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清华大学学报(自然科学版)  2024, Vol. 64 Issue (2): 271-281    DOI: 10.16511/j.cnki.qhdxxb.2023.22.037
  水利水电工程 本期目录 | 过刊浏览 | 高级检索 |
基于Kalman滤波的供水管网水力模型用水量动态估计
吴珊1, 吴雨晨1, 侯本伟1, 韩宏泉2
1. 北京工业大学 城市建设学部, 北京 100124;
2. 北京市市政工程设计研究总院有限公司, 北京 100082
Water demand dynamic estimation in water distribution network hydraulic models based on Kalman filter
WU Shan1, WU Yuchen1, HOU Benwei1, HAN Hongquan2
1. Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China;
2. Beijing General Municipal Engineering Design and Research Institute Co., Ltd., Beijing 100082, China
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摘要 节点用水量的动态估计是供水管网水力模型动态更新的主要工作,以扩展Kalman滤波(EKF)为代表的数据同化方法已被应用于管网水力模型参数动态校核和估计中,但现有研究未考虑用户节点24 h用水模式对节点用水量估计的影响。该文研究了节点用水模式先验信息对EKF方法节点用水量动态估计结果的影响。根据用户24 h的用水模式和t时刻用水量估计值预测t+1时刻节点用水量,并采用t+1时刻管网测量数据校正t+1时刻节点用水量预测值。案例管网的应用结果表明:与现有研究中应用的EKF方法相比,考虑用水模式的扩展Kalman滤波(IEKF)方法对应的用水量估计平均绝对百分比误差降低了15.93%,节点用水量时变曲线的Nash-Sutcliffe效率(NSE)系数值提高了0.40,且2种方法的计算时间相近;与推断测量Kalman滤波(IMKF)相比,IEKF方法对应的用水量估计平均绝对百分比误差降低了12.20%,节点用水量时变曲线的NSE系数值提高了0.35,计算时间缩短了99.8%。在节点用水量估计问题中,考虑用水模式可以显著提高EKF方法的计算精度。
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吴珊
吴雨晨
侯本伟
韩宏泉
关键词 供水管网模型校核用水量估计用水模式扩展Kalman滤波(EKF)    
Abstract:[Objective] Nodal water demand dynamic estimation is the main task in the dynamic update of a water distribution network hydraulic model. The data assimilation method based on the extended Kalman filter (EKF) has been widely adopted in dynamic verification and estimation of hydraulic model parameters for pipeline networks. However, the nodal water demand can be divided into different patterns according to user types. Ignoring the influence of demand patterns can cause unreasonable results. Accordingly, this study investigates the effect of demand patterns on the nodal water demand dynamic estimation by the EKF. Additionally, this study proposes an improved extended Kalman filter (IEKF) method and analyzes the main parameters affecting estimation accuracy by comparing different methods. [Methods] The EKF obtains the dynamic water demand of the model through the estimation of multiple time steps, each of which comprises the prediction and correction steps. In the initial time step, the demands are calibrated using an iterative calibration model to avoid state parameter error transmission. In the prediction step, a 24-h demand pattern is adopted as a priori information to predict the nodal water demand at time step t+1 with the demand estimation result at time step t. In the correction step, the nodal demand predicted at time step t+1 is corrected by using the measurement data at time step t+1. The abovementioned steps lead to the IEKF. Aiming at the key parameters in the calculation process, including system noise, measurement noise, and sampling interval, the adaptability of several data assimilation algorithms, including EKF and inferred-measurement Kalman filter (IMKF), is analyzed under different parameter settings. The root mean square error (RMSE) and mean absolute percentage error (MAPE) are used to evaluate the accuracy of the results. Additionally, the Nash-Sutcliffe efficiency (NSE) coefficient is introduced to evaluate the similarity between the demand estimation curves and their corresponding demand patterns. The robustness of the IEKF to errors in the demand estimation curves is also analyzed by establishing an error condition. [Results] (1) Compared with the EKF and IMKF, the MAPE obtained by the IEKF is 15.93% and 12.20% lower, respectively. Additionally, the NSE coefficient of the demand estimation curve is improved by 0.40 and 0.35, respectively. The computation time of the IEKF is similar to that of the EKF and is 99.8% lower compared with the IMKF. (2) The IEKF can adapt to larger sampling intervals, offering more advantages over the EKF and IMKF for larger sampling intervals. (3) Within the 24-h estimation period, the IEKF suffers smaller errors at all sampling intervals, and the demand estimation curves match the real demand pattern curves. Compared with the EKF and IMKF, IEKF can more accurately capture the demand change and is robust to the demand pattern curve errors in a priori information. [Conclusions] The proposed IEKF uses demand patterns of different user types as a priori information, and NSE coefficient is introduced to assess the similarity between the demand estimation curves and real demand patterns, which improves the accuracy of dynamic estimation of nodal water demand using data assimilation algorithms. In the estimation of nodal water demand, considering the user water consumption patterns can significantly improve the computational accuracy of the EKF method.
Key wordswater distribution network    model calibration    water demand estimation    water demand pattern    extended Kalman filter (EKF)
收稿日期: 2023-04-10      出版日期: 2023-12-28
ZTFLH:  TU991.33  
基金资助:国家自然科学基金面上项目(51978023)
通讯作者: 侯本伟,副教授,E-mail:benweihou@bjut.edu.cn     E-mail: benweihou@bjut.edu.cn
作者简介: 吴珊(1963-),女,副教授。
引用本文:   
吴珊, 吴雨晨, 侯本伟, 韩宏泉. 基于Kalman滤波的供水管网水力模型用水量动态估计[J]. 清华大学学报(自然科学版), 2024, 64(2): 271-281.
WU Shan, WU Yuchen, HOU Benwei, HAN Hongquan. Water demand dynamic estimation in water distribution network hydraulic models based on Kalman filter. Journal of Tsinghua University(Science and Technology), 2024, 64(2): 271-281.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2023.22.037  或          http://jst.tsinghuajournals.com/CN/Y2024/V64/I2/271
  
