基于变频调速的隧道通风控制系统研究
发布时间：2018-07-25 14:28
【摘要】：随着我国高速公路建设范围的不断拓展，迅速增加的公路隧道里程数直接导致隧道通风系统运营成本增加，节能降耗成为减少隧道通风能耗浪费的重要手段。但是，公路隧道的纵向通风系统具有较大的非线性、时滞性和时变性，很难建立精确的数学模型。本文运用变频调速技术对隧道通风节能系统进行控制，降低过多的电能流失以及设备的损耗，从而降低运营成本。 本文首先对隧道风机传统控制与变频控制进行了比较，说明了变频调速技术在隧道通风控制系统中的可行性。通过对公路隧道通风变频控制系统的研究，文章对隧道通风变频控制系统进行了总体的设计以及数学建模。再以白云隧道为例，根据白云隧道的相关参数和技术指标，对隧道通风变频控制系统的算法以及仿真进行了研究。然后，通过检测白云隧道交通流量以及车速，利用SUMO软件进行仿真。再根据交通流量以及车速的仿真结果，计算出隧道内CO、VI、空气中异味的需风量，通过三者的比较最终得出隧道的需风量。接着通过变频调速技术对隧道内射流风机的输出功率、转速进行调控。通过对隧道风机的传统分档控制和变频调速控制进行能耗对比，验证隧道通风变频控制系统的优越性。 最后采用BP神经网络控制算法来对隧道变频通风系统进行控制，将实时的CO、VI、异味污染物浓度作为BP神经网络的输入样本，将风机的变频输出功率作为神经网络的目标输出样本。利用MATLAB神经网络工具箱进行训练和仿真，，实验结果表明：风机变频输出功率与神经网络的实际输出功率之间的平均误差小于0.2，神经网络模型能够较好地拟合变频通风控制模型，并且具备了较强的泛化能力。
[Abstract]:With the continuous expansion of highway construction scope in China, the rapid increase of highway tunnel mileage directly increases the operating cost of tunnel ventilation system. Energy saving and consumption reduction become an important means to reduce energy waste of tunnel ventilation. However, the longitudinal ventilation system of highway tunnel is nonlinear, time-delay and time-varying, so it is difficult to establish accurate mathematical model. In this paper, the frequency conversion technology is used to control the tunnel ventilation and energy saving system to reduce the excessive loss of electric energy and the loss of equipment, thus reducing the operating cost. In this paper, the traditional control of tunnel fan and frequency conversion control are compared at first, and the feasibility of frequency conversion speed regulation technology in tunnel ventilation control system is explained. Based on the study of frequency conversion control system for highway tunnel ventilation, the overall design and mathematical modeling of frequency conversion control system for tunnel ventilation are carried out in this paper. Taking the Baiyun tunnel as an example, the algorithm and simulation of the frequency conversion control system of the tunnel ventilation are studied according to the relevant parameters and technical indexes of the tunnel. Then, by detecting the traffic flow and speed of Baiyun Tunnel, the simulation is carried out with SUMO software. According to the simulation results of traffic flow and speed, the air demand of COHV I in the tunnel and the air demand of the special smell in the air are calculated. Finally, the air demand of the tunnel is obtained through the comparison of the three. Then, the output power and speed of the tunnel injector fan are regulated by frequency conversion technology. The superiority of the frequency conversion control system of tunnel ventilation is verified by comparing the energy consumption between the traditional control of tunnel fan and the control of variable frequency speed regulation. Finally, the BP neural network control algorithm is used to control the variable frequency ventilation system of the tunnel. The real time COA VI and the odor pollutant concentration are used as the input samples of BP neural network, and the output power of the fan frequency conversion is taken as the target output sample of the neural network. MATLAB neural network toolbox is used for training and simulation. The experimental results show that the average error between fan frequency conversion output power and the actual output power of neural network is less than 0.2, and the neural network model can fit the frequency conversion ventilation control model well. And has strong generalization ability.
【学位授予单位】：长安大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U453.5
本文编号：2144114
[Abstract]:With the continuous expansion of highway construction scope in China, the rapid increase of highway tunnel mileage directly increases the operating cost of tunnel ventilation system. Energy saving and consumption reduction become an important means to reduce energy waste of tunnel ventilation. However, the longitudinal ventilation system of highway tunnel is nonlinear, time-delay and time-varying, so it is difficult to establish accurate mathematical model. In this paper, the frequency conversion technology is used to control the tunnel ventilation and energy saving system to reduce the excessive loss of electric energy and the loss of equipment, thus reducing the operating cost. In this paper, the traditional control of tunnel fan and frequency conversion control are compared at first, and the feasibility of frequency conversion speed regulation technology in tunnel ventilation control system is explained. Based on the study of frequency conversion control system for highway tunnel ventilation, the overall design and mathematical modeling of frequency conversion control system for tunnel ventilation are carried out in this paper. Taking the Baiyun tunnel as an example, the algorithm and simulation of the frequency conversion control system of the tunnel ventilation are studied according to the relevant parameters and technical indexes of the tunnel. Then, by detecting the traffic flow and speed of Baiyun Tunnel, the simulation is carried out with SUMO software. According to the simulation results of traffic flow and speed, the air demand of COHV I in the tunnel and the air demand of the special smell in the air are calculated. Finally, the air demand of the tunnel is obtained through the comparison of the three. Then, the output power and speed of the tunnel injector fan are regulated by frequency conversion technology. The superiority of the frequency conversion control system of tunnel ventilation is verified by comparing the energy consumption between the traditional control of tunnel fan and the control of variable frequency speed regulation. Finally, the BP neural network control algorithm is used to control the variable frequency ventilation system of the tunnel. The real time COA VI and the odor pollutant concentration are used as the input samples of BP neural network, and the output power of the fan frequency conversion is taken as the target output sample of the neural network. MATLAB neural network toolbox is used for training and simulation. The experimental results show that the average error between fan frequency conversion output power and the actual output power of neural network is less than 0.2, and the neural network model can fit the frequency conversion ventilation control model well. And has strong generalization ability.
【学位授予单位】：长安大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U453.5
【参考文献】
相关期刊论文 前6条
1 吴秉礼;隧道射流风机的设计与选型[J];风机技术;2001年04期
2 曾艳华,何川;特长公路隧道全射流通风技术的应用[J];公路;2002年07期
3 顾建明,陆明琦,杨国斌;射流风机的轴向推力[J];流体机械;2001年04期
4 金玉婷;余立建;;基于小波神经网络的短时交通流预测[J];交通科技与经济;2014年01期
5 于建伟;张欢;于燕玲;;污水隧道纵向通风CFD仿真模拟与试验研究[J];山东建筑大学学报;2007年04期
6 赵忠杰;朱斌;;变频调速在公路隧道通风控制中的应用[J];长安大学学报(自然科学版);2006年04期
本文编号：2144114
本文链接：https://www.wllwen.com/kejilunwen/jiaotonggongchenglunwen/2144114.html
