Impedance self-adaptive control method for combined network-following and network-constructing converter

Abstract

The invention discloses an impedance self-adaptive control method for a combined type grid-following type and grid-constructing type converter, and relates to the technical field of wind turbine generators. Comprising the steps that S100, two wind driven generators are adopted to construct a combined inverter self-adaptive topological structure; s200, each wind generator is correspondingly connected with a grid-following type inverter and a grid-constructing type inverter through a corresponding rectifier, and the grid-following type inverter and the grid-constructing type inverter are connected with the power grid in parallel in a parallel mode; s300, collecting and processing sample data of the network-following type inverter and the network-constructing type inverter, wherein the sample data comprises machine side control signal parameters and network side control signal parameters; s400, constructing a self-adaptive control model; s500, acquiring a network tracking type error adjustment value by using the network tracking type nonlinear prediction model, and acquiring a network constructing type error adjustment value by using the network constructing type prediction model; and S600, each MPC control module controls the corresponding inverter according to the obtained error adjustment value, the machine side control signal parameter and the network side control signal parameter. Changes of frequency and damping of the power grid are tracked in a self-adaptive mode, positive and negative values of output damping are flexibly adjusted, and the problems of low inertia, negative damping and weak voltage caused by double high can be fundamentally solved.

Description

Technical Field

[0001] The invention relates to the technical field of wind turbine generator sets, and in particular to an impedance adaptive control method for a combined grid-following type and grid-forming type converter. Background Art

[0002] With the rapid development of renewable energy, the dual-high characteristics of high-proportion renewable energy and high-proportion power electronic equipment are prominent, showing low inertia, negative damping and weak voltage characteristics, which poses a huge challenge to the safe and stable operation of the power system. At present, most new energy units are connected to the grid using a grid-following control method based on a phase-locked loop. It has good stability performance under strong power grids, but under weak power grids, it will cause the interaction between new energy and the power grid and cause new subsynchronous oscillation instability problems. Summary of the invention

[0003] In view of one or more of the above existing problems, the present invention proposes a combined grid-following and grid-forming converter impedance adaptive control method.

[0004] The present application provides a combined grid-following type grid-forming type converter impedance adaptive control method, the control method comprising the following steps:

[0005] S100. Using two wind turbines to construct a combined inverter adaptive topology structure, the adaptive topology structure includes two wind turbines on the motor side, a combined inverter and a power grid, and the combined inverter includes a grid-following inverter and a grid-building inverter;

[0006] S200. Each wind turbine generator is connected to a grid-following inverter and a grid-building inverter respectively through a corresponding rectifier, and the grid-following inverter and the grid-building inverter are connected in parallel with the power grid in a parallel manner;

[0007] S300. Collect and process sample data of the grid-following inverter and the grid-building inverter, wherein the sample data includes machine-side control signal parameters and grid-side control signal parameters;

[0008] S400. Build an adaptive control model, including:

[0009] The sample data of grid-following inverter and grid-building inverter are divided into two subsets, and a nonlinear prediction model is established based on the two subsets, which are respectively a machine-side control signal parameter data set and a grid-side control signal parameter data set;

[0010] The nonlinear prediction model includes a network-following nonlinear prediction model and a network-building nonlinear prediction model, each nonlinear prediction model is connected to a corresponding MPC control module, and the MPC control module is a network-following MPC control module and a network-building MPC control module;

[0011] S500. Using the network-type nonlinear prediction model to obtain the network-type error adjustment value, using the network-type prediction model to obtain the network-type error adjustment value;

[0012] S600. The grid-following MPC control module controls the grid-following inverter according to the acquired grid-following error adjustment value, the grid-following inverter machine-side control signal parameters and the grid-side control signal parameters; and the grid-building MPC control module controls the grid-building inverter according to the acquired grid-building error adjustment value, the grid-building inverter machine-side control signal parameters and the grid-side control signal parameters.

