CN115167308A - Water transfer engineering multi-target prediction control algorithm for guiding gate regulation and control - Google Patents
Water transfer engineering multi-target prediction control algorithm for guiding gate regulation and control Download PDFInfo
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Abstract
The invention discloses a multi-target predictive control algorithm for water transfer engineering for guiding gate regulation, which is characterized by comprising the following steps: constructing an integral time-lag model based on channel pool parameters to realize the prediction of the water level; setting a control target based on the control times of the gate, and introducing the variable amplitude constraint of the gate opening to further construct a multi-target prediction control model; and solving the multi-target prediction control model by using a multi-target particle group intelligent optimization algorithm. The invention utilizes a multi-target canal pond water level prediction control model with a gate control frequency punishment quantity and a gate dead zone constraint, directly adopts a check gate opening adjustment quantity as an optimized regulation variable, and solves through a multi-target particle swarm optimization (MOPSO) algorithm based on Pareto domination thought, so that the gate control accuracy is improved by directly adjusting the gate opening on the premise of not influencing the water level regulation effect, and the effect of reducing the gate control frequency is achieved.
Description
Technical Field
The invention relates to the technical field of real-time regulation and control of open channel water delivery engineering, in particular to a water transfer engineering multi-target predictive control algorithm for guiding the regulation and control of a gate.
Background
The construction of large water transfer projects is an important means for solving the problem of uneven spatial and temporal distribution of water resources. Because of small construction investment, large water delivery flow and convenient construction and maintenance, open channel water delivery becomes a common water delivery mode of large-scale water transfer engineering. With the continuous progress of the technology, various complex open channel water transfer projects are produced at the same time. In a complex open channel regulation system, regulation buildings such as a control gate or a pump station are generally included, and the regulation buildings divide a water regulation project into a plurality of ditches, but the regulation capacity of each ditch is limited. The hydraulic control target of the open channel water transfer engineering mainly takes safety control and water delivery stability control as main targets, and the water supply target is generally completed through regulation and control of a check gate or a pump station. The safety control of the water level in the open channel water transfer is usually affected by unknown hydraulic disturbance, a simulation model formed based on an eriodictyon equation set cannot solve the problem of unknown disturbance, and some scholars propose simplified models such as a storage quantity model, an integral-Delay model, a simplified eriodictyon equation (Reduced Saint-Venant model) and the like. Although the simplified model cannot accurately describe the dynamic characteristics of the control object, the simplified model can correct the model according to the real-time feedback information in the prediction control mode, and the provision of the simplified model lays the foundation of research on the aspect of channel water level control of the prediction control algorithm. Many studies have indicated that simplified models work well when used to make predictive control algorithm adjustments. However, in the existing predictive control model, the hydraulic regulation and control target form of predictive control is single, many problems in the actual engineering are not convex planning problems, and the existing solution target also limits the application scene of the predictive control in the actual engineering. In addition, in the existing predictive control research, the regulation and control instruction generated by the predictive control model is flow regulation and control quantity, and under the condition that the dead zone of the gate is too large or the regulation upper limit of the gate is small, part of small-flow regulation and large-flow regulation instructions cannot be effectively executed.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned problems.
Therefore, the technical problem solved by the invention is as follows: the existing multistage series-connection channel pond is commonly used for solving the problems that a water level prediction control algorithm of a single control target cannot solve the non-convex planning problem in actual engineering and the existence of a gate dead zone cannot be considered, so that the gate is controlled too frequently.
In order to solve the technical problems, the invention provides the following technical scheme: a water regulating engineering multi-target predictive control algorithm for guiding gate regulation comprises the following steps:
constructing an integral time-lag model based on channel pool parameters to realize the prediction of the water level;
setting a control target based on the gate adjustment quantity, and introducing gate opening amplitude variation constraint to further construct a multi-target prediction control model;
and solving the multi-target predictive control model by using an intelligent optimization algorithm of the multi-target particle group.
As a preferred scheme of the multi-target predictive control algorithm for water regulation engineering for guiding gate regulation, the method comprises the following steps: the channel pond parameters comprise:
the length, bottom width, side slope, bottom slope and hydraulic parameter characteristic value under the initial working condition of the channel pond.
