水轮机智能调速系统数学模型仿真及参数辨识

为建立与电网稳定计算有关的水轮机调速系统数学模型及模型参数测量辨识,提出一种基于自适应人工鱼群-神经网络技术并适用于水轮机调速系统控制的新技术,建立智能调速系统数学模型,使之符合实际调节及微机优化控制。分析了该模型组成部分的传递函数,提出采用自适应人工鱼群算法来弥补人工鱼群和神经网络算法的不足,阐述了自适应人工鱼群算法-神经网络优化器的算法。给出了自适应人工鱼群优化算法参数辨识算法设计和实现步骤。利用Matlab和自适应人工鱼群算法进行模型参数辨识,对一次调频和二次调节试验过程进行仿真并与实测对比。结果表明,仿真值与实测值相当接近,所研制的自适应人工鱼群-神经网络优化器,达到了优化PID调节器控制输出量的目标;所建立的调速系统数学模型真实地反映调速系统在机组并网工况下的调节特性,说明该方法原理正确,可用于优化控制。

Hydraulic turbine intelligent governing system mathematical model and its parameters identification

In order to establish the mathematical model of hydraulic turbine governing system for grid stability calculation and model parameter idemification,a novel and suitable technique based on adaptive artificial fish school algorithm-neural network is proposed.The mathematical model is established to adapt to the practical regulation as well as the microcomputer-based control.Its transfer funotions are analyzed and the adaptive artificial fish school algorithm is proposed to avoid the shortage of the artificial fish school algorithm and neural network algorithm.The adaptive artificial fish school algorithm-BP neural network is expounded and the design and implementation of parameter identification algorithm are described.The model parameters are identified with Matlab and the proposed algorithm.The primary frequency regulation and secondary regulation are simulated and tested,and the comparison of results shows that,the simulative values are very close to the measurements:the developed controller based on the adaptive artificial fish school algorithm.BP neural network reaches its control objective of optimizing the control output of the PID regulator;and the established model truly reflects the performance of the governing system under the operating mode of unit connected to grid,which indicates its principle is correct and suitable for optimal control.