Wind heating unit constraint optimal model prediction anti-interference control method, system and equipment and medium

Abstract

The invention discloses a wind heating unit constraint optimal model prediction anti-interference control method, system and device and a medium, and belongs to the technical field of automatic control of the thermal process, and the method comprises the steps: estimating an observation value of lumped disturbance of a wind heating unit in real time; predicting the future dynamic state of the lumped disturbance in the prediction time domain by using a disturbance predictor; the lumped disturbance observation value and future dynamic are fused into a wind-heat unit state space model to solve a steady-state target sequence, and an infinite prediction time domain constraint optimization problem based on the steady-state target sequence and the wind-heat unit state is constructed; constructing a maximum controlled allowable set with disturbance estimation compensation; the infinite prediction time domain constraint optimization problem is converted into an equivalent quadratic programming type constraint optimization problem to be solved, and an anti-interference control law of the wind heating unit is obtained. According to the method, the dual control targets of variable constraint and disturbance suppression are considered, and meanwhile, the anti-interference performance of the wind heating unit can be improved by effectively utilizing the feed-forward signal of lumped disturbance within a period of time in the future.

Description

Technical Field

[0001] The present invention belongs to the technical field of automatic control of thermal processes, and in particular relates to a method, system, equipment and medium for predicting anti-interference control of a wind-heat unit with a constrained optimal model. Background Art

[0002] The wind-heat unit is an efficient and environmentally friendly heating technology that uses wind energy to directly drive the heat pump system for heating. Above the rated wind speed range, the wind turbine output power can be maintained at the rated value by adjusting the wind turbine pitch angle. The control system has multi-source unmeasurable lumped disturbances, including external disturbances (wind speed changes) and internal disturbances (such as parameter perturbations). Most of these disturbances are unmeasurable or difficult to measure accurately. In addition, in order to ensure the safety of the system, there are certain constraints on the changes in the pitch angle and its increment. The simultaneous existence of constraints and disturbances greatly increases the difficulty of control.

[0003] Meeting constraints and suppressing disturbances are conflicting control objectives: in order to suppress disturbances, the change in pitch angle needs to be increased, while constraints limit the change in pitch angle to be small. At present, PID control method is usually used to adjust the pitch angle. PID indirectly makes the pitch angle meet constraints through parameter setting, and compensates for the influence of disturbances by eliminating deviations using output deviations. On the one hand, PID controller cannot directly handle variable constraints. On the other hand, eliminating deviations using deviations is a post-adjustment. When dealing with strong disturbances such as wind speed, the adjustment effect is always untimely. Therefore, PID cannot effectively take into account the dual control objectives of variable constraints and anti-interference. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method, system, equipment and medium for predicting anti-interference control of a wind-thermal unit with a constrained optimal model, which can take into account the dual control objectives of variable constraint and disturbance suppression, and can effectively utilize the feedforward signal of the aggregated disturbance within a period of time in the future to improve the anti-interference performance of the wind-thermal unit.

[0005] The present invention provides the following technical solutions:

[0006] In a first aspect, a method for predicting anti-interference control of a wind-heat unit with a constrained optimal model is provided, comprising:

[0007] The established extended state observer is used to estimate the observed value of the lumped disturbance of the wind-heat unit in real time

[0008] According to the observed value of the lumped disturbance The future dynamics of the lumped disturbance in the prediction time domain are predicted using the established disturbance predictor

[0009]

[0010] The observed value of the lumped disturbance and future developments Fusion into the state space model of the wind-heat unit to perform steady-state target sequence (x ss|k+i ,u ss|k+i ) and construct an infinite prediction time-domain constrained optimization problem based on the steady-state target sequence and the state of the wind-heat unit;

[0011] Combining the steady-state target sequence and the bimodal control strategy, the maximum controlled admissible set with disturbance prediction compensation is constructed.

[0012] Based on the maximum controlled admissible set and steady-state target sequence, the infinite prediction time domain constrained optimization problem is transformed into an equivalent quadratic programming constrained optimization problem and solved to obtain the anti-interference control law of the wind-thermal unit.