[0013] As an achievable implementation method, step S300 collects sample data of the grid-following inverter, including the following method:

[0014] S301. Grid-following inverter side voltage u a 、u b 、u c and current i a 、i b 、i c After abc / dq transformation, the corresponding d and q axis voltages u are obtained. d * 、u q * and current i d 、i q ;

[0015] S302.d, q-axis voltage u d * 、u q * The corresponding phase angle θ is obtained through the phase-locked loop PLL ;

[0016] S303.d, q axis current i d 、i q Respectively with the set reference current i dref 、i qref The output difference is compared and converted into voltage u through the current loop output. d 、u q , the formula is as follows:

[0017]

[0018] In the formula, k p , k i are the proportional gain and integral gain of the current loop PI regulator, i dref 、i qref i d and i q A given reference value;

[0019] S304. Output conversion voltage u d 、u q and phase angle θ PLL At the same time, the reference voltage u is obtained through dq / abc conversion processing aref * 、u bref * 、u cref * .

[0020] As an achievable implementation method, step S300 collects and processes sample data of the grid-connected inverter, including the following method:

[0021] S310. Grid-connected inverter side output voltage u o The three-phase voltage u oa 、u ob 、u oc After the phase-locked loop, u is obtained o The phase angle θ1;

[0022] S311.u o The phase angle θ1, u o The three-phase voltage u oa 、u ob 、u oc and u o The three-phase current i oa 、i ob 、i oc After abc / dq transformation, the corresponding d and q axis voltage u is obtained. od 、u oq and current i od 、i oq ;

[0023] S312.u o The voltage and current of the d and q axes are calculated by the formula to obtain the active power P e and reactive power Q e , the formula is as follows:

[0024] P e =1.5(u od i od +u od i od )

[0025] Q e =1.5(u oq i od -u od i oq );

[0026] S313. Obtained active power Pe , reactive power Q e , Active power given value P set And reactive power set value Q set The inverter is controlled by the virtual synchronous generator (VSG) controller to obtain the phase angle θ2 and voltage amplitude E of the virtual synchronous motor. m ;

[0027] S314. Phase angle θ2 and voltage amplitude E m Generate reference voltage E through three-phase voltage reference matrix aref 、E bref 、E cref , reference voltage E aref 、E bref 、E cref , the current i output from the inverter grid side La 、i Lb 、i Lc And the inverter voltage u after RL filtering oa 、u ob 、u oc The PWM reference voltage u is generated through voltage and current double closed-loop control aref 、u bref 、u cref .

[0028] As an achievable implementation method, S500. Using a network-following nonlinear prediction model to obtain a network-following error adjustment value, and using a network-building prediction model to obtain a network-building error adjustment value, specifically includes the following methods:

[0029] S501. Design a generalized predictive controller C based on the collected machine-side control signal parameters and grid-side control signal parameters i (i=1,2), generalized predictive controller C i (i=1,2) Calculate the control signal u corresponding to the output i (k)(i=1,2) to the switching mechanism;

[0030] S502. The switching mechanism selects the optimal controller according to the error switching strategy to output the actual control signal u(k) of the system, where k refers to a certain moment;

[0031] S503. Establish a generalized virtual prediction module according to the topological structure, the generalized virtual prediction module receives the actual control signal u(k) output by the switching mechanism, and uses the set expected prediction value and the corresponding generalized virtual prediction module M i (i=1,2) output value calculates the output error e at time k+1 i (k+1) as the generalized predictive controller C i (i=1,2) input.

[0032] As an achievable implementation method, step S502 uses a switching mechanism to select the optimal controller to output the actual control signal u(k) of the system according to the error switching strategy, including: selecting the prediction model corresponding to the minimum performance index as the optimal model according to the error performance index formula, and using the control action generated by the optimal controller corresponding to the selected optimal model as the actual control signal u(k) of the system, wherein the error performance index formula is as follows:

[0033] Among them, J m (k) is the error performance index, Ψ(k)=[ω T (k),||ω(k)||] T , ω(k) is the system regression vector, ||ω(k)|| is the bi-norm composed of the system regression vector, e m (k) is the output error of the virtual model in the mth subset at time k.

[0034] As an achievable implementation method, the prediction model of the minimum variance output of each model is obtained according to the established generalized virtual prediction model:

[0035] in, is the system's predicted output value vector for the next moment, Y m,p is the output value vector of the system at the previous moment, ΔU m is the control increment of the prediction model, expressed as U m (k)-U m (k-1) is calculated, U m (k) is the actual control signal of the mth prediction model, and the value range of m is 1 and 2. 2×2 is the parameter matrix in the system, E m,1 is the error compensation term of the nonlinear part of the mth model, which is obtained by the error between the predicted value and the actual value.

