Solar energy collection system control method and device, electronic equipment and storage medium

Abstract

The present disclosure relates to a solar heat collection system control method and device, electronic equipment and storage medium, the method comprising: in response to the control duration being equal to the preset duration, determining the actual state quantity and actual parameter of the solar heat collection system at the current time, then processing the actual parameter and actual state quantity through a target prediction model to obtain a prediction sequence of a preset prediction step number, and obtaining a target control sequence according to the prediction sequence, a preset reference sequence and an interval control target function, wherein the target control sequence comprises a control quantity change value of a preset control step number, and the preset reference sequence comprises a plurality of controlled quantity intervals, and finally controlling the solar heat collection system according to the control quantity change value of the first control step number in the target control sequence. The set target of the controlled quantity in the solar heat collection system can be appropriately relaxed, so as to more quickly and flexibly control the outlet temperature of the heat conducting medium and the outlet temperature of the heat storage medium in the solar heat collection system.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of solar thermal system control, in particular to a solar thermal system control method and device, an electronic device and a storage medium. BACKGROUND

[0002] The solar thermal system itself is a large-inertia, nonlinear, multivariable system and is disturbed by multiple factors. In the related art, the target value of the controlled quantity of the solar thermal system is a single value, and it is difficult to achieve a satisfactory coordinated control effect. SUMMARY

[0003] The purpose of the present disclosure is to provide a solar thermal system control method, device, electronic device and storage medium, based on a target prediction model, according to a preset reference sequence containing multiple controlled quantity intervals and an interval control target function, the set target of the controlled quantity in the solar thermal system can be appropriately relaxed, so as to more quickly and flexibly control the outlet temperature of the heat conducting medium and the outlet temperature of the heat storage medium in the solar thermal system, and provide an effective means for the economic and stable operation of the solar thermal power station.

[0004] In order to achieve the above-mentioned purpose, according to a first aspect of the embodiments of the present disclosure, a solar thermal system control method is provided, comprising:

[0005] In response to the control duration being equal to the preset duration, determining the actual state quantity and the actual parameter of the solar thermal system at the current time, the actual parameter comprising the actual controlled quantity and the actual controlled quantity;

[0006] Processing the actual parameter and the actual state quantity through a target prediction model to obtain a prediction sequence of a preset prediction step number;

[0007] According to the prediction sequence, a preset reference sequence and an interval control target function, a target control sequence is obtained, the target control sequence comprising a control quantity change value of a preset control step number, the preset reference sequence comprising multiple controlled quantity intervals, each prediction step number corresponding to a controlled quantity interval;

[0008] According to the control quantity change value of the first control step number in the target control sequence, the solar thermal system is controlled.

[0009] Optionally, the target prediction model is obtained by the following steps:

[0010] Obtaining dynamic characteristic data of the solar thermal system, the dynamic characteristic data comprising multiple pairs of characteristic parameters, each pair of characteristic parameters comprising a control quantity parameter and a controlled quantity parameter;

[0011] According to the dynamic characteristic data, the target prediction model is established.

[0012] Optionally, the dynamic characteristic data of the solar energy collection system is obtained, including:

[0013] A mechanism model of the solar energy collection system is established;

[0014] According to the mechanism model, the corresponding controlled variables of the solar energy collection system under different control variables are collected to obtain the dynamic characteristic data, the control variables including the flow of the heat conduction medium and the flow of the heat storage medium, and the controlled variables including the outlet temperature of the heat conduction medium and the outlet temperature of the heat storage medium.

[0015] Optionally, the target prediction model is established according to the dynamic characteristic data, including:

[0016] According to the dynamic characteristic data, a linear time-invariant state space model representing the dynamic characteristics of the solar energy collection system is obtained;

[0017] According to a preset sampling period, the linear time-invariant state space model is discretized to obtain a discrete state space model;

[0018] The discrete state space model is disturbed and amplified, and a model is derived to obtain a target prediction model.

[0019] Optionally, the linear time-invariant state space model is represented as:

[0020]

[0021] Wherein, x c represents the state variable of the system, u=[m oil ,m salt ] T represents the control variable of the system, y=[T oil ,T salt ] T represents the controlled variable of the system; T oil represents the outlet temperature of the heat conduction medium; T salt represents the outlet temperature of the heat storage medium; m oil represents the mass flow of the heat conduction medium; m salt represents the mass flow of the heat storage medium; A c , B c , C c represent the corresponding coefficient matrix, and t is the time.

[0022] Optionally, the discrete state space model is represented as:

[0023]

[0024] Wherein, x d(k) represents the state quantity of the system at time k, u(k) represents the control quantity of the system at time k, and y(k) represents the controlled quantity of the system at time k.

