Converter control method and power grid system

Abstract

The invention discloses a control method of a current converter and a power grid system, the control method is applied to control of the current converter in the power grid system, and the control method comprises the following steps: constructing a self-adaptive hyper-local model which comprises a self-adaptive input gain and a hyper-local model, the hyper-local model comprises physical parameters in a controlled system and disturbance factors in a power grid system; an online neural network estimator is adopted to estimate the hyper-local model and the adaptive input gain, and dependence of the hyper-local model and the adaptive input gain on system parameters is eliminated; and predicting an output current of the converter based on the super-local model and the adaptive input gain, generating a voltage vector of the converter based on the predicted current of the converter, and controlling the converter by using the voltage vector. According to the invention, current tracking and neutral point voltage balance of the converter can be realized, and parameter-free operation of the control system is realized.

Description

Technical Field

[0001] The present application relates to the technical field of converter equipment, and in particular to a converter control method and a power grid system. Background Art

[0002] In the field of renewable energy generation and smart grid, traditional converter control strategies rely on preset mathematical models, which are often difficult to cope with in the face of complex and changing external environments. Existing predictive control methods still rely on multiple physical parameters of the power grid system (including capacitance, inductance and resistance parameters). When the inductance parameters are greatly mismatched, the control performance of the existing predictive control methods will be significantly reduced. When the inductance parameters are unknown, the existing predictive control methods are difficult to apply. Summary of the invention

[0003] In order to solve the deficiencies of the prior art, this application adopts the following technical solutions: The present application provides a control method for a converter, the control method being applied to control the converter in a power grid system, the control method comprising the following steps: Constructing an adaptive hyperlocal model, the adaptive hyperlocal model comprising an adaptive input gain and a hyperlocal model, the hyperlocal model comprising physical parameters in the controlled system and disturbance factors in the power grid system; An online neural network estimator is used to estimate the hyperlocal model and the adaptive input gain, thereby eliminating the dependence of the hyperlocal model and the adaptive input gain on system parameters; The output current of the converter is predicted based on the hyperlocal model and the adaptive input gain, a voltage vector of the converter is generated based on the predicted current of the converter, and the converter is controlled using the voltage vector.

[0004] In summary, according to the above description, the present application provides a control method for a converter, which adopts an online neural network estimator to realize online estimation of a hyperlocal model and an adaptive input gain, realizes real-time update of parameters within the hyperlocal model, and eliminates the dependence of the hyperlocal model and the adaptive input gain on system parameters, thereby realizing parameter-free operation of the control system; and also obtains a voltage vector for controlling the converter based on the predicted current of the converter, thereby realizing the balance of the neutral point voltage of the converter.

[0005] Furthermore, the control method further includes: The hyperlocal model is expressed by the following formula: ; in, represents the weight coefficient matrix, is the activation function, is the estimation error, represent The system output current in the coordinate system; The weight coefficient matrix is ​​updated online, and its update rate is expressed by the following formula: ; in, represents the estimated value of the weight coefficient matrix, is the estimated value of the system output current information, , and are all adjustable weight coefficient matrices, Representative Matrix The transposed matrix of represent The system input voltage in the coordinate system, represents the adaptive input gain; The weight coefficient matrix is ​​discretized to obtain a current prediction equation.

[0006] Furthermore, the weight coefficient matrix is ​​discretized by using the first-order Euler discretization method, and the current prediction equation obtained is expressed by the following formula: ; In the formula, Indicates the control period.

[0007] Further, eliminating the dependence of the adaptive input gain on system parameters includes: The update law of the adaptive input gain is designed, and the update law of the adaptive input gain is expressed by the following formula: ; In the formula, the parameters represents the adaptive input gain, parameter Representation parameters The estimated value of express Output current information of the axis, express The predicted output current information of the axis, express Shaft voltage information, Indicates the control period.

[0008] Furthermore, the control method further includes: Introducing the step size factor, the update law of the adaptive input gain is expressed by the following formula: ; Wherein, p represents the step size factor.