  
  
  
  
  
  
  
  
  
  
[1] KANG D, LANSEY K. Demand and roughness estimation in water distribution systems [J]. Journal of Water Resources Planning and Management, 2011, 137(1):20-30.
[2] DINI M, TABESH M. A new method for simultaneous calibration of demand pattern and Hazen-Williams coefficients in water distribution systems [J]. Water Resources Management, 2014, 28(7):2021-2034.
[3] DU K, LONG T Y, WANG J H, et al. Inversion model of water distribution systems for nodal demand calibration [J]. Journal of Water Resources Planning and Management, 2015, 141(9):04015002.
[4] 刘书明, 吴以朋, 车晗. 利用自识别的供水管网监测数据质量控制[J]. 清华大学学报(自然科学版), 2017, 57(9):999-1003. LIU S M, WU Y P, CHE H. Monitoring data quality control for a water distribution system using data self-recognition [J]. Journal of Tsinghua University (Science and Technology), 2017, 57(9):999-1003. (in Chinese)
[5] WALSKI T M. Technique for calibrating network models [J]. Journal of Water Resources Planning and Management, 1983, 109(4):360-372.
[6] ORMSBEE L E, WOOD D J. Hydraulic design algorithms for pipe networks [J]. Journal of Hydraulic Engineering, 1986, 112(12):1195-1206.
[7] DO N C, SIMPSON A R, DEUERLEIN J W, et al. Calibration of water demand multipliers in water distribution systems using genetic algorithms [J]. Journal of Water Resources Planning and Management, 2016, 142(11):04016044.
[8] LETTING L K, HAMAM Y, ABU-MAHFOUZ A M. Estimation of water demand in water distribution systems using particle swarm optimization [J]. Water, 2017, 9(8):593.
[9] 任刚红, 杜坤, 和丽蓉, 等. 基于先验信息的供水管网阻力系数识别[J]. 土木建筑与环境工程, 2018, 40(2):46-52. REN G H, DU K, HE L R, et al. Pipe resistance coefficient identification of water distribution system based on prior information [J]. Journal of Civil, Architectural & Environmental Engineering, 2018, 40(2):46-52. (in Chinese)
[10] 范江, 杜坤, 周明, 等. 基于加权最小二乘法的供水管网节点流量校核[J]. 土木建筑与环境工程, 2016, 38(3):73-79. FAN J, DU K, ZHOU M, et al. Nodal demand calibration of water distribution system using the weighted least squares method [J]. Journal of Civil, Architectural & Environmental Engineering, 2016, 38(3):73-79. (in Chinese)
[11] CHENG W P, YU T C, XU G. Real-time model of a large-scale water distribution system [J]. Procedia Engineering, 2014, 89:457-466.
[12] ZHOU X, GUO S Y, XIN K L, et al. Maintaining the long-term accuracy of water distribution models with data assimilation methods:A comparative study [J]. Water Research, 2022, 226:119268.
[13] DAVIDSON J W, BOUCHART F J C. Adjusting nodal demands in SCADA constrained real-time water distribution network models [J]. Journal of Hydraulic Engineering, 2006, 132(1):102-110.
[14] HUTTON C J, KAPELAN Z, VAMVAKERIDOU-LYROUDIA L, et al. Dealing with uncertainty in water distribution system models:A framework for real-time modeling and data assimilation [J]. Journal of Water Resources Planning and Management, 2014, 140(2):169-183.