[0025] Optionally, the target prediction model is represented as:

[0026] Y(k) = S x · x(k) + (S u · I P ) · U(k-1) + (S u · I M ) · ΔU(k)

[0027] wherein,

[0028]

[0029]

[0030]

[0031] wherein, The value of x(k) is estimated by a Kalman filter in each control period, d(k) is an augmented disturbance vector, T is a transpose, I represents a corresponding unit matrix, O represents a corresponding zero matrix, m is a dimension of the control quantity, I P has a dimension of (P x m) x m, I M has a dimension of (P x m) x (M x m), P is a preset prediction step number, M is a preset control step number, and M ≤ P.

[0032] According to a second aspect of the embodiments of the present disclosure, a solar heat collection system control device is provided, comprising:

[0033] A determination module is configured to determine an actual state quantity and an actual parameter of the solar heat collection system at a current time in response to a control duration being equal to a preset duration, wherein the actual parameter comprises an actual control quantity and an actual controlled quantity.

[0034] A first obtaining module is configured to process the actual parameter and the actual state quantity by a target prediction model to obtain a prediction sequence of a preset prediction step number.

[0035] A second obtaining module is configured to obtain a target control sequence according to the prediction sequence, a preset reference sequence and an interval control target function, wherein the target control sequence comprises a control quantity change value of a preset control step number, the preset reference sequence comprises a plurality of controlled quantity intervals, and each prediction step number corresponds to a controlled quantity interval.

[0036] The control module is configured to control the solar heat collection system according to a control amount change value of a first control step in the target control sequence.

[0037] Optionally, the device further comprises:

[0038] The acquisition module is configured to acquire dynamic characteristic data of the solar heat collection system, the dynamic characteristic data comprising a plurality of pairs of characteristic parameters, each pair of characteristic parameters comprising a control amount parameter and a controlled amount parameter.

[0039] The establishment module is configured to establish the target prediction model according to the dynamic characteristic data.

[0040] Optionally, the acquisition module comprises:

[0041] The establishment submodule is configured to establish a mechanism model of the solar heat collection system.

[0042] The first obtaining submodule is configured to acquire the controlled amount corresponding to the solar heat collection system under different control amounts according to the mechanism model, to obtain the dynamic characteristic data, the control amounts comprising flow rates of the heat-conducting medium and the heat storage medium, and the controlled amounts comprising outlet temperatures of the heat-conducting medium and the heat storage medium.

[0043] Optionally, the establishment module comprises:

[0044] The second obtaining submodule is configured to obtain a linear time-invariant state space model representing the dynamic characteristics of the solar heat collection system according to the dynamic characteristic data.

[0045] The third obtaining submodule is configured to discretize the linear time-invariant state space model according to a preset sampling period, to obtain a discrete state space model.

[0046] The fourth obtaining submodule is configured to perform perturbation augmentation and model derivation on the discrete state space model, to obtain a target prediction model.

[0047] According to a third aspect of an embodiment of the present disclosure, there is provided a non-transitory computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the steps of the solar heat collection system control method according to the first aspect of the present disclosure.

[0048] According to a fourth aspect of an embodiment of the present disclosure, there is provided an electronic device comprising:

[0049] A memory having a computer program stored thereon;

[0050] A processor configured to execute the computer program in the memory to implement the steps of the solar heat collection system control method according to the first aspect of the present disclosure.

[0051] By the above technical solution, in response to the control duration being equal to the preset duration, the actual state quantity and the actual parameter of the solar heat collection system at the current time are determined, the actual parameter includes the actual control quantity and the actual controlled quantity, the actual parameter and the actual state quantity are further processed by the target prediction model to obtain a prediction sequence of a preset prediction step, and the target control sequence is obtained according to the prediction sequence, a preset reference sequence and an interval control target function, the target control sequence includes a control quantity change value of a preset control step, the preset reference sequence includes a plurality of controlled quantity intervals, each prediction step corresponds to a controlled quantity interval, and finally the control quantity change value of the first control step in the target control sequence is used to control the solar heat collection system. By using the target prediction model, the preset reference sequence including a plurality of controlled quantity intervals and the interval control target function, the set target of the controlled quantity in the solar heat collection system can be appropriately relaxed, so as to more quickly and flexibly control the outlet temperature of the heat conducting medium and the outlet temperature of the heat storage medium in the solar heat collection system, and provide an effective means for economic and stable operation of the solar-thermal power station.

[0052] Other features and advantages of the present disclosure will be described in detail in the following detailed description section. BRIEF DESCRIPTION OF DRAWINGS

[0053] The accompanying drawings are included to provide a further understanding of the present disclosure and constitute a part of the specification, and are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation on the present disclosure. In the drawings:

[0054] Figure 1 is an application scenario schematic diagram of a solar heat collection system control method according to an exemplary embodiment.