[0036] As an achievable implementation, the feedback formula in step S503 is as follows:

[0037] y(k+1) represents the output signal of the system at time k+1, which represents the torque control of the motor-side inverter and the impedance control of the grid-side inverter. T (k),||ω(k)||] T , is a decreasing sequence of positive scalars.

[0038] The present application provides a memory, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned combined grid-following type and grid-forming type converter impedance adaptive control method when running.

[0039] The beneficial effects of the present invention are:

[0040] The combined grid-following and grid-forming converter impedance adaptive control method provided by the present invention can adaptively track changes in grid frequency and damping, flexibly adjust the positive and negative values ​​of output damping, and can fundamentally solve the problems of low inertia, negative damping and weak voltage caused by double highs. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A hybrid grid-connected model for combined grid-following type grid-connected converter impedance adaptive control method;

[0042] Figure 2 It is a schematic diagram of the control principle of the grid-following inverter in the impedance adaptive control method of the combined grid-following type grid-type converter;

[0043] Figure 3 It is a schematic diagram of the control principle of the grid-forming inverter in the impedance adaptive control method of the combined grid-following grid-forming converter;

[0044] Figure 4 It is a schematic diagram of the control principle of the impedance adaptive control method of the combined grid-following type grid-type converter;

[0045] Figure 5 A specific schematic diagram of predictive control of a combined grid-following type grid-type converter impedance adaptive control method;

[0046] Figure 6 It is a schematic diagram of the waveform of the grid-connected voltage and current of the grid-following inverter;

[0047] Figure 7 A schematic diagram of the waveforms of the grid-connected voltage and current of the grid-connected inverter;

[0048] Figure 8 This is the control principle diagram of grid-connected inverter with impact-free switching between grid and off-grid;

[0049] Fig. 9 This is the grid-connected and off-grid switching waveform diagram of the impedance adaptive control method for the combined grid-following type grid-type converter.

[0050] The above drawings show clear embodiments of the present disclosure, which will be described in more detail below. These drawings and text descriptions are not intended to limit the scope of the present disclosure in any way, but to illustrate the concepts of the present disclosure to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0051] The technical solution of the application is further described in detail below with reference to the accompanying drawings.

[0052] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0054] The following is combined with Figure 1-9 , some embodiments of the present invention are described in detail. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0055] The present application provides a combined grid-following type grid-forming type converter impedance adaptive control method, the control method comprising the following steps:

[0056] S100. Using two wind turbines to construct a combined inverter adaptive topology structure, the adaptive topology structure includes two wind turbines on the motor side, a combined inverter and a power grid, and the combined inverter includes a grid-following inverter and a grid-building inverter;

[0057] S200. Each wind turbine generator is connected to a grid-following inverter and a grid-building inverter respectively through a corresponding rectifier, and the grid-following inverter and the grid-building inverter are connected in parallel with the power grid in a parallel manner;

[0058] S300. Collect and process sample data of the grid-following inverter and the grid-building inverter, wherein the sample data includes machine-side control signal parameters and grid-side control signal parameters;

[0059] It should be noted that the sample data of the grid-connected inverter is collected, including the following methods:

[0060] S301. Grid-following inverter side voltage u a 、u b 、u c and current i a 、ib 、i c After abc / dq transformation, the corresponding d and q axis voltages u are obtained. d * 、u q * and current i d 、i q ;

[0061] S302.d, q-axis voltage u d * 、u q * The corresponding phase angle θ is obtained through the phase-locked loop PLL ;

[0062] S303.d, q axis current i d 、i q Respectively with the set reference current i dref 、i qref The output difference is compared and converted into voltage u through the current loop output. d 、u q , the formula is as follows:

[0063]

[0064] In the formula, k p , k i are the proportional gain and integral gain of the current loop PI regulator, i dref 、i qref i d and i q A given reference value;

[0065] S304. Output conversion voltage u d 、u q and phase angle θ PLL At the same time, the reference voltage u is obtained through dq / abc conversion processing aref * 、u bref * 、u cref * .