[0009] Furthermore, the control method further includes: Design a neutral point voltage update law, which is expressed by the following formula: ; Where p represents the step size factor, Representatives The estimated value of , C dc Indicates the bus capacitance on the DC side; The neutral point voltage of the power grid system is predicted by the following formula: ; ; In the formula, represents the estimated value of the neutral point voltage, Indicates the neutral point voltage, represents the neutral point voltage update law, Three switch expressions representing the voltage vector used; delay compensation of the converter is achieved to achieve the control target of neutral point voltage balance.

[0010] Furthermore, the control method further includes: Design a sequential value function, which is expressed by the following formula: ; In the formula, It represents the requirements for current tracking quality. represents the balance requirement for the neutral point voltage, and It represents the requirement for switching frequency. is the reference value, That is, using the voltage vector The predicted value obtained later is is the symbolic function, Represents the number of switch changes; According to the predicted current of the converter and the predicted value of the neutral point voltage, based on the sequential value function, the voltage vector of the converter is screened, the optimal voltage vector is selected, and the converter is controlled using the optimal voltage vector.

[0011] Furthermore, the control method further includes: When the predicted neutral point voltage value is less than the neutral point voltage threshold, the cost function in the sequential value function is used to Selecting an optimal vector, and if the optimal vector is a long vector or a medium vector, directly applying it to the converter; If the optimal vector is a small vector, the cost function in the sequential value function is used Select the optimal switching state that makes the midpoint voltage closer to zero; If it is a zero vector, the cost function in the sequential value function is used Select the optimal switching state that minimizes the switching frequency.

[0012] Furthermore, the control method further includes: When the predicted neutral point voltage value exceeds the neutral point voltage threshold, the cost function in the sequential value function is used. and The optimal switching state is selected and applied to the converter.

[0013] In a second aspect, the present application further provides a power grid system, the power grid system comprising a converter, and the power grid system applies the control method of the converter described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flowchart of a method for controlling a converter provided in one embodiment of the present application; Figure 2 A block diagram of the operation of a control method for a converter provided by an embodiment of the present application in practical application; Figure 3 A topological diagram of a converter control method provided in an embodiment of the present application applied to a three-level NPC inverter; Figure 4 A schematic diagram of the spatial distribution of voltage vectors in a converter control method provided in one embodiment of the present application; Figure 5 A schematic diagram of a flow chart of a control method for a converter provided by an embodiment of the present application in a specific application; Figure 6 A schematic diagram of the performance of a converter control method provided by an embodiment of the present application, a traditional finite set model predictive control method, a model-free predictive control method based on an extended state observer, and a model-free control method based on an integral sliding mode observer under the condition of matching simulation parameters; Figure 7 A schematic diagram of the performance of a converter control method provided by an embodiment of the present application, a traditional finite set model predictive control method, a model-free predictive control method based on an extended state observer, and a model-free control method based on an integral sliding mode observer under the condition of input gain mismatch; Figure 8A schematic diagram of the performance of a converter control method provided by an embodiment of the present application, a traditional finite set model predictive control method, a model-free predictive control method based on an extended state observer, and a model-free control method based on an integral sliding mode observer under the condition of inductor parameter mismatch; Fig. 9 It is a schematic diagram of the comparison results of four control methods under different degrees of parameter mismatch conditions; Fig.10 A schematic diagram of an inverter control method provided in the present application for online adjustment of input gain when the real inductance value is 10mH and the value is adapted 50% up and down; Fig.11a It is a schematic diagram of the performance of the traditional finite set model predictive control method under the condition of parameter balancing for the power conversion experimental platform based on 3L-NPC; Fig.11b It is a schematic diagram of the performance of the model-free predictive control method based on the extended state observer under the condition of parameter balancing for the power conversion experimental platform based on 3L-NPC; Fig.11c It is a schematic diagram of the performance of the model-free control method based on the integral sliding mode observer under the condition of parameter balancing for the power conversion experimental platform based on 3L-NPC; Fig.11d A schematic diagram of the performance of a converter control method provided by the present application based on a 3L-NPC power conversion experimental platform under parameter balancing; Fig.12a Schematic diagram of the performance of the traditional finite set model predictive control method under parameter mismatch in the power conversion experimental platform based on 3L-NPC; Figure 12b Schematic diagram of the performance of the model-free predictive control method based on the extended state observer under the condition of parameter mismatch in the power conversion experimental platform based on 3L-NPC; Fig.12c Schematic diagram of the performance of the model-free control method based on the integral sliding mode observer under the condition of parameter mismatch on the power conversion experimental platform based on 3L-NPC; Fig.12d The figure is a schematic diagram of the performance of a converter control method provided by the present application in the case of parameter mismatch on a power conversion experimental platform based on 3L-NPC. DETAILED DESCRIPTION

