【作者】 王巍；

【导师】 李琦；

【作者基本信息】 大连理工大学 ， 电气工程（专业学位）， 2019， 硕士

【摘要】 低真空供热是一种能够有效减少热源侧余热损失的节能供热方案,但由于其技术简单,自动控制系统的构建工作往往被人忽视。本文提出了一种低真空供热智能化控制的新方案,使用就地执行装置替代工人手动操作,应用远程控制系统实现低真空首站的无人值守与调度中心的集中控制,应用智能控制模块实现热网的智能预测前馈控制。这样既避免了工人在就地操作可能遇到的人身危险,也提高了控制精度与科学性。本文完成的智能控制方案设计包括了控制系统的硬件设计、软件组态设计、热网负荷预测分析以及智能控制模块的设计等四个部分的内容。硬件设计包括了控制模块选型、控制柜布局设计、供电冗余设计和通讯网络设计;软件组态设计主要包括了数据库组态、控制器算法组态以及图形组态;热网负荷预测分析使用了SPSS工具软件对真实热网负荷需求进行了线性回归分析,得到了关于天气状况的负荷预测关系函数;智能控制模块的设计利用了Python工具进行了智能算法的编写,实现了供热工艺流程的全自动智能前馈控制。最终达到了提升低真空供热控制精度的目的。本文应用和利时MACSV技术、线性回归分析技术、Python网络爬虫与嵌入技术解决了低真空供热的智能控制问题,实现了热网的精确和科学运行,解决了热电企业中普遍存在的问题,为供热企业的经济运行提供了保障。同时,本文还提出了在DCS系统上进行功能扩展的思路与可行方案,可为热电企业在原有系统的升级改造方面提供助益。更多还原

【Abstract】 Low-vacuum heating is an energy-saving heating scheme that can effectively reduce heat loss on the heat source side.However,due to its simple technology,the construction of automatic control systems is often overlooked.This paper proposes a new scheme for intelligent control of low vacuum heating,using local execution device instead of manual operation of the worker,applying remote control system to realize centralized control of unattended and dispatch center of low vacuum first station,applying intelligent control module Realize intelligent predictive feedforward control of the heating network.This not only avoids the personal danger that workers may encounter when operating on the spot,but also improves the precision and scientific control.The intelligent control scheme design completed in this paper includes four parts: hardware design of the control system,software configuration design,thermal network load forecasting analysis and intelligent control module design.The hardware design includes control module selection,control cabinet layout design,power supply redundancy design and communication network design;software configuration design mainly includes database configuration,controller algorithm configuration and graphic configuration;heat network load forecast analysis use The SPSS tool software performs linear regression analysis on the real heat load requirements,and obtains the load forecasting relationship function about the weather conditions.The design of the intelligent control module uses the Python tool to write the intelligent algorithm and realizes the heating process.Fully automatic intelligent feedforward control.Finally,the purpose of improving the accuracy and scientificity of low vacuum heating control is achieved.In this paper,the application of Hollysys MACSV technology,linear regression analysis technology,Python web crawling and embedding technology solves the problem of intelligent control of low vacuum heating,realizes the precise and scientific operation of the heating network,and solves the common problems in thermoelectric enterprises.It provides guarantee for the economic operation of heating enterprises.At the same time,this paper also proposes the idea and feasible solution for function expansion on the DCS system,which can provide benefits for the thermal power enterprise in upgrading and renovating the original system.更多还原