[0013] Optionally, the expression of the extended state observer is:

[0014]

[0015] in, and are the derivatives of z1 and z2 respectively; z1 and z2 are the estimated values ​​of wind turbine output power y and lumped disturbance d respectively, u is the pitch angle of the wind turbine, A m and B m are all model parameters of the state space model of the wind thermal unit; L = [β 01 ,β 02 ] T is the gain of the extended state observer.

[0016] Optionally, the observation value according to the lumped disturbance The established disturbance predictor predicts the future dynamics of the lumped disturbance in the prediction time domain Specifically:

[0017] According to the current time k and the aggregate disturbance sequence observed in the past, update the training sample D = (d in ,d out );

[0018]

[0019] Where N is the sample size, n p is the dimension of the input variables in the training sample;

[0020] Using the updated training samples, fit the least squares parameters b and α of the disturbance predictor i ;

[0021]

[0022] Where E = [1,1,…,1] T , H is the kernel function matrix;

[0023] Will As the test sample input, the disturbance predictor predicts the predicted value of the lumped disturbance at the next time k+1 for:

[0024]

[0025] Among them, K a is the kernel function;

[0026] The predicted value based on the aggregate disturbance at time k+1 is continuously updated Until the future dynamics of the lumped disturbance in the prediction time domain are obtained

[0027]

[0028] Among them, n a is the predicted length.

[0029] Optionally, the observed value of the lumped disturbance and future developments Fusion into the state space model of the wind-heat unit to perform steady-state target sequence (x ss|k+i ,u ss|k+i ) and construct an infinite prediction time-domain constrained optimization problem based on the steady-state target sequence and the state of the wind-heat unit. The specific process is as follows:

[0030] At the current time k, the aggregated disturbance observation value And future developments Fusion to contain the steady-state target sequence (x ss|k+i ,u ss|k+i ) in the state space model of the wind-heat unit;

[0031]

[0032] where x ss|k+i and u ss|k+i are the steady-state target values ​​of the state variables and the control variables at time k+i when considering disturbances; is the predicted value of the aggregate disturbance at time k+i, A, B, B d , C are the model parameters of the discretized state space model of the wind thermal unit; y a|k+i It is the steady-state output that the system can achieve under the constraints of the control variables and the steady-state target sequence; the constraints of the control variables are:

[0033]

[0034] Among them, Δu and are the lower and upper limits of the variable pitch angle increment, u and are the lower and upper limits of the variable pitch angle, u k+i is the control variable at time k+i, Δu k+i is the increment of the control variable at time k+i;

[0035] By minimizing the steady-state output y that the system can achieve a|k+i With the set value (y r ,u r ) to dynamically solve the steady-state target sequence (x ss|k+i ,u ss|k+i );

[0036]

[0037] in, and are the steady-state target sequences of state variables and input variables, respectively, Q a and R a They are two weight matrices respectively;

[0038] Construct an infinite prediction time domain constrained optimization problem based on the steady-state target sequence and the state of the wind-heat unit;

[0039]

[0040] in, x k+i+1 is the state variable at time k+i+1, u k+i is the control variable at time k+i, Q and R are and The weight matrix of .

[0041] Optionally, the combination of the steady-state target sequence and the dual-mode control strategy to construct a maximum controlled admissible set with disturbance prediction compensation is specifically performed as follows:

[0042] The infinite prediction time domain is divided into two modes: transient regulation area and gradual convergence area, and a dual-mode control law is adopted;

[0043]

[0044] Where K is the feedback gain, u k+i is the control variable of the wind-heating unit at time k+i, c k+i is the control degree of freedom at time k+i, n c is the step length of the transient regulation area, x ss|k+i and u ss|k+i are the steady-state target values ​​of the state variables and the control variables at time k+i when considering disturbances;

[0045] Based on the bimodal control law, the deviation between the state variable of the wind-heat unit and the steady-state target value at time k+1 is obtained;

[0046]

[0047] Defining variables Control model of wind-heat unit with respect to variable s k The autonomous expression of is:

[0048]

[0049] in, is the control degree of freedom sequence starting at time k in the prediction time domain, is the steady-state target sequence of state variables; A and B are the model parameters of the wind-heat unit state space model after discretization; Ω and Φ are parameters used to simplify the formula;

[0050] Transform the constraints on the control variables into constraints on the autonomous variables s k Expression of

[0051]

[0052] in,

[0053] According to the transformation into the autonomous variable s k The constraint expression of is constructed to construct the maximum controlled admissible set with disturbance prediction compensation.

