引用本文:孟安波，叶鲁卿，殷　豪，梁宏柱，傅　闯，程远楚.遗传算法在水电机组调速器PID参数优化中的应用[J].控制理论与应用,2004,21(3):398~404.[点击复制]
MENG An-bo, YE Lu-qing, YIN Hao, LIANG Hong-zhu, FU Chuang, CHENG Yuan-chu.Application of genetic algorithm in adaptive governor with variable PID parameters[J].Control Theory and Technology,2004,21(3):398~404.[点击复制]
遗传算法在水电机组调速器PID参数优化中的应用
Application of genetic algorithm in adaptive governor with variable PID parameters
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DOI编号  
  2004,21(3):398-404
中文关键词  遗传算法  正交实验法  自适应变参数PID调速器  非线性全数字仿真模型
英文关键词  genetic algorithm  orthogonal method  adaptive governor with variable PID parameters  nonlinear full digital simulation model
基金项目  
作者单位
孟安波，叶鲁卿，殷　豪，梁宏柱，傅　闯，程远楚 华中科技大学 水电与数字化工程学院，湖北武汉 430074；华北水利水电学院 动力系，河南郑州 450008 
中文摘要
      水轮发电控制是一个复杂的动态过程,特别是对大型机组,控制策略和优化算法的选取对机组的性能尤为重要.为了获取不同工况下水轮发电机组调速器的优化参数,首先建立了一种机组的非线性全数字仿真模型.结合调节系统的特点,提出了一种新的反映系统综合性能指标的适应度函数,利用遗传算法对自适应变参数PID调速器的3个参数bt,Td,Tn进行了优化.具体实施优化过程中,采用了C++ Builder编制遗传算法主程序;使用了Matlab中的Simulink工具箱建立机组仿真模型,交互接口通过Matlab中的引擎Engine来实现.仿真实验表明:与正交实验法寻优结果相比较,在机组的不同运行状态、不同工况下遗传算法都能获得更为理想的寻优效果.同时,该优化方法已经应用于某巨型电站的调速器中.
英文摘要
      The hydraulic generating control,especially for the giant generating unit,is a complex and dynamic process.The performance of hydraulic generating unit depends mostly on how to identify the appropriate control strategy and optimization algorithm of the adaptive control parameters.A full digital nonlinear model for the governing system was first built for obtaining the PID control parameters.A new fitness function,which reflects the integrated performance index of hydroelectric regulation system,was created and applied to genetic algorithm for optimizing the control parameters of this kind of adaptive governor with variable PID parameters.The whole control algorithms are mainly composed of two parts: the main programming of genetic algorithm was realized by C(++) Builder,the model of hydraulic generating unit was built by the Simulink tool box in Matlab and their interactive interface was implemented through the engine provided by Matlab.The simulation results show that,compared with the traditional orthogonal method,the genetic algorithm is more effective in any operating state and any set point and the results have now been applied to a huge hydropower plant.