Method and device for constructing model predictive controller for VSG control strategy

Abstract

The disclosed embodiments provide a method and apparatus for constructing a model predictive controller for a VSG control strategy. The method includes: constructing a predictive output equation based on the mechanical characteristic equation and active frequency control equation of a virtual synchronous generator; calculating an output vector parameter matrix based on the virtual angular frequency, electromagnetic power, mechanical power, and the damping coefficient and virtual moment of inertia of the virtual synchronous generator at each time point in the prediction and control time domains; constructing a cost function for the sampling period data within the prediction time domain and determining the corresponding diagonal matrix sum; and constructing a model predictive controller based on the input vector parameter matrix and the diagonal matrix.

Description

Technical Field

[0001] The present disclosure relates to the field of electric power technology, and in particular to a method and device for constructing a model predictive controller applied to a VSG control strategy. Background Art

[0002] The integration of large-scale renewable energy generation equipment into the grid will lead to a reduction in the overall inertia of the power grid. Traditional flexible DC converter stations are unable to provide grid inertia support, which affects the stable and safe operation of the power grid. In this context, experts and scholars have proposed using virtual synchronous generator (VSG) control strategies to improve system frequency and voltage stability and simulate the high current output capacity of traditional synchronous generators in the event of a short-circuit fault in the power system. Although droop coefficients are widely used in virtual synchronous generator control strategies for active power-frequency regulation, this method does not significantly contribute to secondary frequency regulation.

[0003] In order to improve the effect of droop control secondary frequency modulation, some scholars have tried to improve it and proposed the application of mixed potential function method. The selection range of the droop coefficient of the energy storage converter was analyzed by formula derivation, but the optimal droop coefficient selection method was not given.

[0004] To find a reasonable control function, common control methods include the sparrow search algorithm, H-∞ control, and model predictive control. Model predictive controllers (MPCs) have gained favor in recent years due to their improved closed-loop performance and robustness. However, the challenge remains to develop a MPC with improved performance. Summary of the Invention

[0005] The embodiments of the present disclosure provide a method and apparatus for constructing a model predictive controller for use in a VSG control strategy.

[0006] In a first aspect, an embodiment of the present disclosure provides a method for constructing a model predictive controller applied to a VSG control strategy, comprising:

[0007] Get the predicted time domain N p and control time domain N m The virtual angular frequency w(k) and electromagnetic power P of each sampling period e (k), mechanical power P m (k), and the damping coefficient D of the virtual synchronous generator p and virtual moment of inertia J, where the prediction time domain N p ≤Control time domain N m ;

[0008] Based on the mechanical characteristic equation and active frequency control equation of the virtual synchronous generator, the output equation and state variable equation of mechanical power are constructed, and the predicted time domain N is obtained. p The mechanical power prediction output equation Y for each sampling period P (k+1|k), the prediction output equation is ΔU(k) ​​is the control time domain N m Mechanical power change vector of the sampling period, S w is the angular frequency parameter matrix, I is the mechanical power parameter matrix, S D is the electromagnetic power parameter matrix, S B is the input vector parameter matrix;

[0009] Based on the prediction time domain N p and control time domain N m Virtual angular frequency w(k) and electromagnetic power P at each time point e (k), mechanical power P m (k), and the damping coefficient D of the virtual synchronous generator p and virtual moment of inertia J, calculate the output vector parameter matrix S B ;

[0010] Make the prediction time domain N p The predicted output of each sampling period approaches the reference signal R(k+1)=0, and the mechanical power increment ΔP m (k) is as small as possible to construct the prediction time domain N for the target p Cost function for internal sampling period data Based on the control time domain N m The optimized control parameter vector ΔU(k) ​​of the sampling period and the predicted output equation Y of each sampling period P (k+1|k) determines the diagonal matrix and

[0011] Based on the input vector parameter matrix S B , diagonal matrix and the diagonal matrix Building a Model Predictive Controller

[0012] Optional,

[0013]

[0014]

[0015] Where T s is the control period of the virtual synchronous generator.

[0016] In a second aspect, an embodiment of the present disclosure provides a method for determining an output angular frequency of a virtual synchronous generator control strategy, comprising:

[0017] The angular frequency change Δw(k) is calculated based on the output angular frequency of the current cycle and the output angular frequency of the previous cycle, and the electromagnetic power change ΔP is calculated based on the electromagnetic power of the current cycle and the electromagnetic power of the previous cycle. e (k);

[0018] use Based on the angular frequency change Δw(k) and the electromagnetic power change ΔP e (k), mechanical power y of the current cycle o (k) Calculate the mechanical power change parameter E at the next moment p (k+1|k), where is the angular frequency parameter matrix S w , mechanical power parameter matrix I, electromagnetic power parameter matrix S D calculated in the process of constructing a model predictive controller applied to a virtual synchronous generator control strategy as claimed in claims 1-2;

[0019] Calculating a predicted amount of mechanical power change based on the analog predictive controller and the mechanical power change parameter determined as claimed in claims 1-2;

[0020] The predicted mechanical power of the next cycle is calculated based on the mechanical power of the virtual synchronous generator in the current cycle and the predicted amount of mechanical power change;

[0021] The output angular frequency of the next cycle of the virtual synchronous generator is determined based on the predicted mechanical power of the next cycle.

[0022] Optional,

[0023]

[0024]

[0025]

[0026] Where T s is the control period of the system.