[0015] The present application will be described in detail below in conjunction with the specific implementation modes shown in the accompanying drawings, but these implementation modes do not limit the present application. Structural, methodological, or functional changes made by ordinary technicians in the field based on these implementation modes are included in the protection scope of the present application.

[0016] In order to solve the deficiencies of the prior art, the present application provides a control method of a converter applied to the control of a converter in a power grid system, such as Figure 1 As shown, the control method includes the following steps: Step S11, constructing an adaptive hyperlocal model, the adaptive hyperlocal model including an adaptive input gain and a hyperlocal model, the hyperlocal model including physical parameters in the controlled system and disturbance factors in the power grid system; Step S12, using an online neural network estimator to estimate the hyperlocal model and the adaptive input gain, eliminating the dependence of the hyperlocal model and the adaptive input gain on system parameters; Step S13: predicting the output current of the converter based on the super-local model and the adaptive input gain, generating a voltage vector of the converter based on the predicted current of the converter, and controlling the converter using the voltage vector.

[0017] Specifically, taking the midpoint clamped three-level converter topology as an example, the model-based predictive control equation can be expressed as: (1); In the formula, R represents the converter resistance, L represents the converter inductance, and Respectively represent Input voltage and output current of the power grid system in the coordinate system.

[0018] The resistance and inductance as well as other disturbance factors in the power grid system are equivalent to a hyperlocal model. The predictive control equation of the adaptive hyperlocal model can be expressed as: (2); Where F represents the system equivalent hyperlocal model, is the adaptive input gain. By constructing a predictive control framework based on an adaptive hyperlocal model, the system parameters are decoupled.

[0019] In the framework of adaptive hyperlocal model, the system equivalent hyperlocal model F and adaptive input gain It will change with the change of parameters in the power grid system, and observe and update the changes of the system equivalent hyperlocal model and adaptive input gain to improve the predictive control performance. The online neural network estimator is used to estimate the hyperlocal model and adaptive input gain, realize the real-time update of the parameters in the hyperlocal model framework, improve the anti-interference ability of the control system, eliminate the dependence of the hyperlocal model and adaptive input gain on the system parameters, and realize the parameter-free operation of the control system.

[0020] Based on the hyperlocal model and adaptive input gain, the output current of the converter is obtained through the current prediction equation. Moreover, based on the predicted output current of the converter, the voltage vector of the converter is generated according to the switching state of the converter bridge arm. The optimal voltage vector is obtained according to the optimal voltage vector screening mechanism to control the converter and achieve voltage balance at the neutral point of the converter.

[0021] In summary, if Figure 2 As shown, the online neural network estimator performs calculations based on the input voltage and output current of the power grid system, and combines the reference voltage calculation to generate the voltage vector of the converter based on the predicted current of the converter, and realizes the optimal control of the converter through prediction-based vector screening.

[0022] According to the above description, the present application provides a control method for a converter, which adopts an online neural network estimator to realize online estimation of a hyperlocal model and an adaptive input gain, realizes real-time updating of parameters within the hyperlocal model, and eliminates the dependence of the hyperlocal model and the adaptive input gain on system parameters, thereby realizing parameter-free operation of the control system; and also obtains a voltage vector for controlling the converter based on the predicted current of the converter, thereby realizing the balance of the neutral point voltage of the converter.

[0023] As an implementation, the hyperlocal model is expressed as follows: (3); in, represents the weight coefficient matrix, is the activation function, represents the estimation error, represent The system output current in the coordinate system.

