Please wait a minute...
浙江大学学报(工学版)  2025, Vol. 59 Issue (10): 2115-2124    DOI: 10.3785/j.issn.1008-973X.2025.10.012
交通工程、水利工程、土木工程     
基于人工兔算法的复杂输水系统泵阀联合优化调控
郝梦园1,2(),张雷克1,2,刘小莲1,2,*(),王雪妮1,2,田雨3
1. 太原理工大学 水利科学与工程学院,山西 太原 030024
2. 流域水资源协同利用山西省重点实验室,山西 太原 030024
3. 中国水利水电科学研究院 流域水循环与水安全调控国家重点实验室,北京 100038
Pumps and valves joint optimization control in complex water conveyance system based on artificial rabbits optimization algorithm
Mengyuan HAO1,2(),Leike ZHANG1,2,Xiaolian LIU1,2,*(),Xueni WANG1,2,Yu TIAN3
1. College of Hydro Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, China
2. Shanxi Key Laboratory of Cooperative Utilization for Basin Water Resources, Taiyuan 030024, China
3. State Key Laboratory of Water Cycle and Water Security, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
 全文: PDF(1744 KB)   HTML
摘要:

在泵站加压提水结合重力输水系统中,单个水利设施调控难以兼顾全线安全,为此以连续停机工况为例,综合考虑机组停机间隔、泵后阀关闭规律及末端控制阀调节规律对管道系统压力极值及高位水池水位波动的影响,建立耦合水力计算模型的泵阀联合优化调控模型,提出基于非支配排序及拥挤度距离的非支配排序人工兔优化算法(NSARO). 超体积指标(HV)验证结果表明,NSARO在泵阀联合优化调控中具有良好的收敛性. 利用灰色关联度分析法(GRA)对所获Pareto解集进行方案优选. 以实际工程为例,利用NSARO-GRA优化求解决策方法得出优选方案,相较于现状方案,管道系统的最大正压力水头、最大负压力水头以及高位水池的水位波动值分别降低了13.57、0.29和5.79 m.

关键词: 泵阀联合调控多目标优化有压输水系统人工兔优化算法灰色关联度分析法(GRA)    
Abstract:

For a water conveyance project that combined pressurization and gravity flow, it is difficult to balance the global safety problem only by controlling a single water facility. The issue of joint control of pumps and valves under continuous shutdown conditions was discussed. The shutdown interval between parallel pumps, the closing law of the pump discharge valves and the closing process of the terminal control valves were considered, and their impacts on the extreme water hammer pressure in the pipeline system and the water level in the high-elevated pool were analyzed. The joint control of pumps and valves optimization model was established, which was coupled with the hydraulic calculation method. A non-dominated sorting artificial rabbits optimization algorithm (NSARO) was proposed based on non-dominated sorting and crowding distance. The excellent convergence of the NSARO in the joint optimization and control of pumps and valves was verified by the hypervolume (HV) index. Grey relational analysis (GRA) was used to pinpoint the optimal scheme from the Pareto solution set. In the case of an actual project, the NSARO-GRA optimization solution decision-making method was used to obtain the optimal scheme. Compared with the current scheme, the maximum positive pressure head, maximum negative pressure head in the pipeline system, and water level fluctuation value in the high-elevated pool of the optimal scheme have been reduced by 13.57, 0.29 and 5.79 m, respectively.

Key words: joint operation of pumps and valves    multi-objective optimization    pressurized water transmission system    artificial rabbits optimization algorithm    grey relational analysis (GRA)
收稿日期: 2024-09-14 出版日期: 2025-10-27
CLC:  TV 675  
基金资助: 国家自然科学基金资助项目(52379091);山西省水利技术研究推广补助项目(2024GM21);山西省基础研究计划项目(202203021222112).
通讯作者: 刘小莲     E-mail: 1213264132@qq.com;liuxiaolian@tyut.edu.cn
作者简介: 郝梦园(1999—),女,硕士生,从事泵站系统运行优化工作. orcid.org/0009-0006-2964-9550. E-mail:1213264132@qq.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章  
郝梦园
张雷克
刘小莲
王雪妮
田雨

引用本文:

郝梦园,张雷克,刘小莲,王雪妮,田雨. 基于人工兔算法的复杂输水系统泵阀联合优化调控[J]. 浙江大学学报(工学版), 2025, 59(10): 2115-2124.

