CN1312629C - Modeling method of uncertain hydraulics model for urban seweage and drainage system - Google Patents

Modeling method of uncertain hydraulics model for urban seweage and drainage system Download PDF

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CN1312629C
CN1312629C CNB2005100615242A CN200510061524A CN1312629C CN 1312629 C CN1312629 C CN 1312629C CN B2005100615242 A CNB2005100615242 A CN B2005100615242A CN 200510061524 A CN200510061524 A CN 200510061524A CN 1312629 C CN1312629 C CN 1312629C
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薛安克
王建中
鲁仁全
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Hangzhou Electronic Science and Technology University
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Abstract

The present invention relates to modeling method of an uncertain hydraulics model for an urban drain system. Presently, the water flow and the water level of water which flows across each pipe network and manholes are estimated by only using human experience in the existing various urban drain optimized dispatching systems. Thereby, the system can not forecast cataclysmic generation and pipeline overflow and can also not achieve the goal of energy saving of a pump station by dispatching the opening of pump station sets. The method of the present invention comprises a hydraulics calculation model which is built on the basis of geographic information of an urban pipeline network, uncertain description, a built uncertain hydraulics model and model correction. The uncertain hydraulics model of the urban drain system is established by the present invention through a data structure technique, a system identification technique, a data mining technique, a robust optimal control technique and a robust forecast control technique. The model can effectively forecast urban cataclysmic generation and can carry out real-time dispatching technology for the pump station.

Description

The uncertain hydraulic model modeling method of sewerage system
Technical field
The invention belongs to areas of information technology, relate to automatic technology, specially refer to the uncertain hydraulic model modeling method of sewerage system.
Background technology
Along with the develop rapidly in city, municipal drainage has become one of bottleneck of restriction city fast development.Municipal drainage is primarily aimed at the existing pumping equipment in city, by unwatering system pipe network model and real-time Monitoring Data, and the operation of global optimization unwatering system, the integrated operation efficient of raising pumping plant network, energy-saving and cost-reducing improves pre-flood, the flood control capacity in city.
Yet in present existing all kinds of municipal drainage Optimization Dispatching system, discharge, the water level of each pipe network, manhole of flowing through can only lean on people's experience estimation, system's generation, pipeline of unpredictable flood thus overflows, and more can't reach the purpose of energy saving in pumping station by the unlatching of scheduling pumping plant unit.
Summary of the invention
The purpose of this invention is to provide the uncertain hydraulic model modeling method of sewerage system, this invention is analyzed with the uncertainty of rainfall amount the Geographic Information System (GIS) of the urban pipe network of sewerage system, set up the uncertain hydraulic model of sewerage system, utilize the accurately generation of predicted city flood of this model, improve the waterlogging-resistant ability of flood control in city, and the operation of scheduling pumping plant, the operational efficiency that improves pumping plant is with energy-saving and cost-reducing.This model has very strong robustness and self-learning function, when the change variation of the city rainfall amount that causes certain period of weather, model can and be caught from motion tracking and be changed, automatically the correction model function, need not human intervention, improved the adaptability of model greatly; Even large-scale uncertain rainfall takes place, model still can guarantee the stability of total system by the adjustment of self parameter.
The present invention is by data structure, System Discrimination, data mining, robust optimum control, Robust Predictive Control technology, set up the uncertain hydraulic model of sewerage system, utilize the generation of predicted city flood effectively of this model, and can carry out the Real-Time Scheduling technology pumping plant.
Model component is as follows:
1. based on the uncertain hydraulic master cast of urban drainage pipe network
This is the core of whole model, this model utilizes computer system can gather and accept the pumping plant of urban pipe network automatically, manhole, pipeline, the geography information of cistern and overflow area and various required data, and initialization model variable, finish two functions: water level and the discharge of at first predicting node sometime in future, process is carried out after the feedback compensation with model output error (forecast model water level and discharge output and observed stage are in the error between the discharge), compare with reference locus (reference value of water level and discharge) again, use quadratic performance index and carry out online rolling optimization, calculate the control action (optimal control law) that current time should be added on system, finish the The whole control circulation.Secondly being responsible for the calling function scheduling broker at each decision point place carries out scheduling decision, and the scheduling result that the function scheduling broker is made is coordinated, and draws the scheduling scheme of overall target optimum, and simulation process is normally carried out.