The invention relates to a preferable scheme of a multi-target predictive control algorithm of water regulating engineering for guiding gate regulation, wherein: the construction of the integral time-lag model comprises the following steps:
constructing an integral time-lag model for describing the water level of the downstream control point, wherein the integral time-lag model is expressed as:
wherein H d The deviation value m of the water level control point at the downstream of the channel pond relative to the target water level; t is time, s; q. q of in Is the variation of the inlet flow of the canal pit relative to the initial state, m 3 /s；q out Is the variation of the outlet flow of the canal pit relative to the initial state, m 3 /s；q offtake Is the variation of the water diversion quantity of the ditch pool relative to the initial state, m 3 /s；A d For the influence of flow variation on the downstream water level, m 2 ；t d Is the lag time, s, of the influence of the inlet flow of the channel pond on the downstream water level.
As a preferred scheme of the multi-target predictive control algorithm for water regulation engineering for guiding gate regulation, the method comprises the following steps: the building of the integral time-lag model further comprises the following steps:
an integral time-lag model describing the water level of the upstream control point is constructed and expressed as:
wherein H u The deviation value m of the water level of the upstream water level control point of the channel pond relative to the target water level; a. The u For the influence of flow variation on the upstream water level, m 2 ；t u The delay time s of the influence of the outlet flow of the channel pond on the upstream water level; q. q.s in Is the variation of the inlet flow of the canal pit relative to the initial state, m 3 /s；q out Is the variation of the outlet flow of the canal pit relative to the initial state, m 3 /s；q offtake M is the variation of the water diversion quantity of the ditch pool relative to the initial state 3 /s。
The invention relates to a preferable scheme of a multi-target predictive control algorithm of water regulating engineering for guiding gate regulation, wherein: set up control target based on gate control number of times, include:
the following control targets are combined by weighting these targets of the minimum deviation of the water level, the minimum flow rate adjustment operation, and the gate operation:
wherein L is i，j Is used for representing the parameter of flow adjustment, when the flow is adjusted and controlled, the time value is 1, and when the flow is not adjusted and controlled, the time value is 0; q i，j Weight of parameter R corresponding to water level deviation i，j Flow variation corresponding parameter weight, K i，j And representing the weight of the parameter corresponding to the change of the gate opening.
As a preferred scheme of the multi-target predictive control algorithm for water regulation engineering for guiding gate regulation, the method comprises the following steps: the establishing of the multi-target predictive control model comprises the following steps:
selecting the gate opening variation as an optimization variable, predicting the flow variation delta Q (k) caused by the gate opening adjustment in each step, wherein the flow variation prediction formula is as follows:
wherein, C d Is the brake-passing flow coefficient; l is the gate width, m; h 0 (k) The water level value before the gate, m, is predicted in the kth step; h s (k) The predicted water depth after gate, m, of step k.
As a preferred scheme of the multi-target predictive control algorithm for water regulation engineering for guiding gate regulation, the method comprises the following steps: the gate opening amplitude variation constraint comprises:
introducing the maximum amplitude constraint and the minimum amplitude constraint of the gate opening.
The invention relates to a preferable scheme of a multi-target predictive control algorithm of water regulating engineering for guiding gate regulation, wherein: the minimum amplitude constraint of the gate opening degree comprises the following steps:
and setting the minimum variable amplitude constraint of the gate opening caused by the dead zone of the gate by adopting the absolute value lower limit of the set optimization variable in the optimization model: | Δ G i,n |≥0.03m
Wherein, Δ G i,n And adjusting the amplitude of change m for the opening of the ith check gate at the nth moment.
The invention relates to a preferable scheme of a multi-target predictive control algorithm of water regulating engineering for guiding gate regulation, wherein: the maximum amplitude constraint of the gate opening degree comprises the following steps:
according to the water level and flow requirements of the check gate and the self opening adjusting capacity of the gate, the maximum opening constraint of the gate is set, and the maximum opening of the gate should meet the following constraint conditions:
|G i,n |≤G i,max
wherein G is i,n The opening degree, m, of the ith regulating brake at the nth moment is regulated once; g i,max And the maximum allowable opening degree m of the single adjustment of the ith seat damper.
