燃煤电厂锅炉燃烧系统的回声状态网络建模  

Echo State Network Modeling of Boiler Combustion System in Coal-fired Power Plant

作　　者：孙灵芳[1] 麻世博 赵瑞[1] 赵光军 

机构地区：[1]东北电力大学自动化工程学院,吉林省吉林市132012 [2]广州华润热电有限人司,广州市511455

出　　处：《电力建设》2014年第3期97-101,共5页Electric Power Construction

摘　　要：燃煤电厂的锅炉燃烧系统是一个复杂而又重要的系统,建立其精确、普适的模型很困难。首先对现场采集的锅炉燃烧系统输入输出数据进行处理和优选,以用于燃烧系统的建模;然后将一种新型的递归神经网络———回声状态网络(echo state network,ESN)进行改进,提高了网络的精度和适应性,并且将改进的回声状态网络应用于燃烧系统静态建模,与其他4种神经网络建立的静态模型比较,前者适应性更强;最后将改进的回声状态网络应用于锅炉燃烧系统的动态建模,与静态建模相比,模型的适应性更好,能够进行长时间的预测。： The boiler combustion system in coal-fired power plant is a so complicated and important system that it is difficult to build a precise and adaptable model for it. First, the input and output data of combustion system collected from the scene was processed and selected for the combustion system modeling. Second, the Echo State Network （ESN）, which was a new type of Recurrent Neural Network, was improved and its precision and adaptability were increased. Then, the improved ESN was applied to building static model of combustion system, which had best adaptability compared with models founded with other four Neural Networks. Last, the improved ESN was applied to building dynamic model of combustion system, which had better adaptability and was more suitable for long-time prediction compared with static model.

关 键 词：燃煤电厂 锅炉燃烧系统 回声状态网络(ESN) 静态模型 动态模型 

分 类 号：TM621.2[电气工程—电力系统及自动化]