[0027] In a third aspect, an embodiment of the present disclosure provides a control method for a grid-type clean power generation system, wherein the grid-type clean power generation system includes a grid-side converter, including:

[0028] Determine the virtual angular frequency of the virtual synchronous generator in the next cycle and the virtual voltage peak value of the virtual synchronous generator in the next cycle by using the aforementioned method for determining the output angular frequency of the virtual synchronous generator control strategy;

[0029] determining a synthetic internal potential based on the virtual angular frequency and the virtual voltage peak value;

[0030] Based on the synthetic internal potential, a control strategy based on a voltage-current dual closed loop is adopted to determine a switch control signal for controlling the switch tube of the grid-side converter.

[0031] In a fourth aspect, an embodiment of the present disclosure provides a device for constructing a model predictive controller for a VSG control strategy, characterized by comprising:

[0032] Parameter acquisition unit, used to obtain the prediction time domain N p and control time domain N m The virtual angular frequency w(k) and electromagnetic power P of each sampling period e (k), mechanical power P m (k), and the damping coefficient D of the virtual synchronous generator p and virtual moment of inertia J, where the prediction time domain N p ≤Control time domain N m ;

[0033] The virtual synchronous generator model building unit is used to build the output equation and state variable equation of mechanical power based on the mechanical characteristic equation and active frequency control equation of the virtual synchronous generator, and obtain the predicted time domain N p The mechanical power prediction output equation Y for each sampling period P (k+1|k), the prediction output equation is ΔU(k) ​​is the control time domain N m Mechanical power change vector of the sampling period, S w is the angular frequency parameter matrix, I is the mechanical power parameter matrix, S D is the electromagnetic power parameter matrix, S B is the input vector parameter matrix;

[0034] Matrix calculation unit, used to predict the time domain N p and control time domain N m Virtual angular frequency w(k) and electromagnetic power P at each time point e (k), mechanical power P m (k), and the damping coefficient D of the virtual synchronous generator p and virtual moment of inertia J, calculate the output vector parameter matrix S B ;

[0035] Loss function calculation unit, used to make the prediction time domain N p The predicted output of each sampling period approaches the reference signal R(k+1)=0, and the mechanical power increment ΔP m(k) is as small as possible to construct the prediction time domain N for the target p Cost function for internal sampling period data Based on the control time domain N m The optimized control parameter vector ΔU(k) ​​of the sampling period and the predicted output equation Y of each sampling period P (k+1|k) determines the diagonal matrix and

[0036] The controller building block is used to calculate the parameter matrix S based on the input vector B , diagonal matrix and the diagonal matrix Building a Model Predictive Controller

[0037] In a fifth aspect, an embodiment of the present disclosure provides a device for determining an output angular frequency of a virtual synchronous generator control strategy, comprising:

[0038] The difference calculation unit is used to calculate the angular frequency change Δw(k) based on the output angular frequency w(k) of the current cycle and the output angular frequency of the previous cycle, and to calculate the electromagnetic power change ΔP based on the electromagnetic power of the current cycle and the electromagnetic power of the previous cycle. e (k);

[0039] Variation parameter calculation unit, used to adopt Based on the angular frequency change Δw(k) and the electromagnetic power change ΔP e (k), mechanical power y of the current cycle o (k)E p (k+1|k) calculates the mechanical power change parameter E at the next moment p (k+1|k), where is the angular frequency parameter matrix S w , mechanical power parameter matrix I, electromagnetic power parameter matrix S D Calculated in the process of building a model predictive controller applied to the virtual synchronous generator control strategy;

[0040] A mechanical power change prediction amount calculation unit, configured to calculate the mechanical power change prediction amount using the previously determined analog prediction controller and the mechanical power change parameter;

[0041] A predicted mechanical power calculation unit, configured to calculate the predicted mechanical power of the next cycle based on the mechanical power of the virtual synchronous generator in the current cycle and the predicted amount of mechanical power change;

[0042] The output angular frequency calculation unit is used to determine the output angular frequency of the next cycle of the virtual synchronous generator based on the predicted mechanical power of the next cycle.

[0043] In a sixth aspect, an embodiment of the present disclosure provides a control device for a grid-type clean power generation system, wherein the grid-type clean power generation system includes a grid-side converter, including:

[0044] A virtual synchronous generator control strategy calculation unit is used to determine the virtual angular frequency of the virtual synchronous generator in the next cycle and the virtual voltage peak value of the virtual synchronous generator in the next cycle using the method for determining the output angular frequency of the virtual synchronous generator control strategy as described above;

[0045] a synthetic internal potential calculation unit, configured to determine a synthetic internal potential based on the virtual angular frequency and the virtual voltage peak value;

[0046] A control signal determination unit is used to determine, based on the synthetic internal potential, a switch control signal for controlling the switch tube of the grid-side converter by adopting a control strategy based on a voltage-current dual closed loop.

[0047] In a seventh aspect, an embodiment of the present disclosure provides a grid-type clean power generation system, comprising a clean generator set, a generator-side converter, a grid-side converter, and a filter circuit; the input end of the generator-side converter is connected to the output end of the clean generator set, and the input end of the grid-side converter is connected to the input end of the generator-side converter; the output end of the grid-side converter is connected to a grid injection and connection point through a filter circuit;

[0048] The grid-type clean power generation system further includes a controller, which executes the above-mentioned method.

[0049] In an eighth aspect, an embodiment of the present disclosure provides a control device, comprising a processor and a memory, wherein the memory is used to store a computer program; when the computer program is loaded by the processor, the processor executes the method as described in any of the preceding items.

