神经网络PID控制器在热网流量调节中的应用
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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。
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.
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。
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.
引文
[1] 邹平华.借鉴俄罗斯经验积极发展我国集中供热事业.暖通空调,2000,30(4):33-37.
[2] 杨治金.丹麦的集中供热.区域供热,2000,1(6):27-30.
[3] 韦新东,尹军,全贞花.日本的集中供(冷)系统的发展现状.吉林建筑工程学院学报,2001,1(1):28-30.
[4] Molodezhnikova L. I., Konishev A. E., Yusupova Yu. B.. Problem Solving Search for Energy Saving in Heat Networks. Modem Techniques and Technology, 2001. MTT 2001. Proceedings of the 7th International Scientific and Practical Conference of Students, Post-graduates and Young Scientists, Tomsk, 2001.
[5] Jacimovic Branislav, Zivkovic Branislav, Genic Srbislav et al. Supply Water Temperature Regulation Problems in District Heating Network with Both Direct and Indirect Connection. Energy and Buildings, 1998, 28(3): 317-322.
[6] Fredrik Wernstedt, Paul Davidsson. An Agent-Based Approach to Monitoring and Control of District Heating Systems. Developments in Applied Artificial Intelligence: 15th International Conference on Industrial and Engineering. Applications of Artificial Intelligence and Expert Systems, Australia: Cairns, 2002.
[7] Bojic M., TrifunovicN., Gustafsson S.I.. Mixed 0-1 Sequential Linear Programming Optimization of Heat Distribution in a District-heating System. Energy and Buildings, 32(3): 309-317.
[8] 刘贺明.中国城市集中供热发展与改革情况.区域供热,2003,1(3):4-8.
[9] 黄文,管昌生.城市集中供热研究现状及发展趋势.国外建材科技,2004,25(5):78-80.
[10] 唐卫.热力站自动监控系统基本思路与控制模式分析.区域供热,2001,1(5):4-13.
[11] 江亿.管网可调性和稳定性的定量分析.暖通空调,1997,27(3):1-7.
[12] 王耕田,王听琦.城市集中供热网智能协调控制系统.测控技术,2000,19(10):24-26.
[13] 贺平,孙刚.供热工程.北京:中国建筑工业出版社,2002.
[14] 贾文姣,李素芬.热电厂集中供热网最佳供回水温度及运行调节的研究.节能,2002,1(4):9-12.
[15] 石兆玉.供热系统运行调节与控制.北京:清华大学出版社,1998.
[16] 徐丽娜.神经网络控制.北京:电子工业出版社,2003.
[17] 易继楷,候媛彬.智能控制技术.北京:北京工业大学出版社,2001.
[18] Wessels L.F.A., Barnard E.. Avoiding False Local Minima by Proper Initialization of Connections. Neural Networks, IEEE Transactions. 1992, 3(6):899 - 905.
[19] Xiao-Hu Yu. Can Backpropagation Error Surface not have Local Minima. Neural Networks, IEEE Transactions. 1992, 3(6): 1019-1021.
[20] 孙德保,高超.一种实用的克服局部极小的BP算法研究.信息与控制,1995,24(5),283-287.
[21] 蒋伟进,张晓琪,张常凡,许宇胜,孙星明.嵌入演化策略的NN混合学习算法研究.第五届全球智能控制与自动化大会,杭州,2004.
[22] 张伟标.基于混合混沌优化法的BP神经网络算法.上海工程技术学院学报,2004,18(1):85-87.
[23] 苏高利,邓芳萍.论基于MATLAB语言的BP神经网络的改进算法.科技通报,2003,19(2):130-135.
[24] 李丽霞,王彤,范逢曦.早停止策略在BP神经网络中的应用.数理医药学杂志,2004,17(2):165-167.
[25] 张文鸽,吴泽宁,逯洪波.BP神经网络的改进及其应用.河南科学,2003,21(2):202-206.
[26] 秦斌,吴敏,王欣,王腾.基于模糊神经网络的多变量解耦控制.小型微型计算机系统,2002,23(5):561-564.
[27] 涂承媛.基于知识的多变量非线性系统变结构解耦控制.北京工业大学学报,2001,27(4):447-450.
[28] 从延奇,谢君,张云.基于神经网络的多变量非线性自适应解耦控制研究.微计算机信息,2004,20(3):22-23.
[29] 李明,林永君,马永光.自适应神经元非模型多变量系统解耦控制.计算机仿真,2003,20(3):68-71.
[30] Iwasa K., Morizumi N., Omatu S.. Pressure Control in a Plant Generating Chloride by Neural Network PID Control. Neural Networks, 1995. Proceedings., IEEE International Conference on, Perth. 1995.
[31] Huailin Shu, Xiucai Guo, Hua Shu. PID Neural Networks in Multivariable Systems. Intelligent Control, 2002. Proceedings of the 2002 IEEE International Symposium on, vancouver, 2002.
[32] Akhyar S., Omatu S.. Neuromorphic Self-tuning PID Controller. Neural Networks, 1993., IEEE International Conference on, San Francisco, 1993.
[33] Kalogirou Soteris A., Panteliou Sofia, Dentsoras Argiris. Modeling of Solar Domestic Water Heating Systems Using Artificial Neural Networks. Solar Energy, 1999, 65(6): 335-342.
[34] Ohnishi Y., Yamamoto T.. A Design of Nonlinear PID Controllers With a Neural-net Based System Estimator. Industrial Electronics Society, 2003. IECON '03. The 29th Annual Conference of the IEEE, Virginia, 2003.
[35] Leszek Kieltyka, Robert Kuceba, Adam Sokolowski. Application of Neural Network Topologies in the Intelligent Heat Use Prediction System. Artificial Intelligence and Soft Computing - ICAISC 2004: 7th International Conference, Poland: Zakopane, 2004.