[0024] The weight coefficient matrix is ​​updated online, and formula (3) is substituted into formula (2). The update rate is expressed by the following formula: (4); in, represents the estimated value of the weight coefficient matrix, is the estimated value of the system output current information, , and are all adjustable weight coefficient matrices, Representative Matrix The transposed matrix of represent The system input voltage in the coordinate system, represents the adaptive input gain; Through the above update law formula, the convergence and stability of the weight coefficient matrix update process are ensured. The weight coefficient matrix can be updated and adjusted online to enable the ultra-local model to dynamically follow the changes in the actual power grid system and effectively deal with the impact of parameter changes on control performance.

[0025] As an implementation method, a first-order Euler discretization method is used to discretize the weight coefficient matrix to obtain the current prediction equation.

[0026] The first-order Euler discretization method is expressed as follows: (5); In the formula, represents the current, Represents time, Represents the control cycle.

[0027] By discretizing the weight coefficient matrix, the current prediction equation is expressed as follows: (6); In the formula, T s Represents the control cycle, that is, every T s The control algorithm is executed once.

[0028] As an implementation method, eliminating the dependence of the adaptive input gain on system parameters includes: consider The current information of the axis is discretized, and the current equation can be obtained as follows: (7); The parameters The estimated value of is expressed as , then , at this time the current prediction formula can be expressed as follows: (8); Subtract formula (7) from formula (8) to design the update law of the adaptive input gain. The update law of the adaptive input gain is expressed by the following formula: (9); In the formula, the parameters Represents the adaptive input gain, parameter Representation parameters The estimated value of express Output current information of the axis, express The predicted output current information of the axis, express Shaft voltage information, Indicates the control period.

[0029] Furthermore, the step size factor is introduced and the denominator in formula (9) is considered to be 0 to improve the parameter The rigor of the update law, the update law of the adaptive input gain can be expressed by the following formula: (10); Where p is the step size factor.

[0030] By designing the update law of the weight coefficient matrix and the input gain, the current prediction equation is obtained and the estimation of the hyperlocal model and the adaptive input gain is completed. In addition, the dependence of the hyperlocal model and the adaptive input gain on the system parameters is eliminated, thus realizing parameter-free predictive control of the power grid system.

[0031] To illustrate the reliability of the online neural network estimator, the stability analysis of the online neural network estimator will be carried out according to the Lyapunov stability principle: First, the estimation error of the weight coefficient matrix is ​​defined as , then the error equation of the online neural network estimator can be expressed as follows: (11); In a control cycle In the control cycle Time is too short, input gain The change in can be ignored, that is , and its observation error can be defined as , then the error equation can be constructed as follows: (12); Through the above formula, we can get that When the stability of the online neural network estimator is considered, the Lyapunov equation of the online neural network estimator can be constructed as: (13); Defining parameters , , then the derivative form of the Lyapunov equation can be expressed as follows: (14); By analyzing the stability of the online neural network estimator by scaling, we can obtain: (15); Define the step factor satisfy , In addition, the step size factor p is defined as follows: (16); At this time, Lyapunov's derivative is of the form satisfy: (17); Furthermore, the function is defined as follows: (18); use equation , then: (19); Through the above description, it is proved that the online neural network estimator maintains good stability in estimating the hyperlocal model and adaptive input gain.

[0032] As an implementation method, the control method further includes: designing a neutral point voltage update law, wherein the neutral point voltage update law is expressed by the following formula: (20); Where p represents the step size factor, Representatives The estimated value of , C dc Indicates the bus capacitance on the DC side.

[0033] The neutral point voltage of the power grid system is predicted by the following formula: (twenty one); (twenty two); In the formula, represents the estimated value of the neutral point voltage, Indicates the neutral point voltage, represents the neutral point voltage update law, Three switching expressions representing the voltage vectors used.

[0034] Substituting formula (20) into formula (21) can realize the prediction of the neutral point voltage. By applying a delay compensation strategy, an adaptive architecture similar to that in the online neural network estimator is adopted to realize the prediction of the neutral point voltage. The prediction of the neutral point voltage is realized without increasing the complexity of the algorithm, and the delay compensation of the converter is completed.

