【作者】 滕琳琳；

【导师】 杨建华；

【作者基本信息】 大连理工大学 ， 控制理论与控制工程， 2005， 硕士

【摘要】 本文以实验室热网实验装置的控制为基础,旨在对集中供热网的运行调节和控制方法进行研究。系统地讨论了神经网络技术在集中供热系统流量调节中的应用,主要进行了以下几方面的工作: 对国内外集中供热系统的运行管理、供热调节和控制技术进行了概述,并针对我国集中供热网的特点和存在的问题提出了适合国情的基于温度的流量控制方案。 系统地讨论了多变量解耦控制的方法,对于热网这种具有强耦合特性的系统,将具有简单控制结构特性的传统的PID控制器和能解决非线性系统控制问题的BP神经网络算法相结合,提出了用具有解耦能力的神经网络PID多变量控制器,并随后深入探讨了控制器的特性和具体的设计思想。另外,在对BP算法和控制器结构改进的基础上,加入了非线性预测模型,来解决热网的大滞后问题。 简要介绍了实验室热网硬件组成、实时监控系统平台的设计、带非线性预测模型的神经网络PID多变量控制器的具体实现。将整体的控制方法应用到实验室热网的实际控制中去,通过调节用户一次网侧的流量来实现对各用户二次网回水温度的控制。大量的实验数据表明,与标准的BP神经网络控制器、不具解耦能力的控制器和不具预测能力的控制器相比较,具有解耦能力的带非线性预测模型的神经网络PID多变量控制器对于解决集中供热网的非线性、强耦合和大滞后的问题比较有效,可以实现全网热量的合理优化调度,提高集中供热网的供热质量,最终达到均匀供热的目标。 在文章的最后,对于本人所作的工作进行了总结,并对后续工作进行了展望。更多还原

【Abstract】 On the basis of Laboratory-scale heating system control, this paper aims at the study of running adjusting and control methods for District Heating Network. It systematically discusses the application of Neural Networks technology to District Heating System to adjust the flux. includes:The first part of this paper summarizes the running management, adjusting and control technology at home and abroad. By analyzing the characteristics and possible problems of District Heating System in China, the flow control strategy based on temperature is presented, which adapts to the situation of our country.The second part of this paper discusses the methods of multivariable decoupling control by the numbers. In accordance with the District Heating System having strong coupling characteristic, this paper combines the conventional PID controller with a simple structure and the BP Neural Networks with the ability of solving nonlinear system control problem. Then, a Neural Networks PID multivariable controller with the ability of solving coupling problem is proposed. And then the characteristic and design idea of the controller are thoroughly discussed. Furthermore, on the basis of the improvement on BP algorithm and the controller’s structure, this paper adds a nonlinear prediction model to solve the time-delay problem.The last part of this paper introduces the composing of the Laboratory-scale heating system, the design of the real time monitoring platform and the implementation of the Neural Networks PID multivariable controller with a nonlinear prediction model. The macro control method is applied to the actual management and control of the Laboratory-scale heating system. By adjusting the flux of the user’s primary pipe network the user’s backwater temperature of secondary pipe network can be controlled. Large numbers of running results prove the Neural Networks PID multivariable controller with a nonlinear prediction model has a perfect effect of solving nonlinear, strong coupling, huge time-delay problem comparing with normal BP Neural Networks, non-coupling, non-prediction controller and they also prove that the controller can rationally distributes the heat throughout the heating network, and the heating quality is improved. Moreover, the goal of well-proportioned heating is also achieved.In the end. the work done is summarized and future work is prospected.更多还原