[0050] The solution provided by the embodiment of the present disclosure obtains the input vector parameter matrix S from the model of the virtual synchronous generator control strategy B The diagonal matrices Q and R are determined by constructing a loss function, and the model predictive controller is constructed using Q and R. The model predictive controller determined based on this scheme can achieve model output prediction under the optimization conditions based on the loss function, and can be used in the virtual synchronous generator droop control strategy, which also enables the virtual synchronous generator control strategy to have good secondary frequency regulation capability. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0052] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without inventive work, including:

[0053] Figure 1 This is a schematic diagram of the power-frequency static characteristics of the flexible DC receiving converter station participating in primary frequency regulation;

[0054] Figure 2 A schematic diagram showing the virtual frequency regulator control strategy in the virtual synchronous generator control strategy;

[0055] Figure 3 1 is a schematic structural diagram of a device for constructing a model predictive controller for a virtual synchronous generator control strategy provided by an embodiment of the present disclosure;

[0056] Figure 4 Schematic diagram of the control device structure of the grid-type clean power generation system provided by an embodiment of the present disclosure;

[0057] Figure 5 is a structural diagram of a grid-type clean power generation system provided by an embodiment of the present disclosure;

[0058] Figure 6 is a structural diagram of a grid-type clean power generation system provided by an embodiment of the present disclosure;

[0059] Figure 7 It is a structural diagram of the control device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0060] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0061] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0062] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0063] The embodiments of the present disclosure provide a method for constructing a model predictive controller for a virtual synchronous generator control strategy and a method for constructing a model predictive controller for a VSG control strategy. The model predictive controller is constructed based on a mathematical model of the virtual synchronous machine, thereby better achieving control of the optimal droop coefficient of the virtual synchronous generator.

[0064] Before analyzing the solution provided by the embodiment of the present disclosure, the lack of good secondary frequency modulation capability in the existing technology is first analyzed.

[0065] Figure 1 This is a schematic diagram of the power-frequency static characteristics of the flexible DC receiving converter station participating in primary frequency regulation. Figure 1 As shown in the figure, the frequency modulation process under the traditional droop control strategy is as follows: Assuming that the two characteristic curves P G (f) and P D The intersection point O of (f) is the original operating point, and the system frequency is f0. The system load increases by ΔP D0 After that, the operating point will move to point B, and the system frequency will drop to f1. Under the action of the virtual synchronous generator active power-frequency link simulating the governor characteristics, the static characteristics of the unit move up to P' G (f), the operating point also shifts to point B'. At this time, the system frequency is f1', and the frequency offset value is Δf = f'1-f0.

[0066] This shows that the primary frequency regulation power-frequency strategy has excellent primary frequency adjustment capabilities, but insufficient secondary frequency regulation capabilities. To address this issue, the disclosed embodiment introduces model predictive control into the active power-frequency control link, greatly increasing the ability of the droop link to participate in the secondary frequency regulation of the power system frequency. The prerequisite for implementing the aforementioned solution is to construct a reasonable model predictive controller for use in the virtual synchronous generator control strategy. Therefore, the following first analyzes how to construct a model predictive controller and then analyzes how to use the model predictive controller.

[0067] In the embodiment of the present disclosure, the method for constructing a model predictive controller applied to a virtual synchronous generator control strategy includes S110 to S150.

[0068] S110: Obtain prediction time domain N p and control time domain N m The virtual angular frequency w(k) and electromagnetic power P of each sampling period e (k), mechanical power P m (k), and the damping coefficient D of the virtual synchronous generator p and virtual moment of inertia J, where the prediction time domain N p ≤Control time domain N m .

[0069] S120: Based on the mechanical characteristic equation and active frequency control equation of the virtual synchronous generator, the output equation and state variable equation of mechanical power are constructed, and the predicted time domain N is obtained. p Mechanical power prediction output equation for each sampling period ΔU(k) ​​is the control time domain N m Mechanical power change vector of the sampling period, S w is the angular frequency parameter matrix, I is the mechanical power parameter matrix, S D is the electromagnetic power parameter matrix, S B is the input vector parameter matrix.

[0070] In order to simulate the characteristics of a synchronous generator, the virtual synchronous generator control strategy can simulate the generator providing electromagnetic power and introduce the mathematical model of the generator into the control strategy of the grid-side converter, making the grid-side converter equivalent to a virtual synchronous generator, thereby improving the stability of the receiving power grid.

[0071] The mechanical characteristic equation and active power control equation of the virtual synchronous generator are shown in Formula 1.

[0072]

[0073] In formula 1, J represents the virtual moment of inertia; D p is the damping coefficient of the synchronous generator; P e=T e w0、P m =T m w0 represents the electromagnetic power and mechanical power of the grid-side converter; T e 、T m are the electromagnetic torque and mechanical torque of the converter station respectively; w is the output virtual angular frequency, and w0 is the rated angular frequency.

[0074] By rewriting the mechanical characteristic equation and active frequency control equation of the virtual synchronous generator into incremental form, the following formula 2-5 can be obtained.

[0075]

[0076] P e (k) = P e (k-1)+ΔP e (k) Formula 3

[0077] P m (k) = P m (k-1)+ΔP m (k) Formula 4

[0078]

[0079] In formula 2-5, Δw(k) is the increment of the state variable; ΔP m (k) is the increment of the operating variable. Considering the impact of the environment on the grid-side converter, the power fluctuation on the transmission side affects the safe and stable operation of the grid-side converter, so ΔP e (k) is a measurable disturbance input.

