Control method of power converter based on subspace predictive control

Abstract

The invention discloses a control method of a power converter based on subspace predictive control, which is used in the technical field of power converter control, and comprises the following steps: constructing a subspace predictor model of the power converter based on a historical data matrix of the power converter; predicting a linear part of the model and predicting a nonlinear part through a neural network; based on the linear part predicted value and the non-linear part predicted value of the current control period, obtaining a function relational expression of a future predicted output current; determining the optimal input voltage of the current control period based on the target function; and constructing a cost function by using the optimal input voltage of the current control period and the voltage vectors corresponding to the switching states of the power converter, and taking the switching state corresponding to the voltage vector when the value of the cost function is minimum as the optimal switching state of the current control period. The state of the power converter can be accurately predicted, and the negative influence of parameter mismatch on the power converter is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of power converter control, and in particular to a control method of a power converter based on subspace predictive control. Background Art

[0002] With the advancement of renewable energy technology, the electrification of modern society is gradually increasing. As the interface between energy conversion and consumption in renewable energy systems, power converters play a key role in this process. Finite set model predictive control (FCS-MPC) has attracted widespread attention in the academic community due to its powerful multi-tasking capabilities, simple implementation, and fast dynamic response. In standard finite set model predictive control, the finite switching states of the inverter and the discrete model of the controlled object are first used to predict the possible future trajectories of the system. These predicted trajectories are substituted into the cost function containing the reference trajectory. All switching states are evaluated by enumeration method, and the optimal switching state that can minimize the cost function is selected and applied to the power converter. It can be observed that the accuracy of predicting future behavior depends on the accuracy of information about the controlled object. However, in industrial environments, with the continuous operation of production equipment, frequent switching of working conditions, and continuous changes in external conditions (such as temperature, humidity, electromagnetic interference, etc.), the internal parameters of the system tend to drift, and are also subject to various unpredictable external disturbances. Therefore, modeling errors, unmodeled dynamics, and parameter changes are inevitable. In these cases, the load parameters fluctuate around their nominal values. Since there is an error between the prediction model in the controller and the actual system model, the prediction accuracy will be reduced, thus deteriorating the control performance. The high dependence of FCS-MPC on the prediction model seriously limits its application in the industrial field.

[0003] There are four main schemes to enhance the parameter robustness of model predictive control, namely the current lookup table scheme, the super-local model scheme, the parameter estimation scheme and the time series model scheme. In the current lookup table scheme, a current lookup table is established, and the voltage vector and its corresponding current increment are stored in the lookup table. During control, the voltage vector is screened according to the current increment in the lookup table, so that the control can be completed without relying on system parameters. However, when the scheme updates the lookup table, some voltage vectors have not been used for a long time, and their corresponding current increments are not updated, which will produce a stagnation effect. At this time, when the stagnant voltage vector is updated, the control effect is reduced due to the error of its corresponding current increment. In the hyperlocal model scheme, the control system model is converted into a hyperlocal model, in which the future state of the system is expressed as the sum of the lumped disturbance term and the control input term. An extended state observer is designed to estimate the lumped disturbance in real time and complete the observation of the system state to resist parameter disturbance. However, since it separates the system state into lumped disturbance terms and control input terms, although various observers can be designed to estimate the lumped disturbance terms, the input gain of the control input term still depends on the prior knowledge of the system, which also makes the control scheme unable to complete the control when the system parameters are unknown. In the parameter estimation scheme, the recursive least squares algorithm is used to estimate the parameters such as the inductance and resistance of the control load online to resist parameter disturbance, but it is sensitive to the noise in the input data. If there is a lot of measurement noise in the input signal, it may cause the estimation result to fluctuate, thereby affecting the control performance. In addition, the estimation algorithm cannot fully capture the nonlinear characteristics of the control system. In the time series model scheme, the output of the control system is represented as a subspace time series, and the series coefficients are estimated using recursive least squares. The control input can be obtained based on the future reference output. However, this scheme assumes that the system behavior is linear or can be linearly approximated, so it is difficult to capture the complex nonlinear dynamics in actual systems. The ability of the time series subspace method to characterize nonlinear dynamics is limited by the model structure, especially when the parameter estimation error is large or the external disturbance is significant, which may lead to fluctuations or decreases in control performance. Summary of the invention

[0004] In order to address the deficiencies of the prior art, the present application aims to provide a control method for a power converter based on subspace predictive control, so as to accurately predict the state of the power converter and reduce the negative impact of parameter mismatch on the power converter.