[0035] To further illustrate the control method of the converter provided by the present application, a three-level NPC (Neutral Point Clamped) inverter is used as an example for illustration. The topology diagram of the three-level NPC inverter is shown in FIG. Figure 3As shown in the figure, the inverter has three bridge arms. According to the different switching states of the switch tube, each bridge arm has three working states: 1, 0, and -1. The three bridge arms can form a total of 27 switching states. According to the action characteristics of different voltage vectors, the voltage vector is divided into four types of vectors: large, medium, small, and zero. Among the 27 switching states of the three-level NPC inverter, there are 19 valid vectors and the remaining 8 are redundant vectors.

[0036] The spatial distribution of the voltage vector is as follows: Figure 4 As shown, among the 19 valid vectors, the large vectors are: V7 (1, -1, -1), V9 (1, 1, -1), V11 (-1, 1, -1), V13 (-1, 1, 1), V15 (-1, -1, 1), V17 (1, -1, 1); the medium vectors are: V8 (1, 0, -1), V10 (0, 1, -1), V12 (-1, 1, 0), V14 (-1, 0, 1), V16 (0, -1, 1), V18 (1, -1,0); small vectors are: V1 (1,0,0), (0,-1,-1), V2 (1,1,0), (0,0,-1), V3 (0,1,0), (-1,0,-1), V4 (0,1,1), (-1,0,0), V5 (0,0,1), (-1,-1,0), V6 (1,0,1), (-1,-1,0); zero vector: V0 (-1,-1,-1), (0,0,0), (1,1,1). The 8 redundant vectors include 6 small vectors and 2 zero vectors. The small vectors in the 8 redundant vectors will produce the same voltage magnitude and phase, and produce opposite neutral point voltages.

[0037] As an implementation method, the control method also includes: designing a sequential value function to achieve control objectives of current tracking and neutral point voltage balance.

[0038] The sequential value function is expressed as follows: (twenty three); In the formula, It represents the requirements for current tracking quality. represents the balance requirement for the neutral point voltage, and It represents the requirement for switching frequency. is the reference value, That is, using the voltage vector The predicted value obtained later is is the symbolic function, Represents the number of switch changes.

[0039] As an implementation method, according to the predicted current of the converter and the predicted value of the neutral point voltage, based on the sequential value function, the voltage vector of the converter is screened, the optimal voltage vector is selected, and the converter is controlled using the optimal voltage vector.

[0040] The predicted value of the neutral point voltage is the absolute value of the predicted value obtained using the voltage vector. For ease of explanation, the predicted value of the neutral point voltage is recorded as , the neutral point voltage threshold is recorded as Specifically, when the neutral point voltage prediction value Less than the neutral point voltage threshold When current tracking is given priority, the cost function in the sequential value function is used Select the optimal vector. If the optimal vector is a long vector or a medium vector, it is directly applied to the converter. If the optimal vector is a small vector, the cost function in the sequential value function is used. , in the two switching states corresponding to the small vector, select the optimal switching state that makes the midpoint voltage closer to zero; if it is a zero vector, use the cost function in the sequential value function , among the three switching states corresponding to the zero vector, the optimal switching state that minimizes the switching frequency is selected for output.

[0041] Furthermore, when the neutral point voltage prediction value Exceeding the neutral point voltage threshold When the neutral point voltage is given priority, the cost function in the sequential value function is used. and The optimal switching state is selected and applied to the converter.

[0042] Through the above voltage vector screening strategy, the optimal voltage vector can be selected without the participation of capacitor parameters and weighting factors, avoiding excessive neutral point voltage while further eliminating the control method's dependence on system parameters.

[0043] In summary, if Figure 5 As shown, the present application provides a control method for a converter, firstly current sampling is performed, and the hyperlocal model F and the adaptive input gain are estimated by an online neural network estimator. , to predict the current and neutral point voltage, and to select the optimal voltage vector through the sequential cost function to achieve optimal control of the converter.

[0044] The control method of the converter provided in the present application is compared with the traditional finite set model predictive control method, the model-free predictive control method based on the extended state observer, and the model-free control method based on the integral sliding mode observer to verify the performance of the control method of the converter provided in the present application.

[0045] First, the above method is simulated, and the simulation parameters are shown in Table 1.