[15] 赵思浩, 陆明泉, 冯振明. 一种应用于GPS/低成本INS组合导航的自适应滤波算法[J]. 清华大学学报(自然科学版), 2011, 51(8):1027-1030. ZHAO S H, LU M Q, FENG Z M. Adaptive filtering in GPS and low-cost INS integrated navigation systems [J]. Journal of Tsinghua University (Science and Technology), 2011, 51(8):1027-1030. (in Chinese)
[16] 金力, 吕鹏, 崔晓伟, 等. 新一代GNSS信号的自适应Kalman跟踪算法[J]. 清华大学学报(自然科学版), 2012, 52(9):1249-1254, 1259. JIN L, LÜ P, CUI X W, et al. Adaptive Kalman tracking algorithm for new generation GNSS signals [J]. Journal of Tsinghua University (Science and Technology), 2012, 52(9):1249-1254, 1259. (in Chinese)
[17] JUNG D, LANSEY K. Water distribution system burst detection using a nonlinear Kalman filter [J]. Journal of Water Resources Planning and Management, 2015, 141(5):04014070.
[18] YE G L, FENNER R A. Kalman filtering of hydraulic measurements for burst detection in water distribution systems [J]. Journal of Pipeline Systems Engineering and Practice, 2011, 2(1):14-22.
[19] LI Q, WU Z Y, RAHMAN A. Evolutionary deep learning with extended Kalman filter for effective prediction modeling and efficient data assimilation [J]. Journal of Computing in Civil Engineering, 2019, 33(3):04019014.
[20] OKEYA I, KAPELAN Z, HUTTON C, et al. Online modelling of water distribution system using data assimilation [J]. Procedia Engineering, 2014, 70:1261-1270.
[21] TODINI E. Using a Kalman filter approach for looped water distribution networks calibration [C]//International Conference on Computing and Control for the Water Industry; Water Industry Systems:Modelling and Optimization Applications. Baldock:Research Studies Press, 1999, 1:327-336.
[22] KANG D, LANSEY K. Real-time demand estimation and confidence limit analysis for water distribution systems [J]. Journal of Hydraulic Engineering, 2009, 135(10):825-837.
[23] ZHANG Q Z, ZHENG F F, DUAN H F, et al. Efficient numerical approach for simultaneous calibration of pipe roughness coefficients and nodal demands for water distribution systems [J]. Journal of Water Resources Planning and Management, 2018, 144(10):04018063.
[24] ZHOU X, XU W R, XIN K L, et al. Self-adaptive calibration of real-time demand and roughness of water distribution systems [J]. Water Resources Research, 2018, 54(8):5536-5550.
[25] 马锡涛. 城市供水管网功能抗震易损性与韧性概率特征分析[D]. 北京:北京工业大学, 2022. MA X T. Seismic fragility and resilience probabilistic characteristic analysis of urban water supply networks [D]. Beijing:Beijing University of Technology, 2022. (in Chinese)
[26] LIU N D, DU K, TU J P, et al. Analytical solution of Jacobian matrices of WDS models [J]. Procedia Engineering, 2017, 186:388-396.
[1] 刘书明, 吴以朋, 车晗. 利用自识别的供水管网监测数据质量控制[J]. 清华大学学报(自然科学版), 2017, 57(9): 999-1003.
[2] 刘书明, 吴以朋, 王晓婷, 刘友飞, 李佳杰. 应用聚类算法识别供水管网爆管事故[J]. 清华大学学报(自然科学版), 2017, 57(10): 1096-1101.
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