[0066] It should be noted that collecting and processing sample data of a grid-connected inverter includes the following methods:

[0067] S310. Grid-connected inverter side output voltage u o The three-phase voltage u oa 、u ob 、u oc After the phase-locked loop, u is obtained o The phase angle θ1;

[0068] S311.u o The phase angle θ1, u o The three-phase voltage u oa 、u ob 、u oc and u o The three-phase current i oa 、i ob 、i oc After abc / dq transformation, the corresponding d and q axis voltage u is obtained. od 、u oq and current i od 、i oq ;

[0069] S312.u o The voltage and current of the d and q axes are calculated by the formula to obtain the active power P e and reactive power Q e , the formula is as follows:

[0070] P e =1.5(u od i od +u od i od )

[0071] Q e =1.5(u oq i od -u od i oq );

[0072] S313. Obtained active power P e , reactive power Q e , Active power given value P set And reactive power set value Q set The inverter is controlled by the virtual synchronous generator (VSG) controller to obtain the phase angle θ2 and voltage amplitude E of the virtual synchronous motor. m The output principle expression of the grid-connected inverter using virtual synchronous machine control is:

[0073] Active power - frequency control:

[0074] Reactive power - amplitude control: E m =U n +[Q set +D Q (U n -U om )-Q e ] / Ks (2)

[0075] Where, T m is the mechanical torque, T e is the electromagnetic torque, ω n is the rated angular frequency, J is the moment of inertia, D P is the active power droop coefficient; U n is the rated voltage effective value, Q set is the system reactive power given value, Q e is the real-time reactive power of the system, U om is the peak voltage at the output terminal, D Q is the reactive droop coefficient, K is the excitation integral coefficient, and the control strategy block diagram of the grid-connected inverter can be obtained.

[0076] S314. Phase angle θ2 and voltage amplitude E m Generate reference voltage E through three-phase voltage reference matrix aref 、E bref 、E cref , reference voltage E aref 、E bref 、E cref , the current i output from the inverter grid side La 、i Lb 、i Lc And the inverter voltage u after RL filtering oa 、u ob 、u oc The PWM reference voltage u is generated through voltage and current double closed-loop control aref 、u bref 、u cref S400. Construct an adaptive control model, including:

[0077] The sample data of grid-following inverter and grid-building inverter are divided into two subsets, and a nonlinear prediction model is established based on the two subsets, which are respectively a machine-side control signal parameter data set and a grid-side control signal parameter data set;

[0078] The nonlinear prediction model includes a network-following nonlinear prediction model and a network-building nonlinear prediction model, each nonlinear prediction model is connected to a corresponding MPC control module, and the MPC control module is a network-following MPC control module and a network-building MPC control module;

[0079] S500. Using the network-type nonlinear prediction model to obtain the network-type error adjustment value, using the network-type prediction model to obtain the network-type error adjustment value;

[0080] The specific methods that should be explained include the following:

[0081] S501. Design a generalized predictive controller C based on the collected machine-side control signal parameters and grid-side control signal parametersi (i=1,2), generalized predictive controller C i (i=1,2) Calculate the control signal u corresponding to the output i (k)(i=1,2) to the switching mechanism;

[0082] S502. The switching mechanism selects the optimal controller according to the error switching strategy to output the actual control signal u(k) of the system, where k refers to a certain moment;

[0083] Specifically, it includes: selecting the prediction model corresponding to the minimum performance index as the optimal model according to the error performance index formula, and taking the control action generated by the optimal controller corresponding to the selected optimal model as the actual control signal u(k) of the system, wherein the error performance index formula is as follows:

[0084] Among them, J m (k) is the error performance index, Ψ(k)=[ω T (k),||ω(k)||] T , ω(k) is the system regression vector, ||ω(k)|| is the bi-norm composed of the system regression vector, e m (k) is the output error of the virtual model in the mth subset at time k.

[0085] S503. Establish a generalized virtual prediction module according to the topological structure, the generalized virtual prediction module receives the actual control signal u(k) output by the switching mechanism, and uses the set expected prediction value and the corresponding generalized virtual prediction module M i (i=1,2) output value calculates the output error e at time k+1 i (k+1) as the generalized predictive controller C i (i=1,2) input.