[0080] After performing discretization transformation on Formula 5, Formula 6 and Formula 8 can be obtained.

[0081] Δw(k+1)=AΔw(k)+BΔP m (k)+DΔP e (k) Formula 6

[0082]

[0083] y o (k) = m w Δw(k)+y o (k-1) Formula 8

[0084] Where y in Formula 8 o The actual corresponding ΔP in formula 5 m (k), that is, it represents the mechanical power of a certain cycle. A, B, D, m in formula 7 and formula 9 wRepresent the state quantity, control quantity, disturbance quantity and output quantity of the system respectively; y o is the controlled output variable.

[0085] According to the basic principle of model predictive control, it is necessary to solve the numerical optimization equation online at each sampling moment. However, the huge amount of calculation required for rolling optimization is not conducive to the rapid solution of numerical optimization problems by the model predictive controller. In order to reduce the number of independent variables and the amount of calculation required in the model predictive controller calculation process, this embodiment introduces the concept of control time domain. To this end, the prediction time domain N is set. p and control time domain N m , N p Represents the window length for optimizing future state variables, N m determines the number of control trajectories used to capture the future, and N m ≤N p .

[0086] In order to facilitate the derivation of the prediction equation, this embodiment has two assumptions: (1) Since N m ≤N p , and predicting future dynamics requires the control input of the entire Np, so assuming ΔP m (k+i)=0, i=N m ,N m +1,…,N p -1; (2) At time k, the future value of the interference is uncertain, assuming ΔP e (k+i)=0,i=1,2,…,N p-1 .

[0087] Taking Δw(k) as the starting point, Formula 6 can use the control parameters to predict the future k+1 to k+N p The state variable at time t, we get Formula 9 (state variable equation).

[0088]

[0089] According to Formula 8 and Formula 9, the controlled outputs from k+1 to k+p can be obtained from the predicted state variables, such as the mechanical power prediction output equation of Formula 10.

[0090]

[0091] Prediction time domain N p The mechanical power prediction output equation for each sampling period is simplified to Formula 11.

[0092]

[0093] In formula 11, ΔU(k) ​​is the control time domain N m Mechanical power change vector of the sampling period, Sw is the angular frequency parameter matrix, I is the mechanical power parameter matrix, S D is the electromagnetic power parameter matrix, S B is the input vector parameter matrix.

[0094] S130: Based on the prediction time domain N p and control time domain N m Virtual angular frequency w(k) and electromagnetic power P at each time point e (k), mechanical power P m (k), and the damping coefficient D of the virtual synchronous generator p and virtual moment of inertia J, calculate the output vector parameter matrix S B .

[0095] In the specific implementation, the predicted time domain N obtained in S110 is p and control time domain N m Virtual angular frequency w(k) and electromagnetic power P at each time point e (k), mechanical power P m (k), and the damping coefficient D of the virtual synchronous generator p And the virtual moment of inertia J, according to the above formula 6-formula 11, the output vector parameter matrix S can be obtained B , specifically, the output vector parameter matrix S B As shown in formula 12.

[0096]

[0097] While deriving the output vector parameter matrix, the mechanical power change vector ΔU(k) ​​and the angular frequency parameter matrix S can be obtained. w , mechanical power parameter matrix I, electromagnetic power parameter matrix S D They are Formula 13 to Formula 16 respectively.

[0098]

[0099]

[0100]

[0101]

[0102] S140: Make the prediction time domain N p The predicted output of each sampling period approaches the reference signal R(k+1)=0, and the mechanical power increment ΔP m (k) is as small as possible to construct the prediction time domain N for the target p Cost function for internal sampling period data Based on the control time domain N m The optimized control parameter vector ΔU(k) ​​of the sampling period and the predicted output equation Y of each sampling period P (k+1|k) determines the diagonal matrix and

[0103] The selection of cost function reflects the need for system stability. This paper mainly uses p In the optimization window, the predicted output is close to the reference signal R(k+1)=0, where we assume that the reference signal remains unchanged in the optimization window to find the optimal control parameter vector ΔU(k), calculate the minimum error between the predicted output and the reference value, and control Δw=0 to make the system frequency increment 0, so as to control the receiving end grid to operate stably at the rated frequency. At the same time, we hope to control the manipulated variable increment ΔP m (k) should not be too large, so as not to impose excessive current stress on the grid equipment in a short period of time or trigger the relay protection to malfunction. Therefore, Formula 17 is selected as the cost function, and the optimal control behavior is found by minimizing the objective function within the optimization window.

[0104]

[0105] In formula 17, In formula 17, the first term reflects the consideration of the value of ΔU in order to make the cost function as small as possible, R u,i The larger the corresponding control action ΔP m The smaller the change; the second term is related to the purpose of minimizing the error between the predicted output and the reference signal, Q y,i The larger the value, the closer the corresponding control output is to the given reference input.

[0106] After constructing the cost function represented by formula 17, the numerical calculation method is used based on the control time domain N m The optimized control parameter vector ΔU(k) ​​of the sampling period and the predicted output equation Y of each sampling period P (k+1|k) can determine the diagonal matrix and

[0107] S150: Based on the input vector parameter matrix S B , diagonal matrix and the diagonal matrix Building a Model Predictive Controller

[0108] After getting the input vector parameter matrix S B , diagonal matrix and the diagonal matrix Afterwards, use That is, the model predictive controller K is obtained mpc .