[0054] Optionally, the basis is converted into an autonomous variable s k The constraint expression of the maximum controlled admissible set with disturbance estimation compensation is constructed. The specific process is:

[0055] Step a: define n = 0,

[0056] Step b: Solve the linear programming problem:

[0057]

[0058] Step c: Determine the s obtained in step b k Whether the constraints are met If not, then n=n+1, and return to step b; if so, n is large enough, the algorithm stops, and the maximum controlled admissible set is F·s k ≤t, transform the maximum controlled admissible set into the control degrees of freedom In the form of:

[0059]

[0060] in, N s 、M s and V s They are and The corresponding weight.

[0061] Optionally, based on the maximum controlled admissible set and the steady-state target sequence, the infinite prediction time domain constraint optimization problem is converted into an equivalent quadratic programming type constraint optimization problem for solution to obtain the anti-interference control law of the wind-heat unit, specifically:

[0062] By combining the state model of the wind-heat unit and the maximum controlled admissible set with disturbance estimation compensation, the infinite prediction time domain constrained optimization problem is transformed into an equivalent quadratic programming type constrained optimization problem:

[0063]

[0064] in, is the control degree of freedom sequence at time k, S cs , S xs , S ss and S us They are x k , and The corresponding weight, x k is the state variable at time k,

[0065] The equivalent quadratic programming constrained optimization problem is solved using a quadratic programming solver to obtain the optimal control degree of freedom sequence.

[0066] The first element c of the optimal control degree of freedom sequence is k Combine the dual-mode control law to obtain the anti-interference control law u at the current time k k ;

[0067] u k =-K(x k -x ss|k )+u ss|k-1 +c k

[0068] Among them, x k is the state variable at the current time k, K is the feedback gain, x ss|k is the steady-state target value of the state variable at the current time k, u ss|k-1 is the steady-state target value of the control variable at time k-1.

[0069] In a second aspect, a wind-heat unit constraint optimal model prediction anti-interference control system is provided, comprising:

[0070] The lumped disturbance observation module is used to estimate the observed value of the lumped disturbance of the wind-heat unit in real time using the established extended state observer

[0071] The lumped disturbance prediction module is used to predict the observed value of the lumped disturbance The future dynamics of the lumped disturbance in the prediction time domain are predicted using the established disturbance predictor

[0072] Constrained optimization problem building block for transforming the observed values ​​of the lumped perturbation and future developments Fusion into the state space model of the wind-heat unit to perform steady-state target sequence (x ss|k+i ,u ss|k+i ) and construct an infinite prediction time-domain constrained optimization problem based on the steady-state target sequence and the state of the wind-heat unit;

[0073] The maximum controlled admissible set construction module is used to combine the steady-state target sequence and the bimodal control strategy to construct the maximum controlled admissible set with disturbance estimation compensation;

[0074] The anti-interference control law acquisition module is used to transform the infinite prediction time domain constraint optimization problem into an equivalent quadratic programming type constraint optimization problem based on the maximum controlled allowable set and the steady-state target sequence to obtain the anti-interference control law of the wind-heat unit.

[0075] In a third aspect, a computer device is provided, comprising a processor and a memory; wherein, when the processor executes the computer program stored in the memory, the steps of the wind-thermal unit constraint optimal model prediction anti-interference control method described in any one of the first aspects are implemented.

[0076] In a fourth aspect, a computer-readable storage medium is provided for storing a computer program; when the computer program is executed by a processor, the steps of the wind-thermal unit constraint optimal model prediction anti-interference control method described in any one of the first aspects are implemented.