The invention relates to a preferable scheme of a multi-target predictive control algorithm of water regulating engineering for guiding gate regulation, wherein: the solving multi-target prediction control model using the multi-target particle group intelligent optimization calculation comprises the following steps:
random population selection is carried out in the initial state, a decision variable which is a gate opening adjustment quantity and is initially and randomly generated by an optimization algorithm is transmitted to an integral time-lag model, calculation of a corresponding control target value under given weight is carried out through a state equation, and the target value is stored in a solution set;
updating the optimal solution set and the particle state in the optimization algorithm by comparing the control target values of different states;
and (4) retransmitting the updated decision variable to the integral time-lag model, and repeating the steps at the same time until the optimal decision variable value meeting the control target is found.
The invention has the beneficial effects that: the invention utilizes a multi-target canal pond water level prediction control model with a gate control frequency punishment quantity and a gate dead zone constraint, directly adopts a check gate opening adjustment quantity as an optimized regulation variable, and solves through a multi-target particle swarm optimization (MOPSO) algorithm based on Pareto domination thought, so that the gate control accuracy is improved by directly adjusting the gate opening on the premise of not influencing the water level regulation effect, and the effect of reducing the gate control frequency is achieved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
FIG. 1 is a general flowchart of a multi-objective predictive control algorithm for water diversion engineering for guiding gate regulation according to an embodiment of the present invention;
fig. 2 is a flowchart for solving a multi-target predictive control model based on an MOPSO algorithm according to an embodiment of the present invention;
FIG. 3 is a schematic view of a research trench provided in accordance with an embodiment of the present invention;
FIG. 4 is a control result of the multi-target predictive control algorithm applied to a disturbance condition according to an embodiment of the present invention;
FIG. 5 is a control result of a conventional algorithm applied to a disturbance condition according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, embodiments accompanying figures of the present invention are described in detail below, and it is apparent that the described embodiments are a part, not all or all of the embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, the references herein to "one embodiment" or "an embodiment" refer to a particular feature, structure, or characteristic that may be included in at least one implementation of the present invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in a specific case to those of ordinary skill in the art.
Example 1
Referring to fig. 1 to 2, in an embodiment of the present invention, a multi-objective predictive control algorithm for water diversion engineering for guiding gate regulation is provided, including:
s1: constructing an integral time-lag model based on channel pool parameters to realize the prediction of the water level;
it should be noted that the trench parameters specifically include the length, bottom width, side slope, bottom slope of the trench, and hydraulic parameter characteristic values under the initial working condition.
Furthermore, an integral time-lag model for describing the water level of the downstream control point is constructed, and the relation between the water level of the downstream control point of the channel and the flow variation of the inlet and the outlet of the channel pool is generally described by adopting the integral time-lag model in the following form in the past research:
wherein H d The deviation value m of the water level of a water level control point at the downstream of the channel pond relative to a target water level; t is time, s; q. q.s in Is the variation of the inlet flow of the canal pit relative to the initial state, m 3 /s；q out Is the variation of the outlet flow of the canal pit relative to the initial state, m 3 /s；q offtake M is the variation of the water diversion quantity of the ditch pool relative to the initial state 3 /s；A d The flow change affects the downstream water level to obtain an equivalent water surface area m 2 ；t d Is the lag time, s, of the influence of the inlet flow of the channel pond on the downstream water level.
Furthermore, an integral time lag model for describing the water level of the upstream control point is constructed;
by referring to an integral time lag model for describing the water level of a downstream control point, an integral time lag model form for describing the upstream water level control point of the channel pool is constructed, and the following relational expression for describing the water level deviation of the upstream control point of the channel and the flow variation of the inlet and the outlet of the channel pool is provided:
wherein H u Upstream of the ditch poolDeviation amount, m, of the water level control point with respect to the target water level; a. The u For the influence of flow variation on the upstream water level, m 2 ；t u The delay time s of the influence of the outlet flow of the channel pond on the upstream water level; q. q.s in Is the variation of the inlet flow of the canal pit relative to the initial state, m 3 /s；q out Is the variation of the outlet flow of the canal pit relative to the initial state, m 3 /s；q offtake Is the variation of the water diversion quantity of the ditch pool relative to the initial state, m 3 /s。
It should be noted that, although the control point water level is located downstream in the open channel water diversion project, when the downstream water level control is performed, only the downstream water level needs to be predicted. When the controlled variable is the gate opening, the water level state is predicted based on the flow rate variation in the prediction model, and therefore, it is necessary to calculate a flow rate adjustment value by the gate opening adjustment variation. In the overflow state due to the gate hole, the amount of flow change is related to the amount of change in the opening of the damper gate, the water level before the damper gate (the water level at the downstream end of the trench), and the water level after the damper gate (the water level at the upstream end of the trench). Therefore, the prediction of the upstream water level of the trench pond is also required in the prediction control model.