[0086] Specifically, it should be explained that the prediction model with the minimum variance output of each model is obtained according to the established generalized virtual prediction model: From this we can know that

[0087]

[0088] in, is the system's predicted output value vector for the next moment, Y m,p is the output value vector of the system at the previous moment, ΔU m is the control increment of the prediction model, expressed as U m (k)-U m (k-1) is calculated, U m (k) is the actual control signal of the mth prediction model, and the value range of m is 1 and 2.2×2 is the parameter matrix in the system, E m,1 is the error compensation term of the nonlinear part of the mth model, which is obtained by the error between the predicted value and the actual value. From this, it can be inferred that Combined with the attached drawings, it can be seen Corresponding to y(k+1),Y m,p Corresponding to Y m,p (k+1), which is y in the figure 1,p (k+1), and y 2,p (k+1), E m,1 Corresponding to e m (k+1), that is, e1(k+1) and e2(k+1) in the figure. The feedback formula is as follows: y(k+1) represents the output signal of the system at time k+1, which represents the torque control of the motor-side inverter and the impedance control of the grid-side inverter. T (k),||ω(k)||] T , is a decreasing sequence of positive scalars.

[0089] S600. The grid-following MPC control module controls the grid-following inverter according to the acquired grid-following error adjustment value, the grid-following inverter machine-side control signal parameters and the grid-side control signal parameters; and the grid-building MPC control module controls the grid-building inverter according to the acquired grid-building error adjustment value, the grid-building inverter machine-side control signal parameters and the grid-side control signal parameters.

[0090] As shown in the figure, r(k) in the figure is the expected tracking signal for setting the expected prediction value, which makes y(k+1) transition to the set value in a smooth manner; C i (i=1,2) is a generalized predictive controller, which uses the switching mechanism to select the optimal controller according to the error switching strategy to output the actual control signal u(k) of the system, thereby calculating the control action u i (k)(i=1,2); the switching mechanism calculates the performance index based on the error between the actual output of the system and the output of the prediction model of each subset, and selects the prediction model corresponding to the minimum performance index as the optimal model according to the calculation result. The control signal corresponding to the optimal model is used as the actual control signal u(k) of the system, and then the prediction output y m,p (k+1), in this application, m is 1 and 2, and then subtracted from y(k+1) to obtain e i (k+1), where e i(k+1) is the output error of the virtual model in the i-th (i=1,2) subset at time k, as shown in the figure, that is, e1(k+1) and e2(k+1) as well as the machine-side control signal and the grid-side control signal are input to the corresponding MPC control module to respectively control the torque of the motor-side PWM converter and the impedance of the grid-side PWM converter, thereby accelerating the convergence speed of the parameter estimation value in the controller and overcoming the multi-model degradation problem; and selecting the optimal controller at each moment as the controller of the nonlinear system through the error switching strategy, thereby improving the transient performance and steady-state performance of the system.

[0091] When a hybrid system consisting of a grid-following inverter and a grid-building inverter is connected to the grid, the grid-following inverter plays a leading role when the grid is strong, and the grid-building inverter plays a leading role when the grid is weak or disconnected.

[0092] This method combines grid-following and grid-forming converters to adaptively track the changes in grid frequency and damping, and flexibly adjust the positive and negative values ​​of output damping. Grid-following converters are based on phase-locked synchronous control, and use phase-locked loops to track the grid phase and stable output in real time, showing negative damping to the outside. Grid-following control shows superior performance under strong grid conditions. Its control structure is simple and easy to implement, and can produce higher power quality and high utilization rate of new energy. However, it is difficult to support changes in grid inertia support and damping under weak grid conditions. Grid-forming control achieves stable output of given frequency and voltage amplitude based on self-synchronous control. The control loop includes active-frequency loop and reactive-voltage loop. Among them, the active-frequency loop is an approximate simulation of the speed regulation system differential characteristics and rotor motion equation of the synchronous generator, and the reactive-voltage loop is an approximate simulation of the excitation system differential characteristics and stator voltage equation of the synchronous generator. The two are combined to generate a reference AC waveform, which has a self-synchronous matching mechanism with the grid phase.

