论文部分内容阅读
将迭代学习方法、递增式学习方法引入电力系统可控基金项目 :国家自然科学基金 ( 5 963 70 5 0 )、电力工业部、东北电力集团联合资助。串补非线性控制器的参数整定 ,并根据实际电力系统的强非线性和动态过程等特点 ,将这些学习方法加以改进 :将离线迭代学习改进为在线等周期学习 ,再改进为在线非等周期学习 ;将离线递增式学习法的定义在连续集上的目标函数变为“点”目标函数 ,从而可进行在线学习 ;在此基础上 ,为了使学习参数在系统遭受大扰动时有满意的效果 ,在学习方法中利用了非线性特性。改进后的学习方法高效、简便、实用、易行 ,为控制器参数的整定提供了新方法。数字仿真结果表明 :在同样的计算条件下 ,非等周期迭代学习方法优于等周期学习方法 ,递增式学习方法优于非等周期迭代学习方法。控制器采用学习参数将有更好的品质特性 ,具有较好的动态性能和较强的鲁棒性。
The iterative learning method, incremental learning method is introduced into the controllable fund project of power system: National Natural Science Foundation of China (5 963 70 5 0), Ministry of Electric Power Industry, Northeast Power Group jointly funded. The parameters of the non-linear controller are adjusted in series, and these learning methods are improved according to the characteristics of strong nonlinearity and dynamic process of the actual power system. The offline iterative learning is improved to the online periodic learning and then to the online non-equal cycle Learning; the offline incremental learning method defined in the continuous set of the target function into a “point” objective function, which can be online learning; on this basis, in order to make the learning parameters in the system suffered a great disturbance with satisfactory results , In the learning method to take advantage of the nonlinear characteristics. The improved learning method is efficient, simple, practical and easy to implement, which provides a new method for tuning the controller parameters. The numerical simulation results show that, under the same computing conditions, the non-equal period iterative learning method is superior to the equal period learning method, and the incremental learning method is superior to the non-equal period iterative learning method. The learning parameters of the controller will have better quality characteristics, better dynamic performance and stronger robustness.