[0077] Compared with the prior art, the present invention has the following beneficial effects:

[0078] The present invention provides a method, system, device and medium for constrained optimal model prediction anti-interference control of a wind-thermal unit. Firstly, an extended state observer and a disturbance predictor are designed based on the invariance principle and the least squares kernel regression idea respectively, so as to obtain the lumped disturbance prediction dynamics online, and organically integrate the disturbance prediction dynamics into the model predictive control framework through the dynamic solution of the steady-state target sequence; then, the equivalent conversion of infinite constraint inequalities to finite number inequalities is realized through the derivation of the autonomous form of the control object, the conversion of the original constraints and the construction of the maximum controlled admissible set; finally, a dual-mode control strategy and quadratic programming are combined to solve the optimal control action at the current moment. The present invention can not only take into account the two-dimensional control objectives of variable constraints and disturbance suppression, but also increase the system feedforward information through the organic introduction of the future dynamics of the lumped disturbance, so that the control action can be more timely, thereby improving the anti-interference ability of the controller. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is a structural diagram of the wind-heat unit constraint optimal model prediction anti-interference control method of the present invention;

[0080] Figure 2 This is a wind turbine output power control effect diagram provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0081] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the scope of protection of the present invention. It should be noted that the term "comprising" and any variation thereof in the specification and claims of the present invention are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0082] Example 1

[0083] like Figure 1 As shown, a method for predicting anti-interference control of a wind-heat unit with a constrained optimal model includes the following steps:

[0084] Step S1: Use the established extended state observer to estimate the observed value of the lumped disturbance of the wind-heat unit in real time

[0085] Specifically, an extended state observer is designed based on the invariance principle, which can estimate the lumped disturbance of the wind-thermal unit in real time.

[0086] Step S11: discretize the state space model (1) of the wind thermal unit into equation (2),

[0087]

[0088] Where y, u, d are wind turbine output power, pitch angle and lumped disturbance. In this embodiment, A m and B m Obtained from step response experiment, B dm =1.

[0089]

[0090] In the formula, A = e Ts , x k ,u k and k represent the discretized values ​​of x, u and y respectively. In this embodiment, the sampling time T s =1s.

[0091] Step S12: Establish an expanded form of the state space model (1)

[0092]

[0093] Step S13: Design the corresponding extended state observer (ESO) according to the expansion form (3):

[0094]

[0095] Where z1 and z2 are the estimated values ​​of y and d respectively, L = [β 01 ,β 02 ] T is the gain of the ESO. In this embodiment, β 01 =100,β 02 =2500.

[0096] Step S14: Use the designed ESO (4) to calculate the aggregate interference d k Make an estimate and obtain the observed value at time k

[0097] Step S2: Based on the observed value of the lumped disturbance The future dynamics of the lumped disturbance in the prediction time domain are predicted using the established disturbance predictor

[0098] Specifically, a disturbance predictor is established based on the least squares kernel regression idea, which can predict the future dynamics of the lumped disturbance online.

[0099] Step S21: Update the training sample D = (d in ,d out );

[0100] Specifically, step S211: construct training sample D = (d in ,d out ).

[0101]

[0102] In this embodiment, N is the sample size, n p is the dimension of the input variables in the training sample; D is initialized to N rows n p As an alternative, N = 45, n p =4.

[0103] Step S212: At the current time k, use the perturbed observation value Update the support vector (d in,up ,d out,up ):

[0104]

[0105] Step S213: Update training samples: Delete the first row of D and update the support vector (d in,up ,d out,up ) is added to the last line of D, that is:

[0106]

[0107] Step S22: Using the updated training samples, fit the least squares parameters b and α of the disturbance predictor i ;

[0108]

[0109] Where E = [1,1,…,1] T , H is the kernel function matrix;

[0110] Step S23: As the test sample input, the disturbance predictor predicts the predicted value of the lumped disturbance at the next time k+1 for:

[0111]

[0112] Among them, K a is the kernel function;

[0113] Step S24: Continuously update the predicted value based on the aggregate disturbance at time k+1 Until the future dynamics of the lumped disturbance in the prediction time domain are obtained

[0114]

[0115] Among them, n a is the predicted length.

[0116] Specifically, we get After that, update According to formula (9), we can predict And so on, we can finally get the future dynamics of the lumped disturbance: As an alternative, n a =1, that is, the dynamic characteristics of the lumped disturbance in the next step are considered.

[0117] Step S3: The observed value of the lumped disturbance and future developments Fusion into the state space model of the wind-heat unit to perform steady-state target sequence (x ss|k+i ,u ss|k+i ) and construct an infinite prediction time-domain constrained optimization problem based on the steady-state target sequence and the state of the wind-thermal unit.