[0005] To achieve the above object, the present application provides a control method for a power converter based on subspace predictive control, the method comprising: Constructing a subspace predictor model of the power converter based on a historical data matrix in a historical time window of the power converter, an output current in a future time window, and an input voltage in a future time window, wherein the historical data matrix includes a historical input voltage and a corresponding historical output current, and the subspace predictor model includes a linear part and a nonlinear part; Constructing an objective function of the linear part based on the historical data matrix, and determining a linear part prediction value of the current control cycle based on the objective function; Constructing a first nonlinear function of the nonlinear part, and constructing a second nonlinear function of the output current of the power converter according to the linear part prediction value of the current control cycle, estimating the first nonlinear function and the second nonlinear function by using a neural network, and determining the nonlinear part prediction value of the current control cycle; Determine a functional relationship for predicting output current in a future time window based on the linear part prediction value of the current control cycle, the nonlinear part prediction value of the current control cycle, and a subspace predictor model; An objective function is constructed by using a functional relationship and an expected output current in a preset future time window, and an optimal input voltage of a current control cycle is determined based on the objective function; A cost function is constructed with the optimal input voltage of the current control cycle and the voltage vectors corresponding to each switching state of the power converter. When the value of the cost function is minimum, the voltage vector corresponding to the optimal input voltage of the current control cycle is determined, and the switching state corresponding to the voltage vector is used as the optimal switching state of the power converter in the current control cycle.

[0006] Furthermore, a subspace predictor model of a power converter is constructed, including: Define the nonlinear part of the subspace predictor model; A subspace predictor model of a power converter is constructed based on a historical data matrix within a historical time window of the power converter, an output current within a future time window, an input voltage within a future time window, and a nonlinear part, wherein the linear part of the subspace predictor model is determined based on a coefficient matrix of the subspace predictor model.

[0007] Furthermore, a subspace predictor model of a power converter is constructed, including: The subspace predictor model of the power converter is expressed as: ; ; ; ; ; Among them, k represents the current control cycle, represents the length of the historical time window, represents the length of the future time window, represents the predicted output current matrix in the future time window, represents the output current increment matrix of adjacent control cycles in the future time window, and represents the coefficient matrix, represents the input voltage matrix in the future time window, represents the input voltage increment matrix of adjacent control cycles in the future time window, represents the historical data matrix, and Represents the historical input voltage and the corresponding historical output current, Represents the historical input voltage increment matrix and the corresponding historical output current increment matrix of two adjacent control cycles in the historical time window, represents the nonlinear part of the subspace predictor model, represents a single nonlinear term, express A matrix with all 1s in one row and one column; by Represents the linear part of the subspace predictor model.

[0008] Furthermore, constructing the objective function of the linear part based on the historical data matrix and determining the linear part prediction value of the current control cycle based on the objective function include: The objective function of constructing the coefficient matrix of the linear part is constructed by the increment of the historical data matrix and the predicted value of the nonlinear part of the previous control cycle; The coefficient matrix of the linear part of the current control cycle is obtained by taking partial derivatives of the objective function, and the predicted value of the linear part of the current control cycle is determined based on the historical data matrix and the coefficient matrix of the linear part of the current control cycle.

[0009] Furthermore, the objective function of the linear part is constructed based on the historical data matrix, and the linear part prediction value of the current control cycle is determined based on the objective function, including: The objective function of the coefficient matrix of the linear part It is expressed as: ; ; in, represents the weight factor, express The increment of the historical data matrix at time , express The output current increment at the moment, express The input voltage increment at time Representation coefficient matrix The j-th row of data, represents the coefficient matrix prediction value of the current control cycle, Represents the predicted value of a single nonlinear term in the previous control period; make , then the coefficient matrix prediction value of the linear part of the current control cycle is It is expressed as: ; ; in, represents the step size factor, Represents the coefficient matrix prediction value of the previous control cycle; The coefficient matrix predictions based on the linear part of the current control cycle The linear part prediction value of the current control period is determined by using the historical data matrix.

[0010] Furthermore, the first nonlinear function and the second nonlinear function are estimated by using a neural network to determine a nonlinear part prediction value of the current control cycle, including: Define the weight matrix of the neural network and define the activation function of the neural network with current as parameter; Constructing a first nonlinear function of a single nonlinear term according to the weight matrix and the activation function; Constructing a second nonlinear function of the output current of the power converter according to the linear part prediction value of the current control cycle, the weight matrix and the activation function; Using a neural network to estimate the first nonlinear function and the second nonlinear function, an estimated value of the output current of the current control cycle, an estimated value of the weight matrix of the current control cycle, and estimated values ​​of each single nonlinear term of the current control cycle; Based on the estimated values ​​of each single nonlinear term of the current control cycle, the predicted value of the nonlinear part of the current control cycle is determined.

