基于卡尔曼滤波的推定末端压力控制技术研究
摘要：恒定泵口压力控制模式是我国常用的二次供水运行模式，这种控制模式不但造成低峰时刻运行压力过高问题，影响用户用水体验，而且会引起能源浪费与管道运行风险，急需研究开发基于用户水量需求的控制模式。根据苏州某生活泵房实测数据，构建卡尔曼滤波算法，进行为期6个月实时预测及试验。结果表明，水量预测算法对于天级水量预测精度较高，对于控制需求较强的分钟级预测精度基本满足自动运行控制需求；实测流量精度不足是导致该算法中分钟级预测精度不足的一大诱因，通过计量器具选型、合理设置算法调节参数，预测精度可得到改善；模拟分析可知，变流控制对于稳定用户压力，降低泵房降耗具有一定改善效果。
关键词：卡尔曼滤波水量预测推定末端压力
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参考文献[1] ANDREA M,ARIELE Z,MAURIZIO R.Tuning ANN Hyperparameters for Forecasting Drinking Water Demand[J].Applied Sciences.2021,11(9）.
[2] EL-KEIB A,MA X,MA H.Advancement of statistical based modeling techniques for short-term load forecasting[J].Electric Power Systems Research,1995,35(1):51-58.
[3] AL-KANDARI A M,SOLIMAN S A,EL-HAWARY M E.Fuzzy short-term electric load forecasting[J].Electrical Power and Energy Systems,2004,26(2):111-122.
[4] KALOGIROU S A.Applications of artificial neural-networks for energy systems[J].Applied Energy,2000,67(1):17-35.
[5] PAPADAKIS S E,THEOCHARIS J B,BAKIRTZIS A G.Aload curve based fuzzy modeling technique for short-term load forecasting[J].Fuzzy Sets and Systems,2003,135(2):279-303.
[6] FRANOIS A,HILAIRET M,GUERRERO J M,et al.Industrial Applications of the Kalman Filter:A Review[J].IEEE Transactions on Industrial Electronics,2013,60(12):5458.
[7] 李伟.鲁棒自适应滤波算法及在飞行器技术中的应用研究[D].上海：上海交通大学，2014.
[8] 彭丁聪.卡尔曼滤波的基本原理及应用[J].软件导刊，2009,8(11):32-34.
[9] PAUL A,KAMWA I,JOOS G.Centralized dynamic state estimation using a federation of extended Kalman filters with intermittent PMU data from generator terminals[J].IEEETrans on Power Systems,2018:1-1.
[10] HU J,WANG Z,GAO H,et al.Extended Kalman filtering with stochastic nonlinearities and multiple missing measurement[J].Automatica,2012,48(9):2007-2015.
[11] 曲正伟，董一兵，王云静，等.用于电力系统动态状态估计的改进鲁棒无迹卡尔曼滤波算法[J].电力系统自动化，2018,42(10):87-92.
[12] ARASARATNAM I,HAYKIN S.Cubature Kalman filters[J].IEEE Transactions on Automatic Control,2009,54 (6):1254-1269.
[13] 顾建强，刘海星，张朝等.基于自适应卡尔曼滤波的用水量预测和爆管诊断[J].中国给水排水，2019,35(7):62-67.
[2] EL-KEIB A,MA X,MA H.Advancement of statistical based modeling techniques for short-term load forecasting[J].Electric Power Systems Research,1995,35(1):51-58.
[3] AL-KANDARI A M,SOLIMAN S A,EL-HAWARY M E.Fuzzy short-term electric load forecasting[J].Electrical Power and Energy Systems,2004,26(2):111-122.
[4] KALOGIROU S A.Applications of artificial neural-networks for energy systems[J].Applied Energy,2000,67(1):17-35.
[5] PAPADAKIS S E,THEOCHARIS J B,BAKIRTZIS A G.Aload curve based fuzzy modeling technique for short-term load forecasting[J].Fuzzy Sets and Systems,2003,135(2):279-303.
[6] FRANOIS A,HILAIRET M,GUERRERO J M,et al.Industrial Applications of the Kalman Filter:A Review[J].IEEE Transactions on Industrial Electronics,2013,60(12):5458.
[7] 李伟.鲁棒自适应滤波算法及在飞行器技术中的应用研究[D].上海：上海交通大学，2014.
[8] 彭丁聪.卡尔曼滤波的基本原理及应用[J].软件导刊，2009,8(11):32-34.
[9] PAUL A,KAMWA I,JOOS G.Centralized dynamic state estimation using a federation of extended Kalman filters with intermittent PMU data from generator terminals[J].IEEETrans on Power Systems,2018:1-1.
[10] HU J,WANG Z,GAO H,et al.Extended Kalman filtering with stochastic nonlinearities and multiple missing measurement[J].Automatica,2012,48(9):2007-2015.
[11] 曲正伟，董一兵，王云静，等.用于电力系统动态状态估计的改进鲁棒无迹卡尔曼滤波算法[J].电力系统自动化，2018,42(10):87-92.
[12] ARASARATNAM I,HAYKIN S.Cubature Kalman filters[J].IEEE Transactions on Automatic Control,2009,54 (6):1254-1269.
[13] 顾建强，刘海星，张朝等.基于自适应卡尔曼滤波的用水量预测和爆管诊断[J].中国给水排水，2019,35(7):62-67.
Research on Kalman-filter-based pressure extremity control technology
Abstract: Constant pressure control mode is commonly used in secondary-water-supply operation in china,which not only causes redundant pressure in low peak time,but also generates energy waste and pipeline operation risks.Developing a new control model based on the user's water demand is needed.Based on the measured data of a living pump house of Suzhou city,a Kalman filter algorithm was constructed and used to control pump for 6-month.The results showed that flux accuracy for the day-level prediction was better than the minute-level.The lower precision of flux prediction for the minute-level by Kalman filter algorithm was mainly caused by insufficient accuracy of the measured flow rate.The prediction accuracy could be improved by setting the error parameters of algorithm reasonably.It is showed that users′pressure become more stable and energy consumption of pump was decreasing,when the pump controlled by flux predicted by Kalman filter algorithm.
Keywords: Kalman filter; Water consumption prediction; Pressure extremity control;
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