[0055] Figure 2 is a flowchart of a solar heat collection system control method according to an exemplary embodiment.

[0056] Figure 3 is a flowchart of a method for establishing a target prediction model according to an exemplary embodiment.

[0057] Figure 4 is a flowchart of a method for obtaining dynamic characteristic data according to an exemplary embodiment.

[0058] Figure 5 is a flowchart of a method for obtaining a target prediction model according to an exemplary embodiment.

[0059] Figure 6 is a simulation result schematic diagram of a controlled quantity according to an exemplary embodiment.

[0060] Figure 7is a simulation result diagram of a control quantity according to an example embodiment.

[0061] Figure 8 is a block diagram of a solar heat collection system control device according to an example embodiment.

[0062] Figure 9 is a block diagram of an electronic device according to an example embodiment. DETAILED DESCRIPTION

[0063] The specific embodiments of the present disclosure are described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely intended to illustrate and explain the present disclosure, and are not intended to limit the present disclosure.

[0064] In a solar thermal power station, the outlet temperature of the heat-conducting working medium of the solar heat collection system directly affects the economy and safety of the overall system operation. However, the solar heat collection system itself is a large-inertia, nonlinear, multivariable system and is disturbed by multiple factors, and the mainstream PID control and other single-variable control means are difficult to achieve satisfactory coordinated control effect.

[0065] In view of the above technical problems, the embodiments of the present disclosure provide a solar heat collection system control method and device, an electronic device and a storage medium, based on a target prediction model, according to a preset reference sequence containing multiple controlled variable intervals and an interval control target function, the set target of the controlled variable in the solar heat collection system can be appropriately relaxed, so as to more quickly and flexibly control the outlet temperature of the heat-conducting medium and the outlet temperature of the heat storage medium in the solar heat collection system, and provide an effective means for the economic and stable operation of the solar thermal power station.

[0066] Figure 1 is an application scenario diagram of a solar heat collection system control method according to an example embodiment, as shown in Figure 1 The solar heat collection system can include an interval coordinated controller, a solar heat collector, a heat storage tank and a heat exchanger. The heat storage tank includes a hot tank and a cold tank, and the heat exchanger is connected to the solar heat collector and the hot tank and the cold tank in the heat storage tank through pipelines. The heat storage medium in the cold tank flows into the heat exchanger through a pipeline, exchanges heat with the heat-conducting medium flowing from the solar heat collector through a pipeline, and then flows into the hot tank. The heat-conducting medium after heat exchange in the heat exchanger reenters the solar heat collector to absorb heat energy. The pipeline can be provided with multiple control valves, and the interval coordinated controller is connected to the multiple control valves. The multiple control valves can be controlled by the interval coordinated controller, so as to control the flow of the heat-conducting medium and the flow of the heat storage medium, thereby achieving the purpose of controlling the outlet temperature of the heat-conducting medium and the outlet temperature of the heat storage medium.

[0067] Figure 2Fig. 1 is a flow chart of a solar heat collection system control method according to an exemplary embodiment, as shown in Fig. 1, the method can be applied to an interval coordination controller, and the method comprises the following steps: Figure 2

[0068] In step S201, in response to a control duration being equal to a preset duration, actual state quantity and actual parameters of the solar heat collection system at a current time are determined, the actual parameters comprising actual control quantity and actual controlled quantity.

[0069] In the present embodiment, the solar heat collection system can be regulated periodically, for example, the preset duration can be one period, and regulation of a corresponding period is performed every time the preset duration elapses, the preset duration can be 20 seconds, that is, when the control duration reaches 20 seconds, regulation is triggered, and the control duration is restarted. In response to the control duration being equal to the preset duration, regulation can be triggered, at which time actual state quantity and actual parameters of the solar heat collection system at the current time can be determined, wherein the actual state quantity of each control period can be estimated by a Kalman filter. For example, x(k) is the state quantity, the value of x(k) is estimated by the Kalman filter in each control period, x d (k) represents the state quantity of the system at time k, d(k) is an augmented disturbance vector, which has no actual physical meaning, and its function is to increase the integral action of the controller to achieve zero static error regulation of the controller, and T is a transpose. The actual parameters can include actual control quantity and actual controlled quantity, wherein the control quantity can include the flow rate of the heat conducting medium and the flow rate of the heat storage medium, and the controlled quantity can include the outlet temperature of the heat conducting medium and the outlet temperature of the heat storage medium. The heat conducting medium can be heat conducting oil, and the heat storage medium can be molten salt. The actual state quantity is the state quantity at the current time, the actual control quantity is the control quantity at the current time, and the actual controlled quantity is the controlled quantity at the current time.