[0093] Therefore, this method can fundamentally solve the low inertia, negative damping and weak voltage problems caused by double highs through combined control and impedance model adaptive control algorithm. In order to correctly compensate for the impedance, impedance predictive control is added on the basis of the combined control strategy. The change space of unknown parameters is divided into multiple subsets according to prior knowledge, and multiple generalized impedance prediction models are established in each subset; secondly, the unknown parameters are identified according to the switching signal of the previous moment by using the recursive least squares identification algorithm, and the virtual model parameters of each subset are obtained by using the identification results of the model with constrained weighting coefficients, and the predictive controller of each subset is designed based on this, so as to accelerate the convergence speed of the parameter estimation value in the controller and overcome the multi-model degradation problem; thirdly, the optimal sub-controller at each moment is selected as the controller of the nonlinear system through the error switching strategy, so as to improve the transient performance and steady-state performance of the system; finally, the stability analysis of the designed controller is carried out.

[0094] As shown in the figure, Figure 6 The grid-connected voltage and current waveforms of the grid-following inverter are shown below: Figure 7 The grid-connected voltage and current of the grid-connected inverter; Figure 8 This is the waveform diagram of the grid-connected and off-grid impact-free switching of the grid-connected inverter. Fig. 9 The following figure shows the switching waveform of the grid-following and grid-building inverter hybrid system. As can be seen from the figure below, due to the effect of the adaptive controller, the current waveforms of the two converter switching and grid-connected and off-grid switching are impact-free switching, which greatly reduces the impact on the power grid.

[0095] The present application also provides a memory, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned combined grid-following type grid-forming type converter impedance adaptive control method when running. Based on such an understanding, the present disclosure implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments.

[0096] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention. The above are only some implementation methods of the present invention. For those of ordinary skill in the art, several variations and improvements can be made without departing from the creative concept of the present invention, and these all belong to the protection scope of the present invention.

Claims

1. A combined grid-following and grid-forming converter impedance adaptive control method, characterized in that: The control method comprises the following steps: S100. Using two wind turbines to construct a combined inverter adaptive topology structure, the adaptive topology structure includes two wind turbines on the motor side, a combined inverter and a power grid, and the combined inverter includes a grid-following inverter and a grid-building inverter; S200. Each wind turbine generator is connected to a grid-following inverter and a grid-building inverter respectively through a corresponding rectifier, and the grid-following inverter and the grid-building inverter are connected in parallel with the power grid in a parallel manner; S300. Collect and process sample data of the grid-following inverter and the grid-building inverter, wherein the sample data includes machine-side control signal parameters and grid-side control signal parameters; S400. Build an adaptive control model, including: The sample data of grid-following inverter and grid-building inverter are divided into two subsets, and a nonlinear prediction model is established based on the two subsets, which are respectively a machine-side control signal parameter data set and a grid-side control signal parameter data set; The nonlinear prediction model includes a network-following nonlinear prediction model and a network-building nonlinear prediction model, each nonlinear prediction model is connected to a corresponding MPC control module, and the MPC control module is a network-following MPC control module and a network-building MPC control module; S500. Using the network-type nonlinear prediction model to obtain the network-type error adjustment value, using the network-type prediction model to obtain the network-type error adjustment value; S600. The grid-following MPC control module controls the grid-following inverter according to the acquired grid-following error adjustment value, the grid-following inverter machine-side control signal parameters and the grid-side control signal parameters; and the grid-building MPC control module controls the grid-building inverter according to the acquired grid-building error adjustment value, the grid-building inverter machine-side control signal parameters and the grid-side control signal parameters.

2. The impedance adaptive control method of the combined grid-following and grid-forming converter according to claim 1 is characterized in that: Step S300 collects sample data of the grid-following inverter, including the following methods: S301. Grid-following inverter side voltage u a 、u b 、u c and current i a 、i b 、i c After abc / dq transformation, the corresponding d and q axis voltages u are obtained. d * 、u q * and current i d 、i q ; S302.d, q-axis voltage u d * 、u q * The corresponding phase angle θ is obtained through the phase-locked loop PLL ; S303.d, q axis current i d 、i q Respectively with the set reference current i dref 、i qref The output difference is compared and converted into voltage u through the current loop output. d 、u q , the formula is as follows: In the formula, k p , k i are the proportional gain and integral gain of the current loop PI regulator, i dref 、i qref i d and i q A given reference value of S304. Output conversion voltage u d 、u q and phase angle θ PLL At the same time, the reference voltage u is obtained through dq / abc conversion processing aref * 、u bref * 、u cref * .