[0109] The model predictive controller construction method provided by the embodiment of the present disclosure is used to construct the input vector parameter matrix S from the model of the virtual synchronous generator control strategy. B The diagonal matrices Q and R are determined by constructing a loss function, and the model predictive controller is constructed using Q and R. The model predictive controller determined based on this scheme can achieve model output prediction under the optimization conditions based on the loss function, and can be used in the virtual synchronous generator droop control strategy, which also enables the virtual synchronous generator control strategy to have good secondary frequency regulation capability.

[0110] The embodiment of the present disclosure further provides a method for determining an output angular frequency of a virtual synchronous generator control strategy. The method for controlling an output angular frequency of a virtual synchronous generator control strategy includes steps S210 to S250.

[0111] S210: Calculate the angular frequency variation Δw(k) based on the output angular frequency of the current cycle and the output angular frequency of the previous cycle, and calculate the electromagnetic power variation ΔP based on the electromagnetic power of the current cycle and the electromagnetic power of the previous cycle. e (k).

[0112] The output angular frequency of the current cycle is w(k), and the output angular frequency of the previous cycle is w(k-1), so the angular frequency change Δw(k)=w(k)-w(k-1).

[0113] The electromagnetic power of the current cycle is P e (k), the electromagnetic power of the previous cycle is P e (k-1), then the change in electromagnetic power ΔP e (k) = P e (k)-P e (k-1).

[0114] S220: Adopt Based on the angular frequency change Δw(k) and the electromagnetic power change ΔP e (k), mechanical power y of the current cycle o (k)E p (k+1|k) calculates the mechanical power change parameter E at the next moment p (k+1|k).

[0115] In the embodiment of the present disclosure, the aforementioned angular frequency parameter matrix S w , mechanical power parameter matrix I, electromagnetic power parameter matrix S D It is calculated in the process of constructing the model predictive controller applied to the virtual synchronous generator control strategy in the previous embodiment.

[0116] S230: Calculating a mechanical power change prediction value based on the simulation prediction controller and the mechanical power change parameter.

[0117] In the specific implementation, the mechanical power change prediction can be obtained by multiplying the analog predictive controller with the mechanical power change parameter, that is, using ΔP m (k) = K mpc E p (k+1|k) is calculated to obtain the predicted mechanical power change.

[0118] S240: Calculate the predicted mechanical power of the next cycle based on the mechanical power of the virtual synchronous generator in the current cycle and the predicted amount of mechanical power change.

[0119] In the specific implementation, the predicted amount of mechanical power conversion in the next cycle can be sorted out by the above formula to be Where: (1) R(k+1) is the reference input in the prediction time domain, K mpc R(k+1) is the feedforward compensation based on the future reference input; (2)ΔP e (k) is the increment of measurable interference, K mpc S D ΔP e (k) is the feedforward compensation based on the measurable disturbance; (3) w(k) is the system state value obtained from the measurement value, K mpc (S w +Im w )w(k)+K mpc S w w(k-1) is the feedback compensation based on the measured value. Therefore, the predictive control has a "feedforward-feedback" structure, which shows that the model predictive controller method is effective and has excellent performance in terms of control structure. m * Substituting (k) into Formula 5, the closed-loop control system can be derived as Formula 18.

[0120]

[0121] By argument, if A-BK mpc (S w +Im w ) all eigenvalues ​​are inside the unit circle, then It is asymptotically stable in closed-loop system theory.

[0122] The mechanical power of the virtual synchronous generator in the current cycle is P m (k-1), P m (k-1) and the predicted mechanical power change ΔP m The predicted mechanical power for the next cycle is calculated.

[0123] S250: Determine the output angular frequency of the next cycle of the virtual synchronous generator based on the predicted mechanical power of the next cycle.

[0124] After obtaining the predicted mechanical power of the next cycle, the output power and P ref Calculate P set , P set and P e By performing phase difference calculation and adopting the existing virtual frequency modulator control method, the output angular frequency of the next cycle can be obtained.

[0125] Figure 2 A schematic diagram showing the virtual frequency regulator control strategy in the virtual synchronous generator control strategy. Figure 2 It generally shows how to use the virtual frequency modulation to calculate the output angular frequency of the next cycle according to S220-S250.

[0126] In addition to providing the aforementioned method for determining the output angular frequency of the virtual synchronous generator control strategy, the present disclosure also provides a control method for a grid-type clean power generation system. The control method for a grid-type clean power generation system provided by the present disclosure includes S310-S330.

[0127] S310: Determine the virtual angular frequency of the virtual synchronous generator in the next cycle and the virtual voltage peak value of the virtual synchronous generator in the next cycle using a method for determining the output angular frequency of the virtual synchronous generator control strategy.

[0128] S330: Determine a synthetic internal potential based on the virtual angular frequency and the virtual voltage peak value.

[0129] S330: Based on the synthetic internal potential, a control strategy based on a voltage-current dual closed loop is adopted to determine a switch control signal for controlling a switch tube of the grid-side converter.

[0130] In specific implementation, the method of determining the virtual voltage peak value of the virtual synchronous generator in the next cycle in S310 and the steps of S320-S330 are the same as those in the prior art and will not be described in detail here. For details, please refer to relevant technical documents.

[0131] In addition to providing the aforementioned method, the embodiments of the present disclosure also provide a device determined based on the aforementioned method. The following analyzes the structure of the device provided by the embodiments of the present disclosure.