[0070] Wherein, the actual state quantity can be estimated by the Kalman filter according to the controlled quantity and the control quantity of the previous period.

[0071] In step S202, the actual parameters and the actual state quantity are processed by a target prediction model to obtain a prediction sequence of a preset prediction step number.

[0072] In the present embodiment, the actual parameters and the actual state quantity can be input into the target prediction model to obtain a prediction sequence of a preset prediction step number. Wherein, the preset prediction step number can be P, which is a positive integer, for example, the value of P can be 5, that is, the prediction sequence can include prediction control parameters corresponding to the preset prediction step number, and each prediction step number corresponds to a group of prediction control parameters.

[0073] ​In step S203, a target control sequence is obtained according to the prediction sequence, the preset reference sequence and the interval control target function, the target control sequence including control quantity change values of the preset control steps, and the preset reference sequence including a plurality of controlled quantity intervals, each prediction step corresponding to a controlled quantity interval.

[0074] In the embodiment, the interval control target function can be set based on the target prediction model, and the target control sequence can be obtained by solving the interval control target function according to the prediction sequence and the preset reference sequence, the target control sequence including control quantity change values of the preset control steps, and the preset reference sequence including a plurality of controlled quantity intervals, each prediction step corresponding to a controlled quantity interval. Any controlled quantity interval can be determined by a lower limit of the controlled quantity and an upper limit of the controlled quantity. The preset reference sequence including a plurality of controlled quantity intervals and the interval control target function can be used to obtain the target control sequence more quickly and flexibly.

[0075] In step S204, the solar heat collection system is controlled according to the control quantity change value of the first control step in the target control sequence.

[0076] In the embodiment, the target control sequence including control quantity change values corresponding to a plurality of control steps can be obtained by the above method, the control quantity change value corresponding to the first control step can be determined as a control parameter corresponding to the current control period, the first control step being the first step in the control steps, and the control quantity of the solar heat collection system is controlled to make the controlled quantity after the control be in the controlled quantity interval corresponding to the first prediction step, so that the control target can be quickly and accurately reached, the first prediction step being the first step in the prediction steps, and the preset prediction step and the preset control step can have the same value. The preset control step can be M, and M is a positive integer, for example, the value of M can be 5. Specifically, the target control quantity can be determined according to the control quantity change value corresponding to the first control step, and the solar heat collection system is controlled according to the target control quantity, and the target control quantity can be obtained by adding the control quantity change value corresponding to the first control step to the control quantity of the migration period.

[0077] In the embodiment, based on the target prediction model, the preset reference sequence including a plurality of controlled quantity intervals and the interval control target function can be used to appropriately relax the set target of the controlled quantity in the solar heat collection system, so that the outlet temperature of the heat conducting medium and the outlet temperature of the heat storage medium in the solar heat collection system can be more quickly and flexibly controlled, and effective means can be provided for the economic and stable operation of the solar thermal power station.

[0078] Figure 3 is a flow chart of a method for establishing a target prediction model according to an exemplary embodiment, as shown in Figure 3As shown in a possible implementation, the target prediction model can be obtained by the following steps:

[0079] In step S301, dynamic characteristic data of the solar heat collection system is acquired, the dynamic characteristic data including a plurality of pairs of characteristic parameters, each pair of characteristic parameters including a control quantity parameter and a controlled quantity parameter.

[0080] In the embodiment, the dynamic characteristic data of the solar heat collection system is acquired, the dynamic characteristic data being the controlled quantity parameters corresponding to different control quantity parameters. The dynamic characteristic data includes a plurality of pairs of characteristic parameters, each pair of characteristic parameters including a control quantity parameter and a corresponding controlled quantity parameter.

[0081] In step S302, a target prediction model is established according to the dynamic characteristic data.

[0082] In the embodiment, the target prediction model is established according to the dynamic characteristic data, i.e. the controlled quantity parameters corresponding to different control quantity parameters. The target prediction model can also be established according to the change amount of the controlled quantity parameters corresponding to the change amount of different control quantity parameters, and according to the change amount of the controlled quantity parameters corresponding to the change amount of different control quantity parameters. The target prediction model can obtain a prediction sequence for a preset prediction step number of the solar heat collection system according to actual parameters and actual state quantities of the solar heat collection system.

[0083] Figure 4 is a flow chart of a method for acquiring dynamic characteristic data according to an example embodiment, as shown in Figure 4 As shown in a possible implementation, the method for acquiring dynamic characteristic data of the solar heat collection system can include the following steps:

[0084] In step S401, a mechanism model of the solar heat collection system is established.

[0085] In the embodiment, the mechanism model of the solar heat collection system can be established according to parameters such as the structure and principle of the solar heat collection system, and the mechanism model can be used for simulation experiments on the solar heat collection system.