Mengyuan HAO,Leike ZHANG,Xiaolian LIU,Xueni WANG,Yu TIAN. Pumps and valves joint optimization control in complex water conveyance system based on artificial rabbits optimization algorithm. Journal of ZheJiang University (Engineering Science), 2025, 59(10): 2115-2124.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2025.10.012        https://www.zjujournals.com/eng/CN/Y2025/V59/I10/2115

图 1  输水工程示意图
图 2  泵连续停机工况示意图
图 3  非支配排序及拥挤度距离示意图
图 4  非支配排序人工兔优化算法的泵阀联合优化调控求解流程
图 5  超体积指标的迭代过程曲线
图 6  泵阀联合优化调控模型的优化结果
图 7  目标函数的Spearman相关系数矩阵图
图 8  Pareto前沿解的灰色关联度
方案决策变量目标函数
t1/sη/%t2/s?t1/s?t2/sβ1/%β2/%Hmax/mHmin/mZmax?Zmin/m
现状方案303360600600106.13?4.677.98
优选方案123687414572351392.56?4.382.19
表 1  现状方案与优选方案的联合调控策略
图 9  不同方案泵后阀及末端控制阀的调控规律对比
图 10  管道压力包络线对比
图 11  高位水池水位和体积流量变化曲线
1 石林, 张健, 俞晓东, 等 长距离多支线输水系统稳压塔降高方案研究[J]. 华中科技大学学报: 自然科学版, 2023, 51 (8): 67- 73
SHI Lin, ZHANG Jian, YU Xiaodong, et al Study on reducing height of surge tank in long-distance and multi-branch water conveyance system[J]. Journal of Huazhong University of Science and Technology: Natural Science Edition, 2023, 51 (8): 67- 73
2 汪顺生, 郭新源 基于Bentley Hammer的气囊式空气罐的水锤防护研究[J]. 振动与冲击, 2022, 41 (6): 177- 182
WANG Shunsheng, GUO Xinyuan Water hammer protection effect of gasbag-type pneumatic tank based on the analysis using the Bentley Hammer software[J]. Journal of Vibration and Shock, 2022, 41 (6): 177- 182
3 ZHANG T, ZHOU J, YANG X, et al Multi-objective optimization and decision-making of the combined control law of guide vane and pressure regulating valve for hydroelectric unit[J]. Energy Science and Engineering, 2022, 10 (2): 472- 487
doi: 10.1002/ese3.1038
4 WAN W, LI F Sensitivity analysis of operational time differences for a pump–valve system on a water hammer response[J]. Journal of Pressure Vessel Technology, 2016, 138 (1): 011303
doi: 10.1115/1.4031202
5 丁梓恒, 俞晓东, 马世波, 等 泵站加压与重力自流联合供水工程的停泵水锤防护[J]. 排灌机械工程学报, 2022, 40 (4): 338- 344
DING Ziheng, YU Xiaodong, MA Shibo, et al Pump failure water hammer protection of pumping station pressurization and gravity combined water supply project[J]. Journal of Drainage and Irrigation Machinery Engineering, 2022, 40 (4): 338- 344
6 LIU X, LIU Z, HOU X, et al A parallel multi-objective optimization based on adaptive surrogate model for combined operation of multiple hydraulic facilities in water diversion project[J]. Journal of Hydroinformatics, 2024, 26 (6): 1351- 1369
doi: 10.2166/hydro.2024.285
7 FENG T, ZHANG D, SONG P, et al Numerical research on water hammer phenomenon of parallel pump-valve system by coupling FLUENT with RELAP5[J]. Annals of Nuclear Energy, 2017, 109: 318- 326
doi: 10.1016/j.anucene.2017.05.049
8 YAN P, ZHANG Z, LEI X, et al A multi-objective optimal control model of cascade pumping stations considering both cost and safety[J]. Journal of Cleaner Production, 2022, 345: 131171
doi: 10.1016/j.jclepro.2022.131171
9 叶倩琳, 王万良, 王铮 多目标粒子群优化算法及其应用研究综述[J]. 浙江大学学报: 工学版, 2024, 58 (6): 1107- 1120
YE Qianlin, WANG Wanliang, WANG Zheng Survey of multi-objective particle swarm optimization algorithms and their applications[J]. Journal of Zhejiang University: Engineering Science, 2024, 58 (6): 1107- 1120
10 冯仲恺, 廖胜利, 牛文静, 等 改进量子粒子群算法在水电站群优化调度中的应用[J]. 水科学进展, 2015, 26 (3): 413- 422