[0132] Figure 3 Schematic diagram of the structure of the device for constructing the model predictive controller for the virtual synchronous generator control strategy provided by the embodiment of the present disclosure. Figure 3As shown, the construction device of the model predictive controller 300 applied to the virtual synchronous generator control strategy includes a parameter acquisition unit 301, a virtual synchronous generator model construction unit 302, a matrix calculation unit 303, a loss function calculation unit 304 and a controller construction unit 305.

[0133] The parameter acquisition unit 301 is used to obtain the prediction time domain N p and control time domain N m The virtual angular frequency w(k) and electromagnetic power P of each sampling period e (k), mechanical power P m (k), and the damping coefficient D of the virtual synchronous generator p and virtual moment of inertia J, where the prediction time domain N p ≤Control time domain N m .

[0134] The virtual synchronous generator model building unit 302 is used to build the output equation of mechanical power and the state variable equation based on the mechanical characteristic equation and the active frequency control equation of the virtual synchronous generator, and obtain the predicted time domain N p The mechanical power prediction output equation Y for each sampling period P (k+1|k), the prediction output equation is ΔU(k) ​​is the control time domain N m Mechanical power change vector of the sampling period, S w is the angular frequency parameter matrix, I is the mechanical power parameter matrix, S D is the electromagnetic power parameter matrix, S B is the input vector parameter matrix.

[0135] The matrix calculation unit 303 is used to calculate the matrix based on the prediction time domain N p and control time domain N m Virtual angular frequency w(k) and electromagnetic power P at each time point e (k), mechanical power P m (k), and the damping coefficient D of the virtual synchronous generator p and virtual moment of inertia J, calculate the output vector parameter matrix S B .

[0136] The loss function calculation unit 304 is used to make the prediction time domain N p The predicted output of each sampling period approaches the reference signal R(k+1)=0, and the mechanical power increment ΔP m (k) is as small as possible to construct the prediction time domain N for the target p Cost function for internal sampling period data Based on the control time domain N mThe optimized control parameter vector ΔU(k) ​​of the sampling period and the predicted output equation Y of each sampling period P (k+1|k) determines the diagonal matrix and

[0137] The controller construction unit 305 is used to construct a parameter matrix S based on the input vector B , diagonal matrix and the diagonal matrix Building a Model Predictive Controller

[0138] The embodiment of the present disclosure also provides a device for determining the output angular frequency of a virtual synchronous generator control strategy. Figure 4 Schematic diagram of the structure of the device for determining the output angular frequency of the virtual synchronous generator control strategy provided by the embodiment of the present disclosure. Figure 4 As shown, the device 400 for determining the output angular frequency of the virtual synchronous generator control strategy includes a difference calculation unit 401, a change parameter calculation unit 402, a mechanical power change prediction calculation unit 403, a predicted mechanical power calculation unit 403 and an output angular frequency calculation unit 404.

[0139] The difference calculation unit 401 is used to calculate the angular frequency change Δw(k) based on the output angular frequency w(k) of the current cycle and the output angular frequency of the previous cycle, and to calculate the electromagnetic power change ΔP based on the electromagnetic power of the current cycle and the electromagnetic power of the previous cycle. e (k).

[0140] The variable parameter calculation unit 402 is used to adopt Based on the angular frequency change Δw(k) and the electromagnetic power change ΔP e (k), mechanical power y of the current cycle o (k)E p (k+1|k) calculates the mechanical power change parameter E at the next moment p (k+1|k), where is the angular frequency parameter matrix S w , mechanical power parameter matrix I, electromagnetic power parameter matrix S D It is calculated in the process of building the model predictive controller 300 as previously applied to the virtual synchronous generator control strategy.

[0141] The mechanical power change prediction amount calculation unit 402 is used to calculate the mechanical power change prediction amount using the analog prediction controller and mechanical power change parameters determined previously.

[0142] The predicted mechanical power calculation unit 403 is used to calculate the predicted mechanical power of the next cycle based on the mechanical power of the virtual synchronous generator in the current cycle and the predicted amount of mechanical power change.

[0143] The output angular frequency calculation unit 404 is configured to determine the output angular frequency of the next cycle of the virtual synchronous generator based on the predicted mechanical power of the next cycle.

[0144] An embodiment of the present disclosure further provides a control device for a grid-type clean power generation system, which is applied to a grid-type clean power generation system. Figure 5 Schematic diagram of the control device structure of the grid-type clean power generation system provided by the embodiment of the present disclosure. Figure 5 As shown, the control device 500 of the grid-type clean power generation system includes a virtual synchronous generator control strategy calculation unit 501 , a synthetic internal potential calculation unit 502 and a control signal determination unit 503 .

[0145] The virtual synchronous generator control strategy calculation unit 501 is used to determine the virtual angular frequency of the virtual synchronous generator in the next cycle and the virtual voltage peak value of the virtual synchronous generator in the next cycle by using the above method for determining the output angular frequency of the virtual synchronous generator control strategy.

[0146] The synthetic internal potential calculation unit 502 is configured to determine the synthetic internal potential based on the virtual angular frequency and the virtual voltage peak value.

[0147] The control signal determination unit 503 is used to determine a switch control signal for controlling the switch tube of the grid-side converter based on the synthetic internal potential and a control strategy based on a voltage-current dual closed loop.