[0086] In step S402, the controlled quantity corresponding to different control quantities of the solar heat collection system is collected according to the mechanism model to obtain dynamic characteristic data, the control quantities including the flow of the heat transfer medium and the flow of the heat storage medium, and the controlled quantities including the outlet temperature of the heat transfer medium and the outlet temperature of the heat storage medium.

[0087] In the embodiment, the controlled variable corresponding to different control variables of the solar heat collection system can be collected according to the mechanism model, and the controlled variable is the controlled variable after the system is stabilized, so that the dynamic characteristic data is obtained. The dynamic characteristic data can be obtained by open-loop step test on the mechanism model, that is, by changing the control variable when the control variable and the controlled variable of the solar heat collection system are stable, and obtaining the controlled variable corresponding to the adjusted control variable when the controlled variable of the solar heat collection system is stable again. By changing the control variable multiple times, different control variables and their corresponding controlled variables can be obtained, so that the dynamic characteristic data is obtained. The control variable includes the flow rate of the heat conducting medium and the flow rate of the heat storage medium, and the controlled variable includes the outlet temperature of the heat conducting medium and the outlet temperature of the heat storage medium. For the solar heat collection system, the heat conducting medium can be heat conducting oil, the heat storage medium can be molten salt, the control variable can include the flow rate of the heat conducting oil and the flow rate of the molten salt, and the controlled variable can include the outlet temperature of the heat conducting oil and the outlet temperature of the molten salt.

[0088] Figure 5 is a flow chart of a method for obtaining a target prediction model according to an exemplary embodiment, as shown in Figure 5 In a possible implementation, the target prediction model can be established according to the dynamic characteristic data, which can include the following steps:

[0089] In step S501, a linear time-invariant state space model representing the dynamic characteristics of the solar heat collection system is obtained according to the dynamic characteristic data.

[0090] In the embodiment, the linear time-invariant state space model representing the dynamic characteristics of the solar heat collection system can be identified according to the dynamic characteristic data by using the sub-control method, and the linear time-invariant state space model can be represented as:

[0091]

[0092] wherein x c represents the state variable of the system, which has no specific physical meaning, u = [m oil ,m salt ] T represents the control variable of the system, y = [T oil ,T salt ] T represents the controlled variable of the system; T oil represents the outlet temperature of the heat conducting medium; T salt represents the outlet temperature of the heat storage medium; m oil represents the mass flow rate of the heat conducting medium; m salt represents the mass flow rate of the heat storage medium; A c , B c , C cLet represent the corresponding coefficient matrix, where t is the time step.

[0093] In step S502, the linear time-invariant state-space model is discretized according to the preset sampling period to obtain a discrete state-space model.

[0094] In this embodiment, the linear time-invariant state-space model can be discretized according to a preset sampling period, i.e., the model discrete sampling period Δt, to obtain a discrete state-space model. The discrete state-space model can be represented as:

[0095]

[0096] Where, x d (k) represents the state variable of the system at time k, u(k) represents the control variable of the system at time k, and y(k) represents the controlled variable of the system at time k.

[0097] In step S503, the discrete state-space model is perturbed and a model is derived to obtain the target prediction model.

[0098] In this embodiment, to ensure system control quality, model mismatch, unknown internal and external disturbances, etc. are used as total disturbances to amplify the state variables, thereby perturbing and amplifying the discrete state space model, transforming the discrete state space model into a perturbed amplified discrete state space model, and then deriving the target prediction model from the perturbed amplified discrete state space model.

[0099] Specifically, the perturbation-amplified discrete state-space model can be expressed as:

[0100]

[0101] in, The value of x(k) is estimated by a Kalman filter in each control cycle. d(k) is the amplified disturbance vector, which has no actual physical meaning. Its function is to increase the integral action of the controller in order to achieve zero steady-state error regulation of the controller. I represents the corresponding identity matrix and O represents the corresponding zero matrix.

[0102] Define a preset prediction step number P and a preset control step number M, where M ≤ P; the perturbation-amplified discrete state-space model is recursively applied for P steps to obtain the prediction model of the system in the next P steps. The prediction model is then expressed as:

[0103] Y(k)=S x ·x(k)+S u ·U(k)

[0104]

[0105]

[0106] To improve the real-time calculation efficiency of the controller, it is assumed that the control quantity remains unchanged within a preset prediction step number P and outside a preset control step number M, and the prediction sequence of the control quantity in the prediction model can be further decomposed as:

[0107]

[0108] where m is the dimension of the control quantity; I P has a dimension of (P x m) x m; I M has a dimension of (P x m) x (M x m).