2. model load module
This module provides: 1. analytic system parameter, enclosed mass: the geography information of the bottom high level of each node, top high level, caliber, water (flow) direction and whole pipe network; 2. the rainfall amount boundary comprises each maximum, minimum rainfall amount constantly of each node overflow area; 3. dynamic data in the municipal drainage is provided, comprises pumping station operation parameter, unit quantity, power and flood warning line, data dispatching etc., for the formulation of the scheduling of pumping plant provides basic data.
3. model output module
This module is finished two functions: 1. output stream is to the water level and the discharge of each node, overflows with the node report that exceeds the flood warning line and gives the operator water takes place; 2. the scheduling result of master scheduling agency output is submitted to the operator with parameters such as dispatching evaluation index.The operator can carry out inching to the result according to the municipal drainage real process, and dispatching algorithm required but also the hydraulic model and the scheduling scheme of realistic draining situation thereby not only satisfied.
Uncertain hydraulic master cast modeling method of the present invention is carried out as follows:
1. based on the foundation of the hydraulics computation model of urban pipe network geography information
A. set up vector multiway tree database based on the urban pipe network geography information
As fundamental node, pipe network is for connecting internodal vector, with VC with manhole, pumping plant, cistern ++As developing instrument, set up vector multiway tree database based on the urban pipe network geography information, data in the database are classified, automatically set up the subject data base of each node, and by data mining technology, be valid data with wherein data locking, become main body function input data.
B. internodal hydraulic calculating method
Based on one dimension free water stream interface St.Venant hydraulics equation, adopt implicit expression finite difference method of value solving, confined flow calculates and adopts " narrow slit method " vertically extending part as closed duct, non-linear completely current equation can be found the solution according to the boundary condition of automatic definition, calculate the discharge and the water level that flow to manhole, computing formula is as follows:
∂ Z ∂ t + 1 R ∂ Q ∂ l = 0 ∂ Q ∂ t + ∂ ∂ l ( Q 2 A ) + gA ∂ Z ∂ l + gA Q | Q | K 2 = 0
Wherein: Z represents that water level, Q represent that flow, t express time, l represent that duct length, R represent that pipe diameter, A represented that water sectional area, K represented that hydromodulus, g represent acceleration of gravity.Z and Q are time dependent variablees, and A, K, R, l are the parameter of pipeline in the pipe network, and its value is decided according to concrete engineering.
Based on multiway tree data structure theory, the node that needs are calculated water level and current can calculate the forward direction water level and the discharge of this node as root node.
2. probabilistic description
The uncertainty that relates to this model mainly shows as two aspects:
C. the uncertainty of pipe network structure
The geography information that is embodied in the some pipe networks of forward path (the expression water (flow) direction is for flowing to this node) of certain node can't be discerned, and promptly the structure of this node forward path pipe network has uncertainty.Therefore, the water level of forward path current and flow value are unknown quantitys, still, can observe its maximal value according to historical data, and promptly the water level of forward path current and flow are unknown bounded quantities, are described below
ΔZ(t，l)＝E zF z(t，l)H z
E wherein zAnd H zFor the parameter of known description pipeline, decide F on concrete engineering z(t l) satisfies ‖ F z(t, l) ‖≤1 are the uncertainty that Lebesgue can survey unit that has about water level.
D. the uncertainty of rainfall amount
(Δ Q (t, l)) also is a unknown bounded quantity to rainfall amount, and its describing method is as follows
‖ΔQ(t，l)‖＜∞
3. the foundation of uncertain hydraulic model
E. set up parameterized model
Forward direction water level and discharge that the 1st step was calculated at sometime each node, and the value by the 2nd uncertain forward direction water level of this node that obtains of step and discharge addition is as the input data, uncertain water level and discharge with node are the input data, with the water level of observer nodes and discharge as output data, based on the process identification theory, foundation is described below based on the uncertain controlled autoregressive moving average model (UCARMA) of the discrete differential form of least square method
[ A ^ ( z - 1 ) + Δ A ^ ( z - 1 ) ] y ( k ) = [ B ^ ( z - 1 ) + Δ B ^ ( z - 1 ) ] u ( k ) + [ C ^ ( z - 1 ) + Δ C ^ ( z - 1 ) ] ξ ( k )
Wherein y (k) represents water level and discharge observed reading, and ζ (k) is that unknown immesurable error white noise disturbs. With
Figure C20051006152400083
The known real parameter that expression obtains by identification,
Figure C20051006152400084
Figure C20051006152400085
With
Figure C20051006152400086
The uncertainty of the parameter of expression norm bounded.