[0046]

[0047] Table 1 When the parameters are matched, the performance of the four control methods is as follows: Figure 6 As shown in the figure, part (a) is the traditional finite set model predictive control method, part (b) is the model-free predictive control method based on the extended state observer, part (c) is the model-free control method based on the integral sliding mode observer, and part (d) is the control method provided by this application. Under the condition of parameter matching, the THD (Total Harmonic Distortion) value of the control method provided by this application is 1.09%, which is lower than the other three control methods.

[0048] In the case of input gain mismatch (-70%), the performance of the four control methods is as follows: Figure 7 As shown. In the case of input gain mismatch (-70%), the THD value of the control method provided by the present application is 1.05%, which is lower than the other three control methods. Furthermore, in the case of inductor parameter mismatch (-40%), the performance of the four control methods is as follows Figure 8 In the case of inductor parameter mismatch (-40%), the THD value of the control method provided by the present application is 1.79%, which is lower than that of the other three control methods.

[0049] Under the above parameter matching and parameter mismatching conditions, the control method provided in the present application achieves a lower current harmonic content, and the control performance of the control method provided in the present application is better than that of other control methods.

[0050] Different degrees of parameter adaptation conditions are designed to verify the robustness range of the above four control methods, such as Fig. 9 As shown, it can be seen that the control method provided by the present application has a wider robustness range, and the control method of the converter provided by the present application has better robustness. Fig.10 As shown, taking the real inductance value of 10mH as the benchmark and the upper and lower mismatch of 50%, the estimation effect of the input gain by the online neural network estimator is verified, and the online adjustment of the input gain is realized.

[0051] Furthermore, a power conversion experimental platform based on 3L-NPC was designed. The specific parameters of the platform were the same as the above simulation parameters. Comparative experiments were conducted under two working conditions: parameter matching and parameter mismatch. The results of the above four control methods under the parameter matching condition are shown in the following figure. Fig.11a , Fig.11b , Fig.11c , Fig.11d As shown; Under the parameter mismatch condition (L-36%), the results of the four methods are as follows Fig.12a , Figure 12b , Fig.12c , Fig.12d shown.

[0052] Under the condition of parameter mismatch, the control method provided in the present application improves the current quality by 80.34% compared with the traditional finite set model predictive control method, and improves by 54.94% and 56.29% respectively compared with the two existing methods based on super-local models. The robustness of the control method provided in the present application is better than the other three control methods, and the control system provided in the present application can operate with high current quality in the case of severe parameter mismatch.

[0053] According to the above description, the present application provides a control method for a converter, which uses an online neural network estimator to realize online estimation of a hyperlocal model and an adaptive input gain, realizes real-time updating of parameters in the hyperlocal model, and eliminates the dependence of the hyperlocal model and the adaptive input gain on system parameters, thereby realizing parameter-free operation of the control system; it also obtains a voltage vector for controlling the converter based on the predicted current of the converter, thereby realizing the balance of the neutral point voltage of the converter; and uses an adaptive architecture similar to that in the online neural network estimator to realize the prediction of the neutral point voltage, thereby realizing the prediction of the neutral point voltage without increasing the complexity of the algorithm, and realizing delay compensation for the converter.

[0054] In the second aspect, the present application also provides a power grid system, which includes a converter. The power grid system applies the converter control method described above. The converter can realize current tracking and neutral point voltage balance of the converter, thereby realizing parameter-free operation of the control system.

[0055] It will be appreciated that the word "exemplary" as used herein means "serving as an example, instance, or illustration". Any embodiment described as "exemplary" is not necessarily preferred or superior to other embodiments and / or does not exclude the combination of features of other embodiments. It will be appreciated that certain features of the present application described in the context of separate embodiments for the sake of clarity may also be provided in a single embodiment by combination. Conversely, various features of the present application described in the context of a single embodiment for the sake of clarity may also be provided individually or in any suitable combination or as any other described embodiment of the present application.

[0056] The above disclosure is only the preferred embodiment of the present application, but it is not intended to limit the scope of rights of the present application. A person of ordinary skill in the art can understand that without departing from the spirit and scope of the present application and the appended claims, changes, modifications, substitutions, combinations, and simplifications should all be equivalent replacement methods and still fall within the scope of the invention.