FENG Zhongkai, LIAO Shengli, NIU Wenjing, et al Improved quantum-behaved particle swarm optimization and its application in optimal operation of hydropower stations[J]. Advances in Water Science, 2015, 26 (3): 413- 422
11 WANG L, CAO Q, ZHANG Z, et al Artificial rabbits optimization: a new bio-inspired meta-heuristic algorithm for solving engineering optimization problems[J]. Engineering Applications of Artificial Intelligence, 2022, 114: 105082
doi: 10.1016/j.engappai.2022.105082
12 OZKAYA B, DUMAN S, KAHRAMAN H T, et al Optimal solution of the combined heat and power economic dispatch problem by adaptive fitness-distance balance based artificial rabbits optimization algorithm[J]. Expert Systems with Applications, 2024, 238: 122272
doi: 10.1016/j.eswa.2023.122272
13 KHARRICH M, HASSAN M H, KAMEL S, et al Designing an optimal hybrid microgrid system using a leader artificial rabbits optimization algorithm for domestic load in Guelmim city, Morocco[J]. Renewable Energy, 2024, 223: 120011
doi: 10.1016/j.renene.2024.120011
14 GUO Y, TANG Q, DARKWA J, et al A novel prediction model for integrated district energy system based on secondary decomposition and artificial rabbits optimization[J]. Energy and Buildings, 2024, 310: 114106
doi: 10.1016/j.enbuild.2024.114106
15 TIAN Y, ZHAO Y, WANG Z, et al Non-dominated sorting artificial rabbit multi-objective sizing optimization for a conceptual powertrain of a 6×4 battery electric tractor truck[J]. Energy, 2024, 304: 132009
doi: 10.1016/j.energy.2024.132009
16 ZHANG X, TIAN Y, JIN Y Approximate non-dominated sorting for evolutionary many-objective optimization[J]. Information Sciences, 2016, 369: 14- 33
doi: 10.1016/j.ins.2016.06.007
17 TIAN Y, WANG H, ZHANG X, et al Effectiveness and efficiency of non-dominated sorting for evolutionary multi- and many-objective optimization[J]. Complex and Intelligent Systems, 2017, 3 (4): 247- 263
doi: 10.1007/s40747-017-0057-5
18 WANG R, ZHOU Z, ISHIBUCHI H, et al Localized weighted sum method for many-objective optimization[J]. IEEE Transactions on Evolutionary Computation, 2018, 22 (1): 3- 18
doi: 10.1109/TEVC.2016.2611642
19 DOS SANTOS P H, NEVES S M, SANT’ANNA D O, et al The analytic hierarchy process supporting decision making for sustainable development: an overview of applications[J]. Journal of Cleaner Production, 2019, 212: 119- 138
doi: 10.1016/j.jclepro.2018.11.270
20 KOKSALMIS E, KABAK Ö Deriving decision makers’ weights in group decision making: an overview of objective methods[J]. Information Fusion, 2019, 49: 146- 160
doi: 10.1016/j.inffus.2018.11.009
21 HU Y, WU L, SHI C, et al Research on optimal decision-making of cloud manufacturing service provider based on grey correlation analysis and TOPSIS[J]. International Journal of Production Research, 2020, 58 (3): 748- 757
doi: 10.1080/00207543.2019.1600760
22 LI W, REN X, DING S, et al A multi-criterion decision making for sustainability assessment of hydrogen production technologies based on objective grey relational analysis[J]. International Journal of Hydrogen Energy, 2020, 45 (59): 34385- 34395
doi: 10.1016/j.ijhydene.2019.11.039
23 刘忠, 陈星宇, 邹淑云, 等 计及碳排放的风-光-抽水蓄能系统容量优化配置方法[J]. 电力系统自动化, 2021, 45 (22): 9- 18
LIU Zhong, CHEN Xingyu, ZOU Shuyun, et al Optimal capacity configuration method for wind-photovoltaic-pumped-storage system considering carbon emission[J]. Automation of Electric Power Systems, 2021, 45 (22): 9- 18