F. change into non-parametric model
The uncertain non-parametric model of the model conversation of last generation one-tenth based on the impulse response transport function; Be described below
z - 1 [ g ^ ( z - 1 ) + Δ g ^ ( z - 1 ) ] = z - 1 B ^ ( z - 1 ) + Δ B ^ ( z - 1 ) A ^ ( z - 1 ) + Δ A ^ ( z - 1 )
Wherein z represent discrete Laplace transformation operator,
Figure C20051006152400088
Indicating impulse response pass function,
Figure C20051006152400089
The uncertain increment of indicating impulse response pass function.
G. the foundation of Predictive Control System
In fact the above uncertain non-parametric model based on the impulse response transport function is exactly the forecast model of predictive control algorithm the inside, according to this forecast model, with Model Predictive Control Algorithm (MAC) is theoretical foundation, set up Predictive Control System, comprise 4 links: forecast model, feedback compensation, rolling optimization, with reference to input trajectory.
4. model correction comprises the steps
H. set up input/output information, earlier uncertain water level of supposition and discharge are zero, and the hydraulics computation model of describing by step 1 calculates water level and discharge as the input data, and observed stage and discharge are as output data;
I. according to forecast model, the output state in prognoses system future (water level and discharge sometime in the future), process is carried out after the feedback compensation with model output error (forecast model water level and discharge output and observed stage are in the error between the discharge), compare with reference locus again, if predicated error is bigger, turn back to step h, adjust the value of uncertain water level and discharge, repeating step h; In the scope that predicated error is being allowed;
J. use quadratic performance index and carry out online rolling optimization, calculate the control action (optimal control law) that current time should be added on system, repeating step i.
The advantage of uncertain hydraulic model modeling method provided by the invention is:
1. uncertain hydraulic model has reflected the influence of the uncertainty of the structural uncertainty of pipe network and rainfall amount to sewerage system, pass through historical data, the utilization Statistics, draw and flow to the bound of each node in the discharge at certain quarter time, and needn't know the discharge in the concrete moment, increase the robustness of model, reduced the calculated amount of modeling.
2. uncertain hydraulic model can be predicted the discharge and the water level of each node, by with the contrast of the observation data of specified node, carry out rolling optimization according to predicated error, calculate the optimum control rate, make the model error minimum.And determine that according to trigger event and the dispatch command for the treatment of emulation the interval of simulation clock carries out emulation, scheduling rule is handled the resource information relevant with task according to the state of there and then, thereby influences the process of emulation.
3. uncertain hydraulic model reflects the scheduling index request based on the draining task, as: open the quantity minimum of pumping plant, open the geographic position optimum of pumping plant, the electric energy minimum that pumping plant consumed, cistern pondage maximum.And record emulation dispatch result and current municipal drainage state parameter.Preserve all emulation intermediate data, when environmental parameter and scheme for irrigation and drainage and task change, can continue emulation, satisfy the requirement of scheduling again and dynamic real-time scheduling according to current state.
4. uncertain hydraulic model can also compare to each bar scheduling result and scheduling evaluation index, if the result meets the scheduling index, then with this result record in addition; Otherwise, select the emulation in addition of new scheduling rule, meet the requirement of dispatching evaluation rule up to the result.
5. the mathematical simulation model of uncertain hydraulic model has the ability of stronger object-oriented and figure.Can comprise according to current rainfall situation: the time of rainfall, the zone of rainfall, situations such as the size of rainfall amount are carried out patterned hydraulics analysis to the entire city drainage pipeline networks, and calculate the water level and the discharge of each node in real time; According to the task situation of scheduling, as the location distribution of pumping plant and the quantity of pumping plant, the unit quantity of single pumping plant etc. is carried out emulation simultaneously.
6. set up the decision rule storehouse.According to different regulation goals,, form the decision rule storehouse with the scheduling rule classification.With simulation result with two-dimensional coordinate figure, three-dimensional coordinate figure, forms such as tabulation, statistical report form are showed, thereby help statistical study intuitively and demonstration.
7. reduce the influence of uncertain factor to system performance, can the quick response system disturbance, improved the robustness and the reliability of system, remedied the deficiency of traditional modeling method.Introduce the ultimate principle of PREDICTIVE CONTROL, the output state in prognoses system future at first removes to determine the control action of current time again, promptly predict afterwards earlier and control, total system has foresight, obviously is better than having earlier feedback information, produces the classical feedback control system of control action again.