[0109] Thus, the target prediction model can be obtained as:

[0110] Y(k) = S x ·x(k) + (S u ·I P )·U(k-1) + (S u ·I M )·ΔU(k)

[0111] For the interval control objective function, an interval control objective function of a finite time domain optimization problem can be defined, and an interval coordination controller corresponding to the solar heat collection system can be designed in combination with the target prediction model, so that the coordination tracking control of the solar heat collection system can be realized through the interval coordination controller.

[0112] Specifically, the interval control objective function can be expressed as:

[0113]

[0114]

[0115] where y slack (i) is a slack variable, and the interval control can be realized by adjusting the lower limit y min and the upper limit y max of the controlled quantity; Q represents an output weight matrix; R represents a control weight matrix; u min represents a lower limit of the control quantity, u max represents an upper limit of the control quantity; Δu min represents a lower limit of the control increment; and Δu max represents an upper limit of the control increment.

[0116] The specific implementation process of the interval coordination controller to realize the coordinated control of the solar heat collection system is as follows: at each sampling time k, the interval coordination controller estimates the current system state x(k) through the Kalman filter; then, the future P-step output prediction sequence is obtained according to the target prediction model; then, the given finite time domain optimization problem is solved to determine the optimal control sequence ΔU(k); finally, the first group of elements of the optimal control sequence is implemented on the solar heat collection system to adjust the outlet temperatures of the heat conducting medium and the heat storage medium of the solar heat collection system.

[0117] The following takes the solar heat collection system as an example for calculation, and the specific process is as follows:

[0118] Firstly, based on the established mechanism model of the solar heat collection system, an open-loop step experiment is performed to obtain the dynamic characteristic experimental data, i.e., the dynamic characteristic data. Then, according to the dynamic characteristic experimental data, a linear time-invariant state space model representing the dynamic characteristics of the system is obtained by using the subspace identification method. Then, the linear time-invariant state space model is discretized with a given model sampling period Δt to obtain a discrete state space model. On this basis, the unknown total disturbance is expanded into the state quantity, and then a disturbance expanded discrete state space model is obtained. Define the preset prediction step number P and the preset control step number M, and have M≤P. Assume that the control increment remains unchanged within the preset prediction step number P and outside the preset control step number M, recursively predict the disturbance expanded discrete state space model for P steps to obtain the target prediction model of the solar heat collection system. Finally, define the interval control objective function of the finite time domain optimization problem, repeatedly optimize and solve the finite time domain optimization problem according to the specific implementation process, and control the solar heat collection system.

[0119] In order to verify the superiority of the solar heat collection system control method proposed in this embodiment, i.e., the zone model predictive control strategy (ZMPC), it is compared with the conventional model predictive control strategy (CMPC) and the conventional PI control strategy (PID). In the conventional PI control strategy, the parameters of each PI controller are obtained by the MATLAB Tuner toolbox, as shown in Table 1. In the two model predictive control strategies, the basic parameters of the MPC controller are consistent, as shown in Table 2.

[0120] Table 1 - Controller parameters of the conventional PI control strategy

[0121]

[0122] Table 2 - Controller parameters in the three MPC control strategies

[0123]

[0124] At the beginning of the simulation, the system runs in steady state. At this time, the outlet temperature set value of the heat transfer oil and the outlet temperature set value of the molten salt in the CMPC strategy and the PID strategy are 389 DEG C and 380 DEG C respectively; the outlet temperature set value of the heat transfer oil in the ZMPC strategy remains 389 DEG C, and the outlet temperature set value of the molten salt is expanded to a set interval, which is 379 DEG C to 381 DEG C.

[0125] The simulation conditions are as follows: at 500s, the outlet temperature set value of the heat transfer oil is stepped up from 389 DEG C to 394 DEG C due to the power generation demand of the solar-thermal power station; at 2500s, the outlet temperature of the heat transfer oil fluctuates due to the change of solar radiation; at 4500s, the outlet temperature of the molten salt fluctuates due to environmental factors. The simulation results are shown in Figure 6 and Figure 7 . Figure 6 is a simulation result diagram of a controlled variable according to an example embodiment. Figure 7 is a simulation result diagram of a control variable according to an example embodiment.

[0126] As shown in Figure 6 , the results show that, due to the lack of prediction of the dynamic characteristics of the system in the conventional PI control strategy, the interaction between different process channels of the system cannot be fully considered, and the control effect is obviously inferior to the interval model predictive control strategy, and the outlet temperature of the heat transfer oil is difficult to track the set value in time; unlike the conventional MPC strategy, the ZMPC control strategy provides an additional degree of freedom by appropriately relaxing the control target of the outlet temperature of the molten salt (T salt ), so that the outlet temperature of the heat transfer oil (T oil ) can track the corresponding set value more quickly and stably, and the overshoot is small during the adjustment process, and the outlet temperature of the molten salt can also be maintained within the set interval temperature range. Especially when the system is disturbed by different disturbances, the outlet temperature of the heat transfer oil can be more strictly maintained near the set value. Therefore, the interval predictive control method for the solar heat collection system of the solar-thermal power generation proposed in the embodiment of the present disclosure has more superior and flexible coordinated control performance.