S2: setting a control target based on the control times of the gate, and introducing a gate opening amplitude variation constraint to construct a multi-target predictive control model;
further, the targets of the minimum deviation of the water level, the minimum flow rate adjustment action and the gate action are weighted and combined to form the following control targets:
wherein L is i，j Is used for representing the parameter of flow adjustment, when the flow is adjusted and controlled, the time value is 1, and when the flow is not adjusted and controlled, the time value is 0; q i，j Weight of parameter R corresponding to water level deviation i，j Flow variation corresponding parameter weight, K i，j And representing the weight of the parameter corresponding to the change of the gate opening.
It should be noted that the gate control frequency penalty is considered from penalty factors such as minimum deviation of water level, minimum flow adjustment action, gate action and the like, so as to obtain a multi-target control target function formula, and the predictive control model can directly generate an optimal control scheme.
Furthermore, the gate opening variation is selected as an optimization variable, the flow variation delta Q (k) caused by the gate opening adjustment in each step is predicted, and the flow variation prediction formula is expressed as:
wherein, C d Is the brake-passing flow coefficient; l is the gate width, m; h 0 (k) The predicted water level value before the gate in the kth step, m; h s (k) The predicted water depth after gate, m, of step k.
It should be noted that, the traditional predictive control model adopts the flow regulating and controlling quantity as an optimization variable, and then calculates the gate opening regulating quantity to be executed by the flow regulating and controlling quantity through the gate flow calculation formula, so that the problems of dead zone constraint and regulating range constraint of the gate opening cannot be considered in the optimization strategy calculation process of the predictive control model, and part of the flow regulating and controlling quantity cannot be effectively executed through gate opening regulation, so that the gate opening variation is directly selected as the optimization variable; and predicting the flow variation delta Q (k) caused by the gate opening adjustment of each step, thereby realizing the calculation of the water level by using an integral time lag model.
Furthermore, the minimum amplitude variation constraint of the gate opening caused by the dead zone of the gate is considered by setting the absolute value lower limit of an optimization variable in the optimization model: | Δ G i,n |≥0.03m (5)
Wherein, Δ G i,n And adjusting the amplitude of change m for the opening of the ith check gate at the nth moment.
It is to be noted that, in the check gate, a very small gate opening change command cannot be effectively executed due to a control accuracy limit of the gate control device, and such an opening change value that cannot be executed is referred to as a dead zone of the gate.
In actual engineering, the dead zone of the gate is generally about 0.03m to 0.08m, and therefore, the dead zone value of the controlled gate is assumed to be 0.03m.
It should be noted that, because of the existence of the gate dead zone, a minimum variation constraint of the gate opening bat needs to be introduced into the predictive control model.
Furthermore, according to the water level and flow requirement of the check gate and the self opening regulating capacity of the gate, the maximum opening constraint of the gate is set, and the maximum opening of the gate should meet the following constraint conditions:
|G i,n |≤G i,max (6)
wherein G is i,n The opening degree, m, of the ith regulating brake at the nth moment is regulated once; g i,max And the maximum allowable opening degree m of the single adjustment of the ith seat damper.
S3: and solving the multi-target prediction control model by using a multi-target particle swarm optimization (MOPSO) intelligent optimization algorithm.
Further, random population selection is carried out in the initial state, decision variables (gate opening adjustment quantity) generated by the optimization algorithm initially and randomly are transmitted to an integral time-lag model, calculation of corresponding control target values under given weight is carried out through a state equation, and the target values are stored in a solution set;
furthermore, the optimal solution set and the particle state in the optimization algorithm are updated by comparing the control target values of different states;
and further, the updated decision variable is transmitted to the integral time lag model again, and the steps are repeated until the optimal decision variable value meeting the control target is found.