[0118] Step S31: At the current time k, the aggregate disturbance observation value And future developments Fusion to contain the steady-state target sequence (x ss|k+i ,u ss|k+i ) in the state space model of the wind-heat unit;

[0119]

[0120] Among them, x ss|k+i and u ss|k+i are the steady-state target values ​​of the state variables and the control variables at time k+i when considering disturbances; is the predicted value of the aggregate disturbance at time k+i, A, B, B d , C are the model parameters of the discretized state space model of the wind thermal unit; y a|k+i It is the steady-state output that the system can achieve under the constraints of the control variables and the steady-state target sequence; the constraints of the control variables are:

[0121]

[0122] in, and are the lower and upper limits of the variable pitch angle increment, u and are the lower and upper limits of the variable pitch angle, u k+i is the control variable at time k+i, Δu k+i is the increment of the control variable at time k+i; in this embodiment, u=0, (normalized), Δu = -0.2,

[0123] Step S32: By minimizing the steady-state output y that the system can achieve a|k+i With the set value (y r ,u r ) to dynamically solve the steady-state target sequence (x ss|k+i ,u ss|k+i );

[0124]

[0125] in, and are the steady-state target sequences of state variables and input variables, respectively, Q a and R a are two weight matrices respectively; in this embodiment, the weight matrix Q a =50 and R a =1.

[0126] Step S33: constructing an infinite prediction time domain constrained optimization problem based on a steady-state target sequence and the state of the wind-heat unit;

[0127]

[0128] in, x k+i+1 is the state variable at time k+i+1, u k+i is the control variable at time k+i, Q and R are and In this embodiment, the weight matrix Q=0.1, R=30.

[0129] Step S4: Combining the steady-state target sequence and the bimodal control strategy, construct a maximum controlled admissible set with disturbance prediction compensation.

[0130] Step S41: Divide the infinite prediction time domain into two modes: a transient adjustment region and a gradual convergence region, and adopt a dual-mode control law;

[0131]

[0132] Where K is the feedback gain, u k+i is the control variable of the wind-heating unit at time k+i, c k+i is the control degree of freedom at time k+i, n c is the step length of the transient regulation area, x ss|k+i and u ss|k+i are the steady-state target values ​​of the state variables and the control variables at time k+i when considering disturbances;

[0133] Step S42: Based on the dual-mode control law, the one-step dynamic characteristics of the wind-heat unit deviation form are solved as follows:

[0134]

[0135] Step S43: Define variables Control model of wind-heat unit with respect to variable s k The autonomous expression of is:

[0136]

[0137] in,

[0138] in, is the control degree of freedom sequence starting at time k in the prediction time domain, is the steady-state target sequence of state variables; A and B are the model parameters of the wind-heat unit state space model after discretization; Ω and Φ are parameters used to simplify the formula;

[0139] Step S44: Convert the constraints of the control variables into constraints on the autonomous variables s k Expression of

[0140]

[0141] in,

[0142] Step S45: According to the conversion into autonomous variable s k The constraint expression of is constructed to construct the maximum controlled admissible set with disturbance prediction compensation.

[0143] Step S45 further includes the following sub-steps:

[0144] Step a: define n = 0,

[0145] Step b: Solve the linear programming problem:

[0146]

[0147] Step c: Determine the s obtained in step b k Whether the constraints are met If not, then n=n+1, and return to step b; if so, n is large enough, the algorithm stops, and the maximum controlled admissible set is F·s k ≤t, transform the maximum controlled admissible set into the control degrees of freedom In the form of:

[0148] That is converted to

[0149] The maximum controlled admissible set is:

[0150]

[0151] in, N s 、M s and V s They are and The corresponding weight.

[0152] Step S5: Based on the maximum controlled admissible set and the steady-state target sequence, the infinite prediction time domain constrained optimization problem is transformed into an equivalent quadratic programming type constrained optimization problem for solution to obtain the anti-interference control law of the wind-heat unit.