8. scheduling is provided at the line decision support, the implementation of monitoring and scheduling by analysis and the evaluation to simulation result, is found out the defective of system call strategy, so that improve the scheduling strategy and the algorithm of system targetedly, solves scheduling problem effectively.
9. the mathematical model of Jian Liing has automatic study and self compensation function, has very strong adaptability.
Embodiment
Below in conjunction with an embodiment scheme, be described in detail method of the present invention.
Unwatering system with certain block, city is an example, and there are four pumping plants in this system, and each pumping plant has some units, and every unit has different power, four cistern, several drainage manholes, some drainage pipelines.At the uncertain rainfall amount in this area, in order to make each pumping plant, every unit is brought into play efficient, energy-saving and cost-reducing the biglyyest, and prevents that the water of manhole from overflowing, and forms flood, must set up the uncertain hydraulic model of unwatering system.Its step is as follows:
1. with the master data of individual node, for example people's bore dia, bottom high level, top high level etc., based on one dimension free water stream interface St.Venant hydraulics equation, adopt implicit expression finite difference method of value solving, confined flow calculates and adopts " narrow slit method " vertically extending part as closed duct, non-linear completely current equation can be found the solution according to the boundary condition of automatic definition, calculate the node forward direction with the back to discharge and water level;
With the pumping plant node as root node, based on vector multiway tree data structure theory, set up the tree topology structure that flows to this pumping plant, and calculate discharge and the water level that flows to pumping plant according to the set membership of root node and child node based on geography information;
3. repeated for the 1st step, write sewerage system hydraulics and calculate the interface, and set up the database of the tree data structure of whole drainage pipeline networks based on geography information, data comprise the master data of each node, as master data of manhole, pumping plant, cistern etc., data in the database are classified, automatically set up the subject data base of each node, and by data mining technology, be valid data with wherein data locking, become main body function input data;
4. the forward direction water level of the uncertain pipe network that flows to manhole sometime that draws by data mining technology and the bound of discharge and precipitation area rainfall amount are set up the uncertainty description of node forward direction water level and current;
5. forward direction water level and the discharge that the 3rd step was calculated at sometime each node, and by the 4th uncertain forward direction water level of this node that obtains of step and discharge addition as the input data, with the water level of observer nodes and discharge as output data, based on the process identification theory, set up uncertain controlled autoregressive moving average model (UCARMA) based on the discrete differential form of least square method;
6. the model conversation that the 5th step was produced becomes the uncertain non-parametric model based on the impulse response transport function;
7. in fact the above uncertain non-parametric model based on the impulse response transport function is exactly the forecast model of predictive control algorithm the inside, according to this forecast model, with Model Predictive Control Algorithm (MAC) is theoretical foundation, set up Predictive Control System, comprise 4 links: a. forecast model, b. feedback compensation, c. rolling optimization, d. with reference to input trajectory (node water level and discharge reference value, a kind of desirable water level and discharge).
8. Predictive Control System is used in the past and following input/output information, according to forecast model, the output state in prognoses system future (water level and discharge sometime in the future), process is carried out after the feedback compensation with model output error (forecast model water level and discharge output and observed stage are in the error between the discharge), compare with reference locus again, use quadratic performance index and carry out online rolling optimization, calculate the control action (optimal control law) that current time should be added on system, finish the The whole control circulation.
When the change variation of certain regional rainfall amount of causing certain period of weather, and the reason of urban construction causes the variation of urban drainage pipe network structure, uncertain hydraulic model adopts intelligence learning compensation kernel, 1 to 4 step of repeating step, model can and be caught from motion tracking and be changed, automatically the correction model parameter need not human intervention; Even large-scale uncertain rainfall takes place, model still can guarantee the stability of whole Predictive Control System by the adjustment of self parameter.