It should be noted that in the multi-target predictive control model constructed by the invention, the control target equation is a multi-target control target function formula, and the optimal solution obtained by searching through a feasible method cannot be guaranteed to be globally optimal; in order to obtain an optimal solution with high feasibility, the optimization control problem is solved by adopting a multi-target particle swarm optimization (MOPSO) intelligent optimization algorithm.
Example 2
Referring to fig. 3-5, a water diversion project multi-target predictive control algorithm for guiding gate regulation is provided for an embodiment of the invention, and scientific demonstration is carried out through economic benefit calculation and simulation experiments in order to verify the beneficial effects of the invention.
In this embodiment, the last 6 sections of the series-connected canal ponds of a certain project are selected as research objects, and the state of the canal ponds under predictive control is simulated by constructing a simulation model of the canal pond system.
The total length of the research channel pond is 112km, the sections of the open channels are all trapezoidal sections, a water distribution port is arranged at the tail end of each channel pond to supply water to a water user, and the generalization of the channel pond in the research section is shown in figure 3; assuming that the upstream and downstream of the canal system are water level boundaries with water depth of 7m and 3m respectively, the water delivery system is in a stable state at the initial moment, and the water level of each control point is stabilized at a target water level; in the actual regulation and control of engineering, the water level of each channel pool control point is not strictly required to be stabilized on the target water level, and the water level of the control point can fluctuate within the range of +/-0.1 m of the target water level.
Selecting the time step length for researching the operation of the channel pool simulation model as 1min; in order to check the effectiveness of the regulation and control algorithm, disturbance needs to be set to change the channel pool from a steady state to an unsteady state, and then the water level is controlled by using a control algorithm; the set disturbance working condition is as follows: the flow of the divided water in the ditch pool 4 is suddenly reduced by 7m at the moment of 4h 3 And the variation of the water diversion flow rate is unknown from the perspective of a regulator.
1. The implementation procedure of this embodiment
The method comprises the following steps: and collecting corresponding channel pond parameters and water level indexes, and establishing a prediction control model.
In order to establish a predictive control model of a research section, the corresponding basic design parameters of a research channel pool and the characteristic values of hydraulic parameters under the initial working condition are known, and meanwhile, the integral time-lag model parameters of the channel pool under the initial flow working condition are identified according to a parameter identification method; the corresponding parameter indexes are shown in table 1 below.
TABLE 1 study of basic parameters of the trench and the flow, water level and integral time lag model parameters under the initial condition
Step two: establishing a multi-target predictive control model considering gate control times punishment, and setting corresponding target weight indexes
1) Check gate parameter setting
Because the decision variables in the research are gate adjustment variables, the damper brake needs to be generalized and processed in the model construction, and the known damper brake parameters are as follows:
TABLE 2 Regulation brake parameters of research section
2) Multi-target weight setting
Based on the characteristic of an integral time-lag model, the setting and control interval of the check gate is shorter, and the control is more frequent. In order to reduce the regulation frequency, a multi-target prediction control model considering a gate control punishment index is established. The method comprises the steps of minimizing deviation of water level, minimizing flow adjustment action and constraining gate action. These targets are weighted according to the degree of importance of each index.
In equation (3), the larger the value of the weight element in the weight matrix Q, the more sensitive the algorithm is to the water level deviation, and to avoid frequent traffic adjustment, the weight of the traffic adjustment in R is set to 1, and then the value of the element in the weight coefficient matrix Q is adjusted. In the control object herein, only the portion of the control variable whose absolute value of the flow adjustment is larger than the flow change value is weighted. In the weight coefficient matrix Q, when the absolute value of the water level deviation is less than or equal to 0.1m, the corresponding weight value is 10, and when the absolute value of the deviation is greater than 0.1m, the corresponding weight value is 30. The control operation weight K of the shutter is set to 1.
Step three: prediction model solution based on MOPSO optimization algorithm
The multi-target prediction model of the embodiment is optimized and solved by adopting an MOPSO algorithm, and the parameter setting of the MOPSO algorithm is shown in Table 3.