[0148] The present disclosure also provides a grid-type clean power generation system. Figure 6 This is a schematic diagram of the structure of the grid-type clean power generation system provided by the embodiment of the present disclosure. Figure 6 As shown, the grid-connected clean power generation system includes a clean generator set, a generator-side converter, a grid-side converter, and a filter circuit. The input of the generator-side converter is connected to the output of the clean generator set, and the input of the grid-side converter is connected to the input of the generator-side converter. The output of the grid-side converter is connected to the grid injection point via the filter circuit. Furthermore, the grid-connected clean power generation system includes a controller that executes the control method for the grid-connected clean power generation system as previously analyzed.

[0149] In addition to providing the aforementioned grid-type clean power generation system control method and apparatus, the embodiments of the present disclosure also provide a control device. Figure 7 This is a schematic diagram of the structure of the control device provided by the embodiment of the present disclosure. Figure 7 , which shows a structural schematic diagram of a control device 700 suitable for implementing the embodiments of the present disclosure. Figure 7 The control device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0150] like Figure 7 As shown, the control device 700 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory ROM 702 or a program loaded from a storage device 708 into a random access memory RAM 703. Various programs and data required for the operation of the control device 700 are also stored in the RAM 703. The processing device 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output I / O interface 705 is also connected to the bus 704.

[0151] Typically, the following devices may be connected to the I / O interface 705: an input device 705 including, for example, a touch screen, a touchpad, etc.; an output device 707 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 708 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 709. The communication device 709 may allow the control device 700 to communicate with other devices wirelessly or by wire to exchange data. Figure 7 The control device 700 is shown with various devices, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have instead.

[0152] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 709, or installed from the storage device 708, or installed from the ROM 702. When the computer program is executed by the processing device 701, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0153] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0154] In some embodiments, the client and server can communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0155] The computer-readable medium may be included in the control device, or may exist independently without being incorporated into the control device.

[0156] The computer-readable medium carries one or more programs. When the one or more programs are executed by the control device, the control device is enabled to: obtain the rated active output power of the grid-side converter and the actual active output power when the grid voltage drops; calculate the unbalanced power based on the rated active output power and the actual active output power; adjust the input power based on the current input power and the unbalanced power calculation of the grid-side converter; and determine the virtual angular frequency of the virtual synchronous machine based on the adjusted input power using the virtual frequency regulator in the virtual synchronous machine control strategy.

[0157] Computer program code for performing operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the tester computer, partially on the tester computer, as a stand-alone software package, partially on the tester computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the tester computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0158] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0159] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device that includes the element.

[0160] The foregoing are merely specific embodiments of the present disclosure, intended to enable those skilled in the art to understand and implement the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure is not to be limited to the embodiments described herein, but is to be construed in the broadest manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for constructing a model predictive controller for VSG control strategy, characterized in that: include: Get the predicted time domain N p and control time domain N m The virtual angular frequency w(k) and electromagnetic power P of each sampling period e (k), mechanical power P m (k), and the damping coefficient D of the virtual synchronous generator p and virtual moment of inertia J, where the prediction time domain N p ≤Control time domain N m ; Based on the mechanical characteristic equation and active frequency control equation of the virtual synchronous generator, the output equation and state variable equation of mechanical power are constructed, and the predicted time domain N is obtained. p The mechanical power prediction output equation Y for each sampling period P (k+1|k), the prediction output equation is ΔU(k) ​​is the control time domain N m Mechanical power change vector of the sampling period, S w is the angular frequency parameter matrix, I is the mechanical power parameter matrix, S D is the electromagnetic power parameter matrix, S B is the input vector parameter matrix; Based on the prediction time domain N p and control time domain N m Virtual angular frequency w(k) and electromagnetic power P at each time point e (k), mechanical power P m (k), and the damping coefficient D of the virtual synchronous generator p and virtual moment of inertia J, calculate the output vector parameter matrix S B ; Make the prediction time domain N p The predicted output of each sampling period approaches the reference signal R(k+1)=0, and the mechanical power increment ΔP m (k) is as small as possible to construct the prediction time domain N for the target p Cost function for internal sampling period data And based on the control time domain N m The optimized control parameter vector ΔU(k) ​​of the sampling period and the predicted output equation Y of each sampling period P (k+1|k) determines the diagonal matrix and Based on the input vector parameter matrix S B , diagonal matrix and the diagonal matrix Building a Model Predictive Controller Where T s is the control period of the virtual synchronous generator, A, B, D, m w Represent the state quantity, control quantity, disturbance quantity and output quantity of the system respectively; y o is the controlled output variable.

2. A method for determining the output angular frequency of a virtual synchronous generator control strategy, characterized in that: include: The angular frequency change Δw(k) is calculated based on the output angular frequency of the current cycle and the output angular frequency of the previous cycle, and the electromagnetic power change ΔP is calculated based on the electromagnetic power of the current cycle and the electromagnetic power of the previous cycle. e (k); use Based on the angular frequency change Δw(k) and the electromagnetic power change ΔP e (k), mechanical power y of the current cycle o (k) Calculate the mechanical power change parameter E at the next moment p (k+1|k), where is the angular frequency parameter matrix S w , mechanical power parameter matrix I, electromagnetic power parameter matrix S D calculated in the process of constructing a model predictive controller applied to a virtual synchronous generator control strategy as claimed in claim 1; Calculating a mechanical power change prediction amount based on the analog predictive controller and the mechanical power change parameter determined as claimed in claim 1; The predicted mechanical power of the next cycle is calculated based on the mechanical power of the virtual synchronous generator in the current cycle and the predicted amount of mechanical power change; The output angular frequency of the next cycle of the virtual synchronous generator is determined based on the predicted mechanical power of the next cycle.

