Identification of non-linear dynamic systems in power plants

C. Alippi, V. Piuri
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引用次数: 2

Abstract

Problems related to the identification of non-linear systems are analysed by considering as a case study the neural modelling of the furnace and the superheater systems. As far as the furnace is concerned, identification addresses neural modelling of the total heat reaching the evaporator; the process is not dynamic because the heat generation is particularly rapid. Conversely, this is not the case in a superheater where dynamics play a relevant role: identification of the steam and the flue gas temperatures requires specific recurrent type neural models. Identification of such systems, belonging to a one-through 320 MW group, are the first step in developing computationally simple distributed nonlinear neural models for the whole plant. Issues related to training data extraction, training algorithms and stability are taken into account.
电厂非线性动态系统辨识
以加热炉和过热器系统的神经网络建模为例,分析了非线性系统辨识的相关问题。就炉而言,识别涉及到达蒸发器的总热量的神经模型;这个过程不是动态的,因为热的产生特别快。相反,在动力学起相关作用的过热器中,情况并非如此:识别蒸汽和烟气温度需要特定的循环型神经模型。这类系统属于一通320兆瓦组,识别它们是为整个电厂开发计算简单的分布式非线性神经模型的第一步。考虑到与训练数据提取、训练算法和稳定性相关的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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