TABLE 3 MOPSO Algorithm parameter settings
2. Comparison of results
In a traditional predictive control Model (MPC), a quadratic programming algorithm is often adopted for solving, and the selection of a control time interval of the traditional model needs to comprehensively consider the lag time of a channel pool and the actual regulation and control requirements. The time lag time of each channel pool is 24-75 min, and considering that the actual regulation and control interval of the central line is in the unit of hours, the control time interval is selected to be 1h. The corresponding prediction time domain is set to 10, so that the prediction process can cover the time lag duration of the whole system (about 5 h), and the control time domain is set to 5. The dead zone of the gate of the central line is 3cm, and the corresponding flow variation value obtained by trial calculation is about 3m 3 S, so in a conventional control model, the absolute value of the regulatory amplitude of the flow should be greater than or equal to 3m 3 And s. In the traditional algorithm solving process, because the decision variable is the flow adjustment quantity, partial small flow adjustment and large flow adjustment instructions can not be effectively executed under the condition that the dead zone of the gate is too large or the adjustment upper limit of the gate is small, so that the regulation and control effect is influenced.
In the multi-objective intelligent optimization algorithm adopted by the method, the gate adjustment quantity is taken as a decision variable, the control objective considers the flow punishment quantity, the gate action and the gate dead zone constraint, and the gate regulation and control times are effectively reduced. The calculation results of the invention are shown in figure 4, the calculation results of the traditional algorithm are shown in figure 5, and the statistical table of the gating times of each model is shown in figure 4.
TABLE 4 gating frequency of each control model in disturbance condition
In the process of the water level deviation in fig. 4 and 5, it can be seen that the water level deviation in the trench pool 4 in fig. 5 is slightly larger than 0.1m, and the water level deviation in other trench pools is stabilized within 0.1m, which shows that the control algorithm has better control effect. However, after 24h, the gate adjustment action is more frequent in the conventional algorithm control compared with the multi-objective algorithm control in the text, so that the water level deviation and the change trend of each channel pool are continuously reduced and kept consistent under the conventional algorithm (fig. 5), while the water level deviation and the change trend in each channel pool are greatly different in the optimization model in the text (fig. 4).
As can be seen from table 4, the number of gating times in the present optimal control model is relatively small due to the addition of the flow adjustment penalty. In the first 24h, because the water level of the whole line at this stage is in the rapid descending stage, the water level of both the two regulation models needs to be gradually regulated by adopting more gating, and the gating times under the regulation and control of the two models are respectively 22 and 34. In the last 24h, the gating times under the regulation and control of the two models are respectively 1 and 18, the regulation and control times under the optimization algorithm are obviously less than those of the traditional algorithm, and the water level regulation effect of the optimization algorithm under unknown disturbance is better. From the total gating times within 48h, the optimization algorithm can reduce the gating times by 56% compared with the gating times under the regulation and control of the traditional algorithm, and the generated gating scheme has stronger applicability.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (10)
1. A water regulation engineering multi-target predictive control algorithm for guiding gate regulation is characterized by comprising the following steps:
constructing an integral time-lag model based on channel pool parameters to realize the prediction of the water level;
setting a control target based on the control times of the gate, and introducing the variable amplitude constraint of the gate opening to further construct a multi-target prediction control model;
and solving the multi-target prediction control model by using a multi-target particle group intelligent optimization algorithm.
2. The multi-objective predictive control algorithm for water conditioning engineering for directing gate regulation as claimed in claim 1, wherein: the trench pool parameters include:
the length, bottom width, side slope, bottom slope and hydraulic parameter characteristic values under the initial working condition of the channel pond.
3. The multi-objective predictive control algorithm for water conditioning engineering for directing gate regulation as claimed in claim 2, wherein: the construction of the integral time-lag model comprises the following steps:
constructing an integral time-lag model for describing the water level of the downstream control point, and expressing as follows:
wherein H d The deviation value m of the water level control point at the downstream of the channel pond relative to the target water level; t is time, s; q. q.s in Is the variation of the inlet flow of the canal pit relative to the initial state, m 3 /s；q out Is the variation of the outlet flow of the canal pit relative to the initial state, m 3 /s；q offtake M is the variation of the water diversion quantity of the ditch pool relative to the initial state 3 /s；A d The flow change affects the downstream water level to obtain an equivalent water surface area m 2 ；t d Is the lag time, s, of the influence of the inlet flow of the channel basin on the downstream water level.
