基于流量测点的热网变动阻力系数优化辨识

doi:10.19596/j.cnki.1001-246x.8280

摘要/Abstract

摘要：

随着供热计量系统的普及，系统可以根据负荷的变化进行调整，管网的阻力系数随即发生变化。对变动阻力系数进行优化辨识是了解供热管网实时运行状况的有效手段。提出一种基于流量监测数据的供热管网变动阻力系数优化辨识方法，并利用遗传算法进行求解。对洛阳市某小区供暖季管网实际运行数据进行验证，辨识结果的相对误差在5%以内。结果表明：该方法可以在只有流量观测数据时得到精度较高的供热管网变动阻力系数，为供热系统的运行调节提供指导。

关键词: 供热管网, 无压力测点, 变动阻力系数, 优化辨识, 遗传算法

Abstract:

With popularity of heating metering system, system can be adjusted according to the change of load, and the resistance coefficient of pipe network change. Optimal identification of variable resistance coefficient is an effective means to understand the real-time operation of heating network. We present an optimal identification method for variable resistance coefficient of heat supply network based on flow observation data. Genetic algorithm is used to solve the problem. Relative error of the identification results is less than 5% in practical verification example. It shows that the method can obtain variable resistance coefficient of heating network with high accuracy when only flow observation data is available. It can provide guidance for the operation and regulation of heating systems.

Key words: heating pipe network, no pressure measuring point, variable resistance coefficient, optimization identification, genetic algorithm

中图分类号:

TK323

屠丽娟, 周恩泽, 吴雪飞, 杨琪, 丁雪峰. 基于流量测点的热网变动阻力系数优化辨识[J]. 计算物理, 2021, 38(4): 498-504.

Lijuan TU, Enze ZHOU, Xuefei WU, Qi YANG, Xuefeng DING. Optimal Identification of Variable Resistance Coefficient of Heat Supply Network Based on Flow Measurement Points[J]. Chinese Journal of Computational Physics, 2021, 38(4): 498-504.

图/表 11

图1 流量测点下变动阻力系数优化辨识流程图

Fig.1 Flow chart of optimal identification of variable resistance coefficient with flow measurement points

图2 遗传算法辨识流程图

Fig.2 Flow chart of genetic algorithm identification

图3 管网试算结果

Fig.3 Trial calculation results of pipe network

表1 遗传算法各参数的确定

Table 1 Determination of parameters in genetic algorithms

	参数类型	参数确定值
种群Population	种群类型Population type	双精度向量Double vector
	种群规模Population size	100
	初始种群生成函数Creation function	可行随机种群Feasible population
适应度比例Fitness scaling	适应尺度变换函数Scaling function	排序变换Rank
选择Selection	选择函数Selection function	随机均匀Stochastic uniform
复制Reproduction	精英个数Elite count	2
复制Reproduction	交叉比例Crossover fraction	0.8
变异Mutation	变异函数Mutation function	自适应Adaptive feasible
交叉Crossover	交叉函数Crossover function	分散交叉Scattered
停止准则Stop criteria	进化代数Generations	200
	停滞代数Stalls generations	50
	函数容差Function tolerance	10^-6

图4 洛阳市某小区供热管网空间拓扑结构

Fig.4 Spatial topological structure of heating pipe networkin a district of Luoyang

表2 管网阻力系数设计值及搜索范围(单位：Pa ·m-6 ·h2)

Table 2 Set values and search ranges of pipe networkresistance coefficients(in unit Pa ·m-6 ·h2)

热力入口	设计阻力系数	搜索上限	搜索下限
1	49.36	39.98	62.19
2	49.36	39.98	62.19
3	49.36	39.98	62.19
4	24.68	21.22	29.86
5	43.55	35.28	54.87
6	43.55	35.28	54.87
7	43.55	35.28	54.87
8	43.55	35.28	54.87
9	24.68	21.22	29.86
供回水干管	0.42	0.38	0.48

图5 热力入口1~3实测流量数据与辨识流量数据

Fig.5 Measured flow data and identified flow dataof thermal inlet 1~3

图6 供回水干管实测流量数据与辨识流量数据

Fig.6 Measured flow data and identified flow dataof main supply and return water pipes

图7 热力入口4~6实测流量数据与辨识流量数据

Fig.7 Measured flow data and identified flow dataof thermal inlet 4~6

图8 热力入口7~9实测流量数据与辨识流量数据

Fig.8 Measured flow data and identified flowdata of thermal inlet 7~9

图9 计算流量与观测流量的误差

Fig.9 Error between calculated and observed discharge

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