[0153] Step S51: Combine the state model of the wind-heat unit and the maximum controlled admissible set with disturbance estimation compensation to transform the infinite prediction time domain constrained optimization problem into an equivalent quadratic programming type constrained optimization problem:

[0154]

[0155] in, is the control degree of freedom sequence at time k, S cs , S xs , S ss and S us They are x k , and The corresponding weight, x k is the state variable at time k,

[0156] Step S52: Solve the equivalent quadratic programming constrained optimization problem using a quadratic programming solver to obtain the optimal control degree of freedom sequence.

[0157] Step S53: Set the first element c of the optimal control degree of freedom sequence k Combine the dual-mode control law to obtain the anti-interference control law u at the current time k k ;

[0158] u k =-K(x k -x ss|k )+u ss|k-1 +c k (twenty two)

[0159] Among them, x k is the state variable at the current time k, K is the feedback gain, x ss|kis the steady-state target value of the state variable at the current time k, u ss|k-1 is the steady-state target value of the control variable at time k-1.

[0160] In this embodiment, the added external disturbance is wind speed disturbance: in the simulation 0-1000s, the wind speed is 12m / s, 1001s-2000s, the wind speed drops to 8m / s, 2001s-3000s, the wind speed rises to 12m / s. The results are shown in the attached figure. Figure 2 shown.

[0161] By the attached Figure 2 It can be seen that after considering the feedforward compensation of the future dynamics of the disturbance, the output power of the wind turbine can still quickly stabilize at the set value after a small fluctuation, which shows that the present invention can effectively control the wind thermal unit.

[0162] Example 2

[0163] A wind-heat unit constraint optimal model prediction anti-interference control system, comprising:

[0164] The lumped disturbance observation module is used to estimate the observed value of the lumped disturbance of the wind-heat unit in real time using the established extended state observer

[0165] The lumped disturbance prediction module is used to predict the observed value of the lumped disturbance The future dynamics of the lumped disturbance in the prediction time domain are predicted using the established disturbance predictor

[0166] Constrained optimization problem building block for transforming the observed values ​​of the lumped perturbation and future developments Fusion into the state space model of the wind-heat unit to perform steady-state target sequence (x ss|k+i ,u ss|k+i ) and construct an infinite prediction time-domain constrained optimization problem based on the steady-state target sequence and the state of the wind-heat unit;

[0167] The maximum controlled admissible set construction module is used to combine the steady-state target sequence and the bimodal control strategy to construct the maximum controlled admissible set with disturbance estimation compensation;

[0168] The anti-interference control law acquisition module is used to transform the infinite prediction time domain constraint optimization problem into an equivalent quadratic programming type constraint optimization problem based on the maximum controlled allowable set and the steady-state target sequence to obtain the anti-interference control law of the wind-heat unit.

[0169] For more specific processes of the above method, please refer to the corresponding contents disclosed in the aforementioned embodiments, which will not be repeated here.

[0170] Example 3

[0171] The present invention provides a computer device, comprising a processor and a memory; wherein, when the processor executes a computer program stored in the memory, the steps of the above-mentioned wind-heat unit constraint optimal model prediction anti-interference control method are implemented.

[0172] For more specific processes of the above method, please refer to the corresponding contents disclosed in the aforementioned embodiments, which will not be repeated here.

[0173] Example 4

[0174] The present invention provides a computer-readable storage medium for storing a computer program; when the computer program is executed by a processor, the steps of the above-mentioned wind-heat unit constraint optimal model prediction anti-interference control method are implemented.

[0175] For more specific processes of the above method, please refer to the corresponding contents disclosed in the aforementioned embodiments, which will not be repeated here.

[0176] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system, device, and storage medium disclosed in the embodiment, since they correspond to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0177] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a disk, an optical disk, etc., and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention or some parts of the embodiments.

[0178] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A method for predicting anti-interference control of wind-heat unit with optimal model constraints, characterized in that: include: The established extended state observer is used to estimate the observed value of the lumped disturbance of the wind-heat unit in real time According to the observed value of the lumped disturbance The future dynamics of the lumped disturbance in the prediction time domain are predicted using the established disturbance predictor The observed value of the lumped disturbance and future developments Fusion into the state space model of the wind-heat unit to perform steady-state target sequence (x ss|k+i ,u ss|k+i ) and construct an infinite prediction time-domain constrained optimization problem based on the steady-state target sequence and the state of the wind-heat unit; Combining the steady-state target sequence and the bimodal control strategy, the maximum controlled admissible set with disturbance prediction compensation is constructed. Based on the maximum controlled admissible set and steady-state target sequence, the infinite prediction time domain constrained optimization problem is transformed into an equivalent quadratic programming constrained optimization problem and solved to obtain the anti-interference control law of the wind-thermal unit.