3. The impedance adaptive control method of the combined grid-following type grid-forming type converter according to claim 1 is characterized in that Step S300 collects and processes sample data of the grid-connected inverter, including the following methods: S310. Grid-connected inverter side output voltage u o The three-phase voltage u oa 、u ob 、u oc After the phase-locked loop, u is obtained o The phase angle θ1; S311.u o The phase angle θ1, u o The three-phase voltage u oa 、u ob 、u oc and u o The three-phase current i oa 、i ob 、i oc After abc / dq transformation, the corresponding d and q axis voltage u is obtained. od 、u oq and current i od 、i oq ; S312.u o The voltage and current of the d and q axes are calculated by the formula to obtain the active power P e and reactive power Q e , the formula is as follows: P e =1.5(in od and od +in od and od ) Q e =1.5(u oq i od -u od i oq ); S313. Obtained active power P e , reactive power Q e , Active power given value P set And reactive power set value Q set The inverter is controlled by the virtual synchronous generator (VSG) controller to obtain the phase angle θ2 and voltage amplitude E of the virtual synchronous motor. m ; S314. Phase angle θ2 and voltage amplitude E m Generate reference voltage E through three-phase voltage reference matrix aref 、E bref 、E cref , reference voltage E aref 、E bref 、E cref , the current i output from the inverter grid side La 、i Lb 、i Lc And the inverter voltage u after RL filtering oa 、u ob 、u oc The PWM reference voltage u is generated through voltage and current double closed-loop control aref 、u bref 、u cref .

4. The impedance adaptive control method of the combined grid-following and grid-forming converter according to claim 1 is characterized in that: S500. Using the network-following nonlinear prediction model to obtain the network-following error adjustment value, and using the network-building prediction model to obtain the network-building error adjustment value, specifically including the following methods: S501. Design a generalized predictive controller C based on the collected machine-side control signal parameters and grid-side control signal parameters i (i=1,2), generalized predictive controller C i (i=1,2) Calculate the control signal u corresponding to the output i (k)(i=1,2) to the switching mechanism; S502. The switching mechanism selects the optimal controller according to the error switching strategy to output the actual control signal u(k) of the system, where k refers to a certain moment; S503. Establish a generalized virtual prediction module according to the topological structure, the generalized virtual prediction module receives the actual control signal u(k) output by the switching mechanism, and uses the set expected prediction value and the corresponding generalized virtual prediction module M i (i=1,2) output value calculates the output error e at time k+1 i (k+1) as the generalized predictive controller C i (i=1,2) input.

5. The impedance adaptive control method of the combined grid-following and grid-forming converter according to claim 4 is characterized in that: Step S502 uses the switching mechanism to select the optimal controller to output the actual control signal u(k) of the system according to the error switching strategy, including: selecting the prediction model corresponding to the minimum performance index as the optimal model according to the error performance index formula, and taking the control action generated by the optimal controller corresponding to the selected optimal model as the actual control signal u(k) of the system, wherein the error performance index formula is as follows: Among them, J m (k) is the error performance index, Ψ(k)=[ω T (k),||ω(k)||] T , ω(k) is the system regression vector, ||ω(k)|| is the bi-norm composed of the system regression vector, e m (k) is the output error of the virtual model in the mth subset at time k.

6. The impedance adaptive control method of the combined grid-following and grid-forming converter according to claim 4 is characterized in that: According to the established generalized virtual prediction model, the prediction model with the minimum variance output of each model is obtained as follows: in, is the system's predicted output value vector for the next moment, Y m,p is the output value vector of the system at the previous moment, ΔU m is the control increment of the prediction model, expressed as U m (k)-U m (k-1) is calculated, U m (k) is the actual control signal of the mth prediction model, and the value range of m is 1 and 2. 2×2 is the parameter matrix in the system, E m,1 is the error compensation term of the nonlinear part of the mth model, which is obtained by the error between the predicted value and the actual value.

7. The impedance adaptive control method of the combined grid-following and grid-forming converter according to claim 4 is characterized in that: The feedback formula in step S503 is as follows: y(k+1) represents the output signal of the system at time k+1, which represents the torque control of the motor-side inverter and the impedance control of the grid-side inverter. T (k),||ω(k)||] T , is a decreasing sequence of positive scalars.

8. A memory, characterized in that: A computer program is stored in the memory, wherein the computer program is configured to execute the impedance adaptive control method of the combined grid-following type grid-forming converter according to any one of claims 1 to 7 when running.

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