4. The multi-objective predictive control algorithm for water conditioning engineering for directing gate regulation as claimed in claim 3, wherein: the building of the integral time-lag model further comprises the following steps:
an integral time-lag model describing the water level of the upstream control point is constructed and expressed as:
wherein H u The deviation value m of the water level of the upstream water level control point of the channel pond relative to the target water level; a. The u The flow change affects the upstream water level to obtain an equivalent water surface area m 2 ；t u The delay time s of the influence of the outlet flow of the channel pond on the upstream water level; q. q of in Is the variation of the inlet flow of the canal pit relative to the initial state, m 3 /s；q out Is the variation of the outlet flow of the canal pit relative to the initial state, m 3 /s； qofftake M is the variation of the water diversion quantity of the ditch pool relative to the initial state 3 /s。
5. The multi-objective predictive control algorithm for water conditioning engineering for directing gate regulation of claim 4, characterized in that: set up control target based on gate control number of times, include:
the following control targets are combined and formed by weighting the targets of the minimum deviation of the water level, the minimum flow adjusting action and the gate action:
wherein L is i，j Is used for representing the parameter of flow adjustment, when the flow is adjusted and controlled, the time value is 1, and when the flow is not adjusted and controlled, the time value is 0; q i，j Weight of parameter corresponding to water level deviation, R i，j Flow variation corresponding parameter weight, K i，j And representing the weight of the parameter corresponding to the change of the gate opening.
6. The water regulating engineering multi-target predictive control algorithm for guiding gate regulation as claimed in claim 5, characterized in that: the establishing of the multi-target predictive control model comprises the following steps:
selecting the gate opening variation as an optimization variable, predicting the flow variation delta Q (k) caused by the gate opening adjustment in each step, wherein the flow variation prediction formula is as follows:
wherein, C d Is the brake-passing flow coefficient; l is the gate width m; h 0 (k) The predicted water level value before the gate in the kth step, m; h s (k) The predicted water depth after gate, m, of step k.
7. The multi-objective predictive control algorithm for water conditioning engineering for directing gate regulation of claim 6, characterized by: the gate opening amplitude variation constraint comprises:
and introducing a maximum amplitude constraint and a minimum amplitude constraint of the gate opening.
8. The multi-objective predictive control algorithm for water conditioning engineering for directing gate regulation of claim 7, characterized by: the minimum amplitude constraint of the gate opening degree comprises the following steps:
and setting the minimum variable amplitude constraint of the gate opening caused by the dead zone of the gate by adopting the absolute value lower limit of the set optimization variable in the optimization model: | Δ G i,n |≥0.03m
Wherein, Δ G i,n And adjusting the amplitude of change m for the opening of the ith check gate at the nth moment.
9. The multi-objective predictive control algorithm for water conditioning engineering for directing gate regulation of claim 8, characterized by: the maximum amplitude constraint of the gate opening degree comprises the following steps:
according to the water level and flow requirements of the check gate and the self opening adjusting capacity of the gate, the maximum opening constraint of the gate is set, and the maximum opening of the gate should meet the following constraint conditions:
|G i,n |≤G i,max
wherein G is i,n The opening degree, m, of the ith seat damper at the nth moment is adjusted once; g i,max Single adjustment of the brake for the ith seatM, m.
10. The multi-objective predictive control algorithm for water conditioning engineering for directing gate regulation of claim 9, wherein: the solving multi-target prediction control model using the multi-target particle group intelligent optimization calculation comprises the following steps:
random population selection is carried out in the initial state, a decision variable which is a gate opening adjustment quantity and is initially and randomly generated by an optimization algorithm is transmitted to an integral time-lag model, calculation of a corresponding control target value under given weight is carried out through a state equation, and the target value is stored in a solution set;
updating the optimal solution set and the particle state in the optimization algorithm by comparing the control target values of different states;
and (4) transferring the updated decision variable to the integral time-lag model again, and repeating the steps at the same time until the optimal decision variable value meeting the control target is found.
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| CN117314062B (en) * | 2023-09-14 | 2024-04-12 | 长江信达软件技术（武汉）有限责任公司 | Multi-stage gate combined multi-target optimized water distribution scheduling method |
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