燃煤机组汽水系统作为一种多专业交叉的复杂系统,具有结构复杂、参数耦合、工况多变等特点,为了响应智慧电厂的发展需求,达到燃煤机组汽水系统智能化诊断的目的,提出了一种基于数据融合的燃煤机组汽水系统智能诊断研究的方法。首先,采用黏菌优化算法(SMA)对变分模态(VMD)算法进行分解层数、收敛容性差和惩罚因子的参数优化,其次采用优化后的VMD算法对燃煤机组汽水系统进行数据的分解,然后采用改进后的双向长短期记忆(Bi-LSTM)算法搭建燃煤机组汽水系统智能化诊断模型,最后以大唐国际某电厂1000MW燃煤机组汽水系统为例进行研究分析,结果表明该智能化诊断模型可以有效实现燃煤机组汽水系统的智能诊断,对燃煤电厂智能化的建设具有重要的实际意义。

Coal-fired units steam water system as a kind of professional cross complex system, with complex structure, parameter coupling, changeable condition, in response to the development of intelligent power plant demand, achieve the purpose of coal-fired unit soda system intelligent diagnosis, puts forward a based on data fusion of the coal-fired units steam water system intelligent diagnosis research method.Firstly, the myxus optimization algorithm (SMA) is used to optimize the number of decomposition layers,tol and alpha, Secondly, the optimized VMD algorithm is used to decompose the data of tthe coal-fired units steam water system, Then, the improved bidirectional long-term and short-term memory (Bi-LSTM) algorithm is used to build the intelligent diagnosis model of the soda water system of coal-fired units, Finally, taking the steam water system of a 1000MW coal-fired unit of a power plant of Datang International as an example for the study and analysis, The results show that the intelligent diagnosis model can effectively realize the intelligent diagnosis of the coal-fired units steam water system, it has important practical significance to the intelligent construction of coal-fired power plants.

新建燃煤电厂智慧化关键技术应用研究与工程示范