基于人工神经网络的自主风-柴油混合动力系统无功控制

R. Bansal, T. Bhatti, V. Kumar
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引用次数: 69

摘要

本文提出了一种基于人工神经网络(ANN)的SVC无功控制器在大范围典型负荷模型参数下的参数整定方法。根据负载电压特性的典型值,采用传统方法对基于PI(比例积分)的无功控制器的增益进行了优化。利用生成的数据,采用误差反向传播训练的多层前馈神经网络方法。将人工神经网络调谐的静态无功补偿器(SVC)控制器应用于隔离型风电-柴油混合动力系统变转差/转速模型的无功控制。给出了典型混合动力系统的暂态响应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reactive power control of autonomous wind-diesel hybrid power systems using ANN
This paper presents an artificial neural network (ANN) based approach to tune the parameters of the SVC reactive power controller over a wide range of typical load model parameters. The gains of PI (proportional integral) based reactive power controller are optimised for typical values of the load voltage characteristics by conventional techniques. Using the generated data, the method of multilayer feed-forward ANN with the error back-propagation training is employed. An ANN tuned static var compensator (SVC) controller has been applied to control the reactive power of variable slip/speed model of isolated wind-diesel hybrid power system. Transient responses of sample hybrid power system have also been presented.
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