[0127] Figure 8 is a block diagram of a solar heat collection system control device according to an example embodiment. Referring to Figure 8 , the solar heat collection system control device 800 can be an interval coordinated controller, and the solar heat collection system control device 800 can include a determination module 801, a first obtaining module 802, a second obtaining module 803, and a control module 804.

[0128] The determining module 801 is configured to determine actual state quantity and actual parameter of the solar heat collection system at the current time in response to the control duration being equal to the preset duration, the actual parameter including actual control quantity and actual controlled quantity.

[0129] The first obtaining module 802 is configured to process the actual parameter and the actual state quantity through a target prediction model to obtain a prediction sequence of a preset prediction step number.

[0130] The second obtaining module 803 is configured to obtain a target control sequence according to the prediction sequence, a preset reference sequence and an interval control target function, the target control sequence including control quantity change values of a preset control step number, the preset reference sequence including a plurality of controlled quantity intervals, each prediction step number corresponding to a controlled quantity interval.

[0131] The control module 804 is configured to control the solar heat collection system according to the control quantity change value of the first control step number in the target control sequence.

[0132] Optionally, the solar heat collection system control device 800 further includes:

[0133] The obtaining module is configured to obtain dynamic characteristic data of a solar heat collection system, the dynamic characteristic data including a plurality of pairs of characteristic parameters, each pair of characteristic parameters including a control quantity parameter and a controlled quantity parameter.

[0134] The establishing module is configured to establish the target prediction model according to the dynamic characteristic data.

[0135] Optionally, the obtaining module includes:

[0136] The establishing submodule is configured to establish a mechanism model of the solar heat collection system.

[0137] The first obtaining submodule is configured to collect the controlled quantity corresponding to different control quantities of the solar heat collection system according to the mechanism model to obtain the dynamic characteristic data, the control quantity including flow rate of a heat conduction medium and flow rate of a heat storage medium, and the controlled quantity including outlet temperature of the heat conduction medium and outlet temperature of the heat storage medium.

[0138] Optionally, the establishing module includes:

[0139] The second obtaining submodule is configured to obtain a linear time-invariant state space model representing dynamic characteristics of the solar heat collection system according to the dynamic characteristic data.

[0140] The third obtaining submodule is configured to discretize the linear time-invariant state space model according to a preset sampling period to obtain a discrete state space model.

[0141] The fourth obtaining sub-module is configured to disturb and amplify the discrete state space model and derive a target prediction model.

[0142] As to the apparatus in the above-mentioned embodiments, the specific manners in which the respective modules perform operations have been described in detail in the embodiments of the method, and thus will not be described in detail here.

[0143] Figure 9 is a block diagram of an electronic device according to an exemplary embodiment. For example, the electronic device 900 can be provided as a zone coordination controller. Referring to Figure 9 , the electronic device 900 includes a processor 922, the number of which can be one or more, and a memory 932 for storing a computer program executable by the processor 922. The computer program stored in the memory 932 can include one or more than one module each corresponding to a set of instructions. In addition, the processor 922 can be configured to execute the computer program to perform the solar heat collection system control method described above.

[0144] In addition, the electronic device 900 can further include a power supply component 926, which can be configured to perform power management of the electronic device 900, and a communication component 950, which can be configured to implement communication of the electronic device 900, such as wired or wireless communication. In addition, the electronic device 900 can further include an input / output (I / O) interface 958. The electronic device 900 can operate based on an operating system stored in the memory 932.

[0145] In another exemplary embodiment, a computer readable storage medium including program instructions is also provided, which, when executed by a processor, implements the steps of the solar heat collection system control method described above. For example, the non-transitory computer readable storage medium can be the memory 932 described above including program instructions, and the program instructions described above can be executed by the processor 922 of the electronic device 900 to complete the solar heat collection system control method described above.

[0146] In another exemplary embodiment, a computer program product is also provided, which contains a computer program executable by a programmable device, the computer program having code portions for performing the solar heat collection system control method described above when executed by the programmable device.

[0147] The preferred embodiments of the present disclosure are described in detail above with reference to the accompanying drawings, but the present disclosure is not limited to the specific details in the above-described embodiments. Within the technical concept range of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all belong to the protection range of the present disclosure.

[0148] It should also be noted that any technically possible combination of the various technical features described in the above embodiments can be made, without contradiction. In order to avoid unnecessary repetition, the disclosure does not describe each possible combination of the various technical features.