[2] 杨治金.丹麦的集中供热.区域供热,2000,1(6):27-30.
[3] 韦新东,尹军,全贞花.日本的集中供(冷)系统的发展现状.吉林建筑工程学院学报,2001,1(1):28-30.
[4] Molodezhnikova L. I., Konishev A. E., Yusupova Yu. B.. Problem Solving Search for Energy Saving in Heat Networks. Modem Techniques and Technology, 2001. MTT 2001. Proceedings of the 7th International Scientific and Practical Conference of Students, Post-graduates and Young Scientists, Tomsk, 2001.
[5] Jacimovic Branislav, Zivkovic Branislav, Genic Srbislav et al. Supply Water Temperature Regulation Problems in District Heating Network with Both Direct and Indirect Connection. Energy and Buildings, 1998, 28(3): 317-322.
[6] Fredrik Wernstedt, Paul Davidsson. An Agent-Based Approach to Monitoring and Control of District Heating Systems. Developments in Applied Artificial Intelligence: 15th International Conference on Industrial and Engineering. Applications of Artificial Intelligence and Expert Systems, Australia: Cairns, 2002.
[7] Bojic M., TrifunovicN., Gustafsson S.I.. Mixed 0-1 Sequential Linear Programming Optimization of Heat Distribution in a District-heating System. Energy and Buildings, 32(3): 309-317.
[8] 刘贺明.中国城市集中供热发展与改革情况.区域供热,2003,1(3):4-8.
[9] 黄文,管昌生.城市集中供热研究现状及发展趋势.国外建材科技,2004,25(5):78-80.
[10] 唐卫.热力站自动监控系统基本思路与控制模式分析.区域供热,2001,1(5):4-13.
[11] 江亿.管网可调性和稳定性的定量分析.暖通空调,1997,27(3):1-7.
[12] 王耕田,王听琦.城市集中供热网智能协调控制系统.测控技术,2000,19(10):24-26.
[13] 贺平,孙刚.供热工程.北京:中国建筑工业出版社,2002.
[14] 贾文姣,李素芬.热电厂集中供热网最佳供回水温度及运行调节的研究.节能,2002,1(4):9-12.
[15] 石兆玉.供热系统运行调节与控制.北京:清华大学出版社,1998.
[16] 徐丽娜.神经网络控制.北京:电子工业出版社,2003.
[17] 易继楷,候媛彬.智能控制技术.北京:北京工业大学出版社,2001.
[18] Wessels L.F.A., Barnard E.. Avoiding False Local Minima by Proper Initialization of Connections. Neural Networks, IEEE Transactions. 1992, 3(6):899 - 905.
[19] Xiao-Hu Yu. Can Backpropagation Error Surface not have Local Minima. Neural Networks, IEEE Transactions. 1992, 3(6): 1019-1021.
[20] 孙德保,高超.一种实用的克服局部极小的BP算法研究.信息与控制,1995,24(5),283-287.
[21] 蒋伟进,张晓琪,张常凡,许宇胜,孙星明.嵌入演化策略的NN混合学习算法研究.第五届全球智能控制与自动化大会,杭州,2004.
[22] 张伟标.基于混合混沌优化法的BP神经网络算法.上海工程技术学院学报,2004,18(1):85-87.
[23] 苏高利,邓芳萍.论基于MATLAB语言的BP神经网络的改进算法.科技通报,2003,19(2):130-135.
[24] 李丽霞,王彤,范逢曦.早停止策略在BP神经网络中的应用.数理医药学杂志,2004,17(2):165-167.
[25] 张文鸽,吴泽宁,逯洪波.BP神经网络的改进及其应用.河南科学,2003,21(2):202-206.
[26] 秦斌,吴敏,王欣,王腾.基于模糊神经网络的多变量解耦控制.小型微型计算机系统,2002,23(5):561-564.
[27] 涂承媛.基于知识的多变量非线性系统变结构解耦控制.北京工业大学学报,2001,27(4):447-450.
[28] 从延奇,谢君,张云.基于神经网络的多变量非线性自适应解耦控制研究.微计算机信息,2004,20(3):22-23.
[29] 李明,林永君,马永光.自适应神经元非模型多变量系统解耦控制.计算机仿真,2003,20(3):68-71.
[30] Iwasa K., Morizumi N., Omatu S.. Pressure Control in a Plant Generating Chloride by Neural Network PID Control. Neural Networks, 1995. Proceedings., IEEE International Conference on, Perth. 1995.
[31] Huailin Shu, Xiucai Guo, Hua Shu. PID Neural Networks in Multivariable Systems. Intelligent Control, 2002. Proceedings of the 2002 IEEE International Symposium on, vancouver, 2002.
[32] Akhyar S., Omatu S.. Neuromorphic Self-tuning PID Controller. Neural Networks, 1993., IEEE International Conference on, San Francisco, 1993.
[33] Kalogirou Soteris A., Panteliou Sofia, Dentsoras Argiris. Modeling of Solar Domestic Water Heating Systems Using Artificial Neural Networks. Solar Energy, 1999, 65(6): 335-342.
[34] Ohnishi Y., Yamamoto T.. A Design of Nonlinear PID Controllers With a Neural-net Based System Estimator. Industrial Electronics Society, 2003. IECON '03. The 29th Annual Conference of the IEEE, Virginia, 2003.
[35] Leszek Kieltyka, Robert Kuceba, Adam Sokolowski. Application of Neural Network Topologies in the Intelligent Heat Use Prediction System. Artificial Intelligence and Soft Computing - ICAISC 2004: 7th International Conference, Poland: Zakopane, 2004.