2. The wind-heat unit constraint optimal model prediction anti-interference control method according to claim 1 is characterized in that: The expression of the extended state observer is: in, and are the derivatives of z1 and z2 respectively; z1 and z2 are the estimated values ​​of wind turbine output power y and lumped disturbance d respectively, u is the pitch angle of the wind turbine, A m and B m are all model parameters of the state space model of the wind thermal unit; L = [β 01 ,β 02 ] T is the gain of the extended state observer.

3. The wind-heat unit constraint optimal model prediction anti-interference control method according to claim 1 is characterized in that: The observed value of the lumped disturbance The established disturbance predictor predicts the future dynamics of the lumped disturbance in the prediction time domain Specifically: According to the current time k and the aggregate disturbance sequence observed in the past, update the training sample D = (d in ,d out ); Where N is the sample size, n p is the dimension of the input variables in the training sample; Using the updated training samples, fit the least squares parameters b and α of the disturbance predictor i ; Where E = [1,1,…,1] T , H is the kernel function matrix; Will As the test sample input, the disturbance predictor predicts the predicted value of the lumped disturbance at the next time k+1 for: Among them, K a is the kernel function; The predicted value based on the aggregate disturbance at time k+1 is continuously updated Until the future dynamics of the lumped disturbance in the prediction time domain are obtained Among them, n a is the predicted length.

4. The wind-heat unit constraint optimal model prediction anti-interference control method according to claim 1 is characterized in that: The observed value of the lumped disturbance and future developments Fusion into the state space model of the wind-heat unit to perform steady-state target sequence (x ss|k+i ,u ss|k+i ) and construct an infinite prediction time-domain constrained optimization problem based on the steady-state target sequence and the state of the wind-heat unit. The specific process is as follows: At the current time k, the aggregated disturbance observation value And future developments Fusion to contain the steady-state target sequence (x ss|k+i ,u ss|k+i ) in the state space model of the wind-heat unit; Among them, x ss|k+i and u ss|k+i are the steady-state target values ​​of the state variables and the control variables at time k+i when considering disturbances; is the predicted value of the aggregate disturbance at time k+i, A, B, B d , C are the model parameters of the discretized state space model of the wind thermal unit; y a|k+i It is the steady-state output that the system can achieve under the constraints of the control variables and the steady-state target sequence; the constraints of the control variables are: Among them, Δu and are the lower and upper limits of the variable pitch angle increment, u and are the lower and upper limits of the variable pitch angle, u k+i is the control variable at time k+i, Δu k+i is the increment of the control variable at time k+i; By minimizing the steady-state output y that the system can achieve a|k+i With the set value (y r ,u r ) to dynamically solve the steady-state target sequence (x ss|k+i ,u ss|k+i ); in, and are the steady-state target sequences of state variables and input variables, respectively, Q a and R a They are two weight matrices respectively; Construct an infinite prediction time domain constrained optimization problem based on the steady-state target sequence and the state of the wind-heat unit; in, x k+i+1 is the state variable at time k+i+1, u k+i is the control variable at time k+i, Q and R are and The weight matrix of .