[0149] Furthermore, any combination of the various different embodiments of the disclosure can also be made, as long as it does not contradict the idea of the disclosure, it should also be considered as disclosed by the disclosure.

Claims

1. A control method for a solar thermal collector system, characterized in that, include: In response to the control duration being equal to the preset duration, the actual state quantities and actual parameters of the solar thermal collector system at the current moment are determined, and the actual parameters include the actual control quantity and the actual controlled quantity. The actual parameters and actual state quantities are processed by the target prediction model to obtain a prediction sequence with a preset number of prediction steps; Based on the predicted sequence, the preset reference sequence, and the interval control objective function, a target control sequence is obtained. The target control sequence includes the control quantity change value of a preset number of control steps. The preset reference sequence includes multiple controlled quantity intervals, with each predicted step corresponding to a controlled quantity interval. The solar thermal system is controlled according to the change value of the control quantity in the first control step of the target control sequence; The step of obtaining the target control sequence based on the predicted sequence, the preset reference sequence, and the interval control objective function includes: setting an interval control objective function for a finite-time-domain optimization problem based on the target prediction model, and solving the interval control objective function based on the predicted sequence and the preset reference sequence to obtain the target control sequence.

2. The solar thermal collector system control method according to claim 1, characterized in that, The target prediction model is obtained through the following steps: Acquire dynamic characteristic data of a solar thermal collector system. The dynamic characteristic data includes multiple pairs of characteristic parameters, and each pair of characteristic parameters includes a control parameter and a controlled parameter. Based on the dynamic feature data, the target prediction model is established.

3. The solar thermal collector system control method according to claim 2, characterized in that, The acquisition of dynamic characteristic data of the solar thermal collector system includes: Establish a mechanistic model of the solar thermal collector system; Based on the aforementioned mechanism model, the controlled variables corresponding to different control quantities of the solar thermal collector system are collected to obtain the dynamic characteristic data. The control quantities include the flow rate of the heat transfer medium and the flow rate of the heat storage medium, and the controlled variables include the outlet temperature of the heat transfer medium and the outlet temperature of the heat storage medium.

4. The solar thermal collector system control method according to claim 2, characterized in that, The step of establishing the target prediction model based on the dynamic feature data includes: Based on the dynamic characteristic data, a linear time-invariant state-space model characterizing the dynamic characteristics of the solar thermal collector system is obtained; According to the preset sampling period, the linear time-invariant state-space model is discretized to obtain a discrete state-space model; The discrete state-space model is perturbed and amplified, and the model is derived to obtain the target prediction model.

5. The solar thermal collector system control method according to claim 4, characterized in that, The linear time-invariant state-space model is expressed as follows: in, Represents the state variables of the system. This represents the control variables of the system. Represents the controlled variable of the system; Indicates the outlet temperature of the heat transfer medium; Indicates the outlet temperature of the heat storage medium; Indicates the mass flow rate of the heat transfer medium; Indicates the mass flow rate of the heat storage medium; , , Let represent the corresponding coefficient matrix, where t is the time step.

6. The solar thermal collector system control method according to claim 5, characterized in that, The discrete state-space model is represented as follows: in, express The state variables of the system at any given time. express Control variables of the time system express The controlled variable of the time system.

7. The solar thermal collector system control method according to claim 6, characterized in that, The target prediction model is expressed as follows: in, in, , The value is estimated by a Kalman filter in each control cycle. The vector to be amplified is T, which is the transpose. Represents the corresponding identity matrix. Represents the corresponding zero matrix. To control the dimension of the quantity, The dimension is , The dimension is , To preset the number of prediction steps, The preset control number of steps, and has .

8. A control device for a solar thermal collector system, characterized in that, include: The determination module is configured to determine the actual state quantities and actual parameters of the solar thermal collector system at the current moment in response to a control duration equal to a preset duration. The actual parameters include actual control quantities and actual controlled quantities. The first acquisition module is configured to process the actual parameters and the actual state variables through a target prediction model to obtain a prediction sequence with a preset number of prediction steps. The second obtaining module is configured to obtain a target control sequence based on the predicted sequence, the preset reference sequence and the interval control objective function. The target control sequence includes control quantity change values ​​for a preset number of control steps. The preset reference sequence includes multiple controlled quantity intervals, with each predicted step corresponding to a controlled quantity interval. The control module is configured to control the solar thermal system based on the change value of the control quantity in the first control step of the target control sequence; The second obtaining module is configured to set an interval control objective function for a finite-time-domain optimization problem based on the target prediction model, and solve the interval control objective function according to the prediction sequence and a preset reference sequence to obtain the target control sequence.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the solar thermal collector system control method according to any one of claims 1-7.

10. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the solar thermal collector system control method according to any one of claims 1-7.

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