Claims (1)

1, the uncertain hydraulic model modeling method of sewerage system is characterized in that this method may further comprise the steps:
(1) foundation comprises the steps based on the hydraulics computation model of urban pipe network geography information
A. set up vector multiway tree database based on the urban pipe network geography information
With manhole, pumping plant, cistern as fundamental node, pipe network is for connecting internodal vector, foundation is based on the vector multiway tree database of urban pipe network geography information, data in the database are classified, automatically set up the subject data base of each node, and by data mining technology, be valid data with wherein data locking, become main body function input data;
B. the hydraulics between computing node
Based on one dimension free water stream interface St.Venant hydraulics equation, adopt implicit expression finite difference method of value solving, confined flow calculates and adopts the vertically extending part of narrow slit method as closed duct, non-linear completely current equation can be found the solution according to the boundary condition of automatic definition, calculate the discharge and the water level that flow to node, computing formula is
∂ Z ∂ t + 1 R ∂ Q ∂ l = 0 ∂ Q ∂ t + ∂ ∂ l ( Q 2 A ) + gA ∂ Z ∂ l + gA Q | Q | K 2 = 0
Wherein Z represents that water level, Q represent that flow, t express time, A represented that water sectional area, K represented that hydromodulus, R represent that pipe diameter, l represent that duct length, g are acceleration of gravity, Z and Q are time dependent variablees, A, K, R, l are the parameter of pipeline in the pipe network, and its value is decided according to concrete engineering;
Based on multiway tree data structure theory, the node that needs are calculated water level and current calculates the forward direction water level and the discharge of this node as root node;
(2) probabilistic description comprises
C., the uncertainty of pipe network structure is described
Observe its maximal value according to historical data, promptly the water level of forward path current and flow are unknown bounded quantities, be described as Δ Z (t, l)=E zF z(t, l) H z, E wherein zAnd H zFor the parameter of known description pipeline, decide F on concrete engineering z(t l) satisfies ‖ F z(t, l) ‖≤1 are the uncertainty that Lebesgue can survey unit that has about water level;
D., the uncertainty of rainfall amount is described
Rainfall amount also is a unknown bounded quantity, and it is described as ‖ Δ Q (t, l) ‖＜∞, Δ Q (t, l) expression rainfall amount;
(3) set up uncertain hydraulic model, comprise the steps
E. set up parameterized model
Forward direction water level and discharge that (1) step was calculated at sometime each node, and the value by (2) the uncertain forward direction water level of this node that obtains of step and discharge addition is as the input data, with the water level of observer nodes and discharge as output data, based on the process identification theory, foundation is described as based on the uncertain controlled autoregressive moving average model of the discrete differential form of least square method
[ A ^ ( z - 1 ) + Δ A ^ ( z - 1 ) ] y ( k ) = [ B ^ ( z - 1 ) + Δ B ^ ( z - 1 ) ] u ( k ) + [ C ^ ( z - 1 ) + Δ C ^ ( z - 1 ) ] ξ ( k )
Wherein y (k) represents water level and discharge observed reading, and ζ (k) is that unknown immesurable error white noise disturbs, With
Figure C2005100615240003C3
The known real parameter that expression obtains by identification,
Figure C2005100615240003C4
With
Figure C2005100615240003C5
The uncertainty of the parameter of expression norm bounded;
F. change into non-parametric model
With the uncertain non-parametric model of model conversation one-tenth, be described as based on the impulse response transport function
z - 1 [ g ^ ( z - 1 ) + Δ g ^ ( z - 1 ) ] = z - 1 B ^ ( z - 1 ) + Δ B ^ ( z - 1 ) A ^ ( z - 1 ) + Δ A ^ ( z - 1 )
Wherein z represent discrete Laplace transformation operator,
Figure C2005100615240003C7
Indicating impulse response pass function, The uncertain increment of indicating impulse response pass function;
G. set up Predictive Control System
With the Model Predictive Control Algorithm is that theoretical foundation is set up Predictive Control System, and Predictive Control System comprises forecast model, feedback compensation, rolling optimization, with reference to input trajectory, and wherein forecast model is non-parametric model;
(4) model correction comprises the steps
H. set up input/output information, earlier uncertain water level of supposition or discharge are zero, and the hydraulics computation model of describing by step (1) calculates water level and discharge as the input data, and observed stage and discharge are as output data;
I. according to forecast model, the output state in prognoses system future is through after carrying out feedback compensation with the model output error, compare with reference locus again,, turn back to step h if predicated error is bigger, adjust the value of uncertain water level and discharge, repeating step h; In the scope that predicated error is being allowed;
J. use quadratic performance index and carry out online rolling optimization, calculate the control action that current time should be added on Predictive Control System, repeating step i.
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《设备状态监测无线集成网络研究及工业应用开发》 徐晓东，硕士论文 2004 *
《设备状态监测无线集成网络通信技术的研究》 唐建明，硕士论文 2005 *
《设备状态监测无线集成网络通信技术的研究》 唐建明，硕士论文 2005;《设备状态监测无线集成网络研究及工业应用开发》 徐晓东，硕士论文 2004 *

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