24 YAZDANI M, KAHRAMAN C, ZARATE P, et al A fuzzy multi attribute decision framework with integration of QFD and grey relational analysis[J]. Expert Systems with Applications, 2019, 115: 474- 485
doi: 10.1016/j.eswa.2018.08.017
25 DEB K, PRATAP A, AGARWAL S, et al A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6 (2): 182- 197
doi: 10.1109/4235.996017
26 张健, 俞晓东, 朱永忠 长距离供水工程的关阀水锤与线路充填[J]. 水力发电学报, 2010, 29 (2): 183- 189
ZHANG Jian, YU Xiaodong, ZHU Yongzhong Study on water-hammer and line packing in long-distance water-supply project[J]. Journal of Hydroelectric Engineering, 2010, 29 (2): 183- 189
27 王眺, 詹航, 万五一 长距离输水系统停泵水锤防护多目标优化研究[J]. 水力发电学报, 2022, 41 (12): 90- 99
WANG Tiao, ZHAN Hang, WAN Wuyi Multi-objective optimization method for water hammer protection against pump failure in long-distance water transfer systems[J]. Journal of Hydroelectric Engineering, 2022, 41 (12): 90- 99
28 梁兴, 张剑焜, 李志红, 等 超驼峰工况下轴流泵站事故停泵防护方案寻优[J]. 排灌机械工程学报, 2020, 38 (10): 1010- 1015
LIANG Xing, ZHANG Jiankun, LI Zhihong, et al Optimizing protection scheme for accident shutdown of axial flow pumping station under super hump conditions[J]. Journal of Drainage and Irrigation Machinery Engineering, 2020, 38 (10): 1010- 1015
29 杜畅, 刘喜元, 张健, 等 泵后管线先降后升的长距离输水系统水锤防护[J]. 排灌机械工程学报, 2022, 40 (12): 1248- 1253
DU Chang, LIU Xiyuan, ZHANG Jian, et al Long-distance water hammer protection of pipeline after pump being first lowered and then raised[J]. Journal of Drainage and Irrigation Machinery Engineering, 2022, 40 (12): 1248- 1253
30 KUO Y, YANG T, HUANG G W The use of grey relational analysis in solving multiple attribute decision-making problems[J]. Computers and Industrial Engineering, 2008, 55 (1): 80- 93
doi: 10.1016/j.cie.2007.12.002
31 GÜLMEZ B Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm[J]. Expert Systems with Applications, 2023, 227: 120346
doi: 10.1016/j.eswa.2023.120346
32 AWADALLAH M A, BRAIK M S, AL-BETAR M A, et al An enhanced binary artificial rabbits optimization for feature selection in medical diagnosis[J]. Neural Computing and Applications, 2023, 35 (27): 20013- 20068
doi: 10.1007/s00521-023-08812-6
[1] 罗亚波,喻少龙,张峰,李存荣. 改进候鸟算法求解可重入混流车间批量流调度[J]. 浙江大学学报(工学版), 2025, 59(8): 1598-1607.
[2] 王昱,马春荣,赵明月. 基于混合策略多目标粒子群的异构无人机协同多任务分配[J]. 浙江大学学报(工学版), 2025, 59(4): 821-831.
[3] 李勇,王跃,柳富强,孙柏青,李恺如. 护工-机器人协作养老情境下的多任务分配框架[J]. 浙江大学学报(工学版), 2025, 59(2): 375-383.
[4] 张盈斐,胡小兵,周航,冯序增. 基于改进的NSGA-II算法的三维扇区自动划设[J]. 浙江大学学报(工学版), 2025, 59(2): 413-422.
[5] 余廷芳,张艮离,周嘉鹏,汤一村. 超临界CO2布雷顿循环耦合有机闪蒸循环的性能分析及优化[J]. 浙江大学学报(工学版), 2025, 59(1): 130-140.
[6] 李若琼,翁源,李欣. 分数阶磁耦合谐振双向无线电能传输系统参数优化[J]. 浙江大学学报(工学版), 2025, 59(1): 141-151.
[7] 叶倩琳,王万良,王铮. 多目标粒子群优化算法及其应用研究综述[J]. 浙江大学学报(工学版), 2024, 58(6): 1107-1120.
[8] 詹燕,陈洁雅,江伟光,鲁建厦,汤洪涛,宋新禹,许丽丽,刘赛淼. 基于改进NSGA-Ⅱ的多目标车间物料配送方法[J]. 浙江大学学报(工学版), 2024, 58(12): 2510-2519.
[9] 曹晓彦,于敏,周瑾,王运志. 可调旋转式流体阻尼器参数多目标优化设计[J]. 浙江大学学报(工学版), 2023, 57(7): 1439-1449.
[10] 余廷芳,宋凌. 超临界CO2布雷顿循环余热回收系统性能分析与优化[J]. 浙江大学学报(工学版), 2023, 57(2): 404-414.
[11] 王万良,陈忠馗,吴菲,王铮,俞梦娇. 基于个体预测的动态多目标优化算法[J]. 浙江大学学报(工学版), 2023, 57(11): 2133-2146.
[12] 王万良,金雅文,陈嘉诚,李国庆,胡明志,董建杭. 多角色多策略多目标粒子群优化算法[J]. 浙江大学学报(工学版), 2022, 56(3): 531-541.
[13] 徐钧恒,杨晓钧,李兵. 基于交叉簧片式铰链的变弯度机翼机构设计[J]. 浙江大学学报(工学版), 2022, 56(3): 444-451, 509.
[14] 邓齐林,鲁娟,陈勇辉,冯健,廖小平,马俊燕. 基于深度强化学习的数控铣削加工参数优化方法[J]. 浙江大学学报(工学版), 2022, 56(11): 2145-2155.
[15] 陈俊杰,李洪均,曹张华. 性能感知的核心网控制面资源分配算法[J]. 浙江大学学报(工学版), 2021, 55(9): 1782-1787.