Claims

1. A control method for a converter, the control method being applied to control the converter in a power grid system, characterized in that: The control method comprises the following steps: Constructing an adaptive hyperlocal model, the adaptive hyperlocal model including an adaptive input gain and a hyperlocal model, the hyperlocal model including physical parameters in the controlled system and disturbance factors in the power grid system; An online neural network estimator is used to estimate the hyperlocal model and the adaptive input gain, thereby eliminating the dependence of the hyperlocal model and the adaptive input gain on system parameters; The output current of the converter is predicted based on the hyperlocal model and the adaptive input gain, a voltage vector of the converter is generated based on the predicted current of the converter, and the converter is controlled using the voltage vector.

2. The control method of the converter according to claim 1, characterized in that: The control method further comprises: The hyperlocal model is expressed by the following formula: ； in, represents the weight coefficient matrix, is the activation function, is the estimation error, represent The system output current in the coordinate system; The weight coefficient matrix is ​​updated online, and its update rate is expressed by the following formula: ； in, represents the estimated value of the weight coefficient matrix, is the estimated value of the system output current information, , and are all adjustable weight coefficient matrices, Representative Matrix The transposed matrix of represent The system input voltage in the coordinate system, represents the adaptive input gain; The weight coefficient matrix is ​​discretized to obtain a current prediction equation.

3. The control method of the converter according to claim 2, characterized in that: The weight coefficient matrix is ​​discretized using the first-order Euler discretization method, and the current prediction equation obtained is expressed by the following formula: ； In the formula, Indicates the control period.

4. The control method of the converter according to claim 1, characterized in that: Eliminating the dependence of the adaptive input gain on system parameters includes: The update law of the adaptive input gain is designed, and the update law of the adaptive input gain is expressed by the following formula: ； In the formula, the parameters represents the adaptive input gain, parameter Representation parameters The estimated value of express Output current information of the axis, express The predicted output current information of the axis, express Shaft voltage information, Indicates the control period.

5. The control method of the converter according to claim 4, characterized in that: The control method further comprises: Introducing the step size factor, the update law of the adaptive input gain is expressed by the following formula: ； Wherein, p represents the step size factor.

6. The control method of the converter according to claim 1, characterized in that: The control method further comprises: Design a neutral point voltage update law, which is expressed by the following formula: ； In the formula, p represents the step size factor, Representatives The estimated value of , C dc Indicates the bus capacitance on the DC side; The neutral point voltage of the power grid system is predicted by the following formula: ； ； In the formula, represents the estimated value of the neutral point voltage, Indicates the neutral point voltage, represents the neutral point voltage update law, Three switch expressions representing the voltage vector used; delay compensation of the converter is achieved to achieve the control target of neutral point voltage balance.

7. The control method of the converter according to claim 6, characterized in that: The control method further comprises: Design a sequential value function, which is expressed by the following formula: ； In the formula, It represents the requirements for current tracking quality. represents the balance requirement for the neutral point voltage, and It represents the requirement for switching frequency. is the reference value, That is, using the voltage vector The predicted value obtained later is is the symbolic function, Represents the number of switch changes; According to the predicted current and the predicted neutral point voltage of the converter, based on the sequential value function, the voltage vector of the converter is screened, the optimal voltage vector is selected, and the converter is controlled using the optimal voltage vector.

8. The control method of the converter according to claim 7, characterized in that: The control method further comprises: When the predicted neutral point voltage value is less than the neutral point voltage threshold, the cost function in the sequential value function is used to Selecting an optimal vector, and if the optimal vector is a long vector or a medium vector, directly applying it to the converter; If the optimal vector is a small vector, the cost function in the sequential value function is used Select the optimal switching state that makes the midpoint voltage closer to zero; If it is a zero vector, the cost function in the sequential value function is used Select the optimal switching state that minimizes the switching frequency.

9. The control method of the converter according to claim 7, characterized in that: The control method further comprises: When the predicted neutral point voltage value exceeds the neutral point voltage threshold, the cost function in the sequential value function is used. and The optimal switching state is selected and applied to the converter.

10. A power grid system, comprising a converter, characterized in that: The power grid system applies the converter control method according to any one of claims 1 to 9.

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