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arXiv math/0007063math.OC
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基于自适应反馈线性化的高阶电力系统神经网络镇定/励磁控制

Neural Stabilization/Excitation Control of a High-Order Power System by Adaptive Feedback Linearization

Kingsley Fregene, Diane Kennedy

摘要:

本文讨论了同步电机/无穷大电力系统高阶模型的自适应反馈线性化神经网络控制器的系统设计。电力系统首先建模为一个输入输出非线性离散时间系统近似两个神经网络。该方法允许一个简单的线性极点配置控制器(这本身不是一个神经网络)的设计。指定控制律,使得控制器通过利用由神经系统模型提供的设备参数估计,在每个采样时刻自适应地计算适当的反馈线性化控制律。控制系统也在线自适应。这避免了对电力系统动态和全状态测量的精确知识的要求,以及与对复杂电力系统模型实施分析输入-输出反馈线性化控制相关联的其它困难。此外,一个出发点是从“特设”的方式,其中许多神经控制器已被设计用于电力系统,这里使用的方法有基础的自适应反馈线性化和极点配置控制设计的控制理论概念。仿真结果证明了该控制器在各种运行条件下对单机/无穷大电力系统配置的代表性示例的性能。

英文摘要:

This paper discusses the systematic design of an adaptive feedback linearizing neurocontroller for a high-order model of the synchronous machine/infinite bus power system. The power system is first modelled as an input-output nonlinear discrete-time system approximated by two neural networks. The approach allows a simple linear pole-placement controller (which is itself not a neural network) to be designed. The control law is specified such that the controller adaptively calculates an appropriate feedback linearizing control law at each sampling instant by utilizing plant parameter estimates provided by the neural system model. The control system also adapts itself on-line. This avoids the requirement for exact knowledge of the power system dynamics and full state measurement as well as other difficulties associated with implementing analytical input-output feedback linearizing control for a complex power system model. Furthermore, a departure is made from the `ad hoc' manner in which many neural controllers have been designed for power systems; the approach used here has foundations in control theoretic concepts of adaptive feedback linearization and pole-placement control design. Simulation results demonstrate the performance of this controller for a representative example of a single-machine/infinite bus power system configuration under various operational conditions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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