5. The wind-heat unit constraint optimal model prediction anti-interference control method according to claim 1, characterized in that: The described method combines the steady-state target sequence and the dual-mode control strategy to construct the maximum controlled admissible set with disturbance prediction compensation. The specific process is as follows: The infinite prediction time domain is divided into two modes: transient regulation area and gradual convergence area, and a dual-mode control law is adopted; Where K is the feedback gain, u k+i is the control variable of the wind-heating unit at time k+i, c k+i is the control degree of freedom at time k+i, n c is the step length of the transient regulation area, x ss|k+i and u ss|k+i are the steady-state target values ​​of the state variables and the control variables at time k+i when considering disturbances; Based on the bimodal control law, the deviation between the state variable of the wind-heat unit and the steady-state target value at time k+1 is obtained; Defining variables Control model of wind-heat unit with respect to variable s k The autonomous expression of is: in, is the control degree of freedom sequence starting at time k in the prediction time domain, is the steady-state target sequence of state variables; A and B are the model parameters of the wind-heat unit state space model after discretization; Ω and Φ are parameters used to simplify the formula; Transform the constraints on the control variables into constraints on the autonomous variables s k Expression of in, K s =[K,[-1,0,...,0],0], According to the transformation into the autonomous variable s k The constraint expression of is constructed to construct the maximum controlled admissible set with disturbance prediction compensation.

6. The wind-heat unit constraint optimal model prediction anti-interference control method according to claim 5 is characterized in that: The basis is transformed into the autonomous variable s k The constraint expression of the maximum controlled admissible set with disturbance estimation compensation is constructed. The specific process is: Step a: define n = 0, Step b: Solve the linear programming problem: Step c: Determine the s obtained in step b k Whether the constraints are met If not, then n=n+1, and return to step b; if so, n is large enough, the algorithm stops, and the maximum controlled admissible set is F·s k ≤t, transform the maximum controlled admissible set into the control degrees of freedom In the form of: in, N s 、M s and V s They are and The corresponding weight.

7. The wind-heat unit constraint optimal model prediction anti-interference control method according to claim 1, characterized in that: Based on the maximum controlled admissible set and the steady-state target sequence, the infinite prediction time domain constraint optimization problem is transformed into an equivalent quadratic programming type constraint optimization problem for solution to obtain the anti-interference control law of the wind-heat unit, which is specifically: By combining the state model of the wind-heat unit and the maximum controlled admissible set with disturbance estimation compensation, the infinite prediction time domain constrained optimization problem is transformed into an equivalent quadratic programming type constrained optimization problem: in, is the control degree of freedom sequence at time k, S cs , S xs , S ss and S us They are x k , and The corresponding weight, x k is the state variable at time k, The equivalent quadratic programming constrained optimization problem is solved using a quadratic programming solver to obtain the optimal control degree of freedom sequence. The first element c of the optimal control degree of freedom sequence is k Combine the dual-mode control law to obtain the anti-interference control law u at the current time k k ; u k =-K(x k -x ss|k )+u ss|k-1 +c k Among them, x k is the state variable at the current time k, K is the feedback gain, x ss|k is the steady-state target value of the state variable at the current time k, u ss|k-1 is the steady-state target value of the control variable at time k-1.

8. A wind-heat unit constrained optimal model prediction anti-interference control system, characterized in that: include: The lumped disturbance observation module is used to estimate the observed value of the lumped disturbance of the wind-heat unit in real time using the established extended state observer The lumped disturbance prediction module is used to predict the observed value of the lumped disturbance The future dynamics of the lumped disturbance in the prediction time domain are predicted using the established disturbance predictor Constrained optimization problem building block for transforming the observed values ​​of the lumped perturbation and future developments Fusion into the state space model of the wind-heat unit to perform steady-state target sequence (x ss|k+i ,u ss|k+i ) and construct an infinite prediction time-domain constrained optimization problem based on the steady-state target sequence and the state of the wind-heat unit; The maximum controlled admissible set construction module is used to combine the steady-state target sequence and the bimodal control strategy to construct the maximum controlled admissible set with disturbance estimation compensation; The anti-interference control law acquisition module is used to transform the infinite prediction time domain constraint optimization problem into an equivalent quadratic programming type constraint optimization problem based on the maximum controlled allowable set and the steady-state target sequence to obtain the anti-interference control law of the wind-heat unit.

9. A computer device, characterized in that: It comprises a processor and a memory; wherein, when the processor executes the computer program stored in the memory, the steps of the wind-thermal unit constraint optimal model prediction anti-interference control method described in any one of claims 1-7 are implemented.

10. A computer-readable storage medium, characterized in that: Used to store computer programs; when the computer programs are executed by the processor, the steps of the wind-thermal unit constraint optimal model prediction anti-interference control method described in any one of claims 1-7 are implemented.

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