3. The method according to claim 2, characterized in that Where T s is the control period of the system.

4. A control method for a grid-type clean power generation system, wherein the grid-type clean power generation system includes a grid-side converter, characterized in that: include: Determine the virtual angular frequency of the virtual synchronous generator in the next cycle and the virtual voltage peak value of the virtual synchronous generator in the next cycle by using the method for determining the output angular frequency of the virtual synchronous generator control strategy according to any one of claims 2 to 3; determining a synthetic internal potential based on the virtual angular frequency and the virtual voltage peak value; Based on the synthetic internal potential, a control strategy based on a voltage-current dual closed loop is adopted to determine a switch control signal for controlling the switch tube of the grid-side converter.

5. A model predictive controller construction device applied to VSG control strategy, characterized in that: include: Parameter acquisition unit, used to obtain the prediction time domain N p and control time domain N m The virtual angular frequency w(k) and electromagnetic power P of each sampling period e (k), mechanical power P m (k), and the damping coefficient D of the virtual synchronous generator p and virtual moment of inertia J, where the prediction time domain N p ≤Control time domain N m ; The virtual synchronous generator model building unit is used to build the output equation and state variable equation of mechanical power based on the mechanical characteristic equation and active frequency control equation of the virtual synchronous generator, and obtain the predicted time domain N p The mechanical power prediction output equation Y for each sampling period P (k+1|k), the prediction output equation is ΔU(k) ​​is the control time domain N m Mechanical power change vector of the sampling period, S w is the angular frequency parameter matrix, I is the mechanical power parameter matrix, S D is the electromagnetic power parameter matrix, S B is the input vector parameter matrix; Matrix calculation unit, used to predict the time domain N p and control time domain N m Virtual angular frequency w(k) and electromagnetic power P at each time point e (k), mechanical power P m (k), and the damping coefficient D of the virtual synchronous generator p and virtual moment of inertia J, calculate the output vector parameter matrix S B ; Loss function calculation unit, used to make the prediction time domain N p The predicted output of each sampling period approaches the reference signal R(k+1)=0, and the mechanical power increment ΔP m (k) is as small as possible to construct the prediction time domain N for the target p Cost function for internal sampling period data Based on the control time domain N m The optimized control parameter vector ΔU(k) ​​of the sampling period and the predicted output equation Y of each sampling period P (k+1|k) determines the diagonal matrix and The controller building block is used to calculate the parameter matrix S based on the input vector B , diagonal matrix and the diagonal matrix Building a Model Predictive Controller (S B T Q y T Q y S B +R u T R u ) - 1 S B T Q y T Q y .

6. A device for determining the output angular frequency of a virtual synchronous generator control strategy, characterized in that: include The difference calculation unit is used to calculate the angular frequency change Δw(k) based on the output angular frequency w(k) of the current cycle and the output angular frequency of the previous cycle, and to calculate the electromagnetic power change ΔP based on the electromagnetic power of the current cycle and the electromagnetic power of the previous cycle. e (k); Variation parameter calculation unit, used to adopt Based on the angular frequency change Δw(k) and the electromagnetic power change ΔP e (k), mechanical power y of the current cycle o (k)E p (k+1|k) calculates the mechanical power change parameter E at the next moment p (k+1|k), where is the angular frequency parameter matrix S w , mechanical power parameter matrix I, electromagnetic power parameter matrix S D calculated in the process of constructing a model predictive controller applied to a virtual synchronous generator control strategy as claimed in claim 1; a mechanical power change prediction amount calculation unit, configured to calculate the mechanical power change prediction amount using the analog prediction controller determined by the method of claim 1 and the mechanical power change parameter; A predicted mechanical power calculation unit, configured to calculate the predicted mechanical power of the next cycle based on the mechanical power of the virtual synchronous generator in the current cycle and the predicted amount of mechanical power change; The output angular frequency calculation unit is used to determine the output angular frequency of the next cycle of the virtual synchronous generator based on the predicted mechanical power of the next cycle.

7. A control device for a grid-type clean power generation system, the grid-type clean power generation system comprising a grid-side converter, characterized in that: include: a virtual synchronous generator control strategy calculation unit, configured to determine a virtual angular frequency of the virtual synchronous generator in the next cycle and a virtual voltage peak value of the virtual synchronous generator in the next cycle by using the method for determining the output angular frequency of the virtual synchronous generator control strategy according to any one of claims 2 to 3; a synthetic internal potential calculation unit, configured to determine a synthetic internal potential based on the virtual angular frequency and the virtual voltage peak value; A control signal determination unit is used to determine, based on the synthetic internal potential, a switch control signal for controlling the switch tube of the grid-side converter by adopting a control strategy based on a voltage-current dual closed loop.

8. A grid-type clean power generation system, characterized in that: It includes a clean generator set, a machine-side converter, a grid-side converter and a filter circuit; the input end of the machine-side converter is connected to the output end of the clean generator set, and the input end of the grid-side converter is connected to the input end of the machine-side converter; the output end of the grid-side converter is connected to the grid injection and grid connection point through the filter circuit; The grid-type clean power generation system further includes a controller, which executes the method according to any one of claims 1 to 4.

9. A control device, characterized in that: comprising a processor and a memory, said memory being configured to store a computer program; When the computer program is loaded by the processor, the processor is caused to execute the method according to any one of claims 1 to 4.

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