计算机与现代化

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基于神经网络逆控制的水轮机调节系统

  

  1. (河海大学能源与电气学院,江苏南京211100)
  • 收稿日期:2019-05-28 出版日期:2020-02-13 发布日期:2020-02-13
  • 作者简介:陈艳琳(1995-),女,江苏宿迁人,硕士研究生,研究方向:水轮机调节系统控制,E-mail: 503904848@qq.com; 李志华(1965-),男,江苏南京人,教授,硕士生导师,博士,研究方向:人工智能,复杂系统故障诊断,E-mail: zhli@hhu.edu.cn; 谢雪涵(1995-),女,硕士研究生,研究方向:多车协同定位,E-mail: 15250960698@163.com。
  • 基金资助:
    江苏省自然科学基金资助项目(BK20151500)

Hydraulic Turbine Governing System Based on Neural Network Inverse Control

  1. (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)
  • Received:2019-05-28 Online:2020-02-13 Published:2020-02-13

摘要: 根据神经网络对非线性系统模型的辨识能力,将其与自适应逆控制相结合,对水轮发电机组的逆模型进行建模,构建一种新的水轮机调节系统。该方案以逆系统以及系统辨识理论为基础,以水轮发电机组作为被控对象,分别针对其频率和负荷扰动,建立神经网络在线逆控制器,对系统进行调控,并将仿真结果与传统PID控制进行比较。从仿真结果可以看出,所提的控制方案能够实现对水轮发电机组的有效控制,使系统具有较好的动态性能和鲁棒性。

关键词: 系统辨识, 神经网络, 逆建模, 水轮发电机组

Abstract: According to the identification ability of neural network to the model of non-linear system, the inverse model of hydraulic turbine generator unit is modeled by combining the neural network with the adaptive inverse control, and a new turbine regulating system is born. Based on the theory of inverse system and system identification, a neural network inverse controller is established for the frequency and load disturbance of the hydraulic turbine generator unit, and the simulation results are compared with the traditional PID control. From the simulation results, it can be seen that the proposed control scheme can effectively control the hydraulic turbine generator unit and make the system have better dynamic performance and robustness.

Key words: system identification, neural network, inverse model, hydraulic turbine generator unit

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