹性水击模型的水轮机系统非线性模型参数辨识的精度、速度、稳定度,采用改进正交学习生物地理学算法(IOLBBO)对该模型进行参数辨识。IOLBBO利用佳点集方法的遍历性,初始化栖息地特征变量;引入精英保留策略,提高算法运行效率;融合正交学习(OL)策略,提高算法全局寻优能力。基于某水轮机动态试验数据的参数辨识计算及对比分析,表明IOLBBO算法可用于水轮机系统非线性模型参数实测辨识,与GA、PSO、QPSO、BBO算法相比,收敛速度更快、参数辨识精度更高、算法更稳定,为电力系统的参数辨识提供了一种新方法。

In order to improve the calculation accuracy, speed and stability for parameter identification in nonlinear models of hydro-turbine system, which the elastic water-hammer model is considered, the improved biogeography-based optimization algorithm based on orthogonal learning （IOLBBO） is proposed. The method of good point set is used to ini- tialize characteristic variables of habitats. The elitism strategy is introduced to improve the efficiency of algorithm＇s oper- ation, and orthogonal learning （OL） strategy is brought to enhance the ability of algorithm＇s global optimization. Based on the comparison of test data with IOLBBO, genetic algorithm, particle swarm optimization, and quantum particle swarm optimization, the conclusion can he obtained that the IOLBBO algorithm can be used for parameter identification of turbine system＇s nonlinear model, and it has advantages of faster convergence and higher precision, which provides a new way for parameters identification of power system.