基于混沌粒子群算法的水轮机调速系统参数辨识及建模试验

发布时间：2018-02-10 18:09

本文关键词： 水轮机调速器建模参数测试满意度函数参数辨识混沌粒子群算法　出处：《长江科学院院报》2016年08期 　论文类型：期刊论文

【摘要】：针对粒子群算法存在的后期收敛速度慢和易陷入局部最优等缺点,引入收缩因子和混沌优化思想对其改进,并将其应用于调速系统被控对象有关参数辨识问题上。提出一种水轮机调速系统参数辨识满意度函数设计的新方法,该方法直接计算系统响应的上升时间、调节时间、反调峰值功率、反调峰值时间等品质参数,并以系统总体满意度作为满意度函数。对某混流式水轮机调速器控制参数进行实测并对机组引水道参数进行辨识,试验结果表明:仿真数据能够准确模拟机组负荷的频率阶跃扰动响应,可以满足电网稳定性计算要求;在系统受到较大干扰时,该算法仍具有精确的参数辨识能力和很高的收敛效率。
[Abstract]:Aiming at the shortcomings of particle swarm optimization (PSO), such as slow convergence rate and easy to fall into local optimization, the shrinkage factor and chaos optimization are introduced to improve PSO. A new method for designing parameter identification satisfaction function of hydraulic turbine speed regulating system is presented, which can directly calculate the rising time and adjusting time of system response. The control parameters of a Francis turbine governor are measured and identified by taking the overall satisfaction of the system as the satisfaction function, and the parameters of the pilot channel are identified by measuring the control parameters of the Francis turbine governor, such as the inverse peak power, the time of the reverse peak, and the total satisfaction degree of the system as the satisfaction function. The experimental results show that the simulation data can accurately simulate the frequency step disturbance response of unit load and can meet the requirements of power network stability calculation. This algorithm still has the accurate parameter identification ability and the very high convergence efficiency.
【作者单位】：国家电网辽宁省电力有限公司电力科学研究院;东北电网有限公司;
【分类号】：TV734.4

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