[0011] Furthermore, the first nonlinear function and the second nonlinear function are estimated by using a neural network to determine a nonlinear part prediction value of the current control cycle, including: The first nonlinear function of a single nonlinear term d is expressed as: ; ; The second nonlinear function of the output current of the power converter is expressed as: ; ; in, represents the output current of the power converter, represents the linear part prediction value of the current control cycle, represents the weight matrix of the neural network, represents the activation function of the neural network, represents the minimum error of the nonlinear term estimate, represents the minimum error of output current estimation; The first nonlinear function and the second nonlinear function are estimated using a neural network, and the estimated value of the nonlinear term d is and the estimated value of the output current of the power converter Respectively expressed as: ; ; ; in, , , represents a positive coefficient, represents the estimated value of the linear part prediction value, represents the estimated value of the weight matrix of the neural network, represents the error between the estimated value of the output current of the power converter and the actual output current of the power converter, represents the derivative of the estimated value of the weight matrix of the neural network; The estimated value of the nonlinear term d , estimated value of output current of power converter and the derivative of the estimate of the weight matrix Perform first-order Euler discretization respectively and obtain the following expressions: ; in, represents the estimated value of the output current in the current control cycle, represents the estimated value of the weight matrix of the current control cycle, represents the estimated value of the linear part prediction value of the current control period, represents the length of the control cycle, represents the estimated value of a single nonlinear term for the current control period; The nonlinear part of the subspace predictor model predicted value for the current control period It is expressed as: .

[0012] Further, based on the linear part prediction value of the current control cycle, the nonlinear part prediction value of the current control cycle and the subspace predictor model, a functional relationship for predicting the output current in the future time window is determined, including: Substitute the linear part prediction value of the current control cycle and the nonlinear part prediction value of the current control cycle into the expression of the subspace predictor model to obtain the predicted output current matrix in the future time window The functional relationship is: ; ; ; in, represents an m×m dimensional matrix, the diagonal elements of the matrix are 1, and the remaining elements are 0, and m represents the data dimension of the system input and output; The objective function is constructed by using the functional relationship and the expected output current in a preset future time window, and the optimal input voltage of the current control cycle is determined based on the objective function, including: To predict the output current matrix The objective function is constructed by using the functional relationship of the expected output current matrix in the preset future time window. It is expressed as: ; ; in, represents the expected output current matrix within the preset future time window, represents the expected output current at time k+1 obtained based on vector angle extrapolation, represents the input voltage matrix in the future time window, and represents the diagonal weight matrix; when According to the objective function Get the input voltage increment matrix in the future time window for: ; ; ; by The sum of the first matrix element of and the optimal input voltage of the previous control cycle is used as the optimal input voltage to the current control cycle. .

[0013] Furthermore, a cost function is constructed based on the optimal input voltage of the current control cycle and the voltage vector corresponding to each switching state of the power converter, including: The cost function is constructed by using the optimal input voltage of the current control cycle and the voltage vector corresponding to each switching state of the power converter. The expression is: ; in, represents the voltage vector corresponding to each switching state of the power converter, and n represents n switching states; Based on the cost function Calculate the optimal input voltage for each voltage vector and the current control cycle The square of the absolute value between and is used to determine the voltage vector corresponding to the minimum value, and the switching state corresponding to the voltage vector is used as the optimal switching state of the power converter in the current control cycle.

[0014] The present application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the control method of a power converter based on subspace predictive control as described above are implemented.

[0015] The present application constructs a subspace predictor model of a power converter having a linear part and a nonlinear part, predicts its linear part and nonlinear part, and obtains the predicted output current in the future time window based on the predicted value of the linear part, the predicted value of the nonlinear part and the subspace predictor model. The nonlinear dynamic information of the device captured by the neural network predictor can be integrated into the subspace predictor updated in real time, so as to accurately predict the state of the power converter and reduce the negative impact of parameter mismatch on the control device. By determining the optimal switching state of the current control cycle of the power converter, the optimal input voltage of the future control input of the power converter can be obtained, the accuracy of the device control can be improved, and the robustness of the device to parameter changes can be enhanced without relying on the physical model and prior knowledge of the controlled object. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A first flow chart of a control method for a power converter based on subspace predictive control provided in an embodiment of the present application; Figure 2 A second flow chart of a control method for a power converter based on subspace predictive control provided in an embodiment of the present application; Figure 3 A third flow chart of a control method for a power converter based on subspace predictive control provided in an embodiment of the present application; Figure 4A fourth flow chart of a control method for a power converter based on subspace predictive control provided in an embodiment of the present application; Figure 5 A system block diagram of a power converter based on subspace predictive control provided in an embodiment of the present application; Figure 6 A schematic diagram of the steady-state performance of the output current and the DC side capacitor voltage in the case of parameter mismatch of the traditional finite set model predictive control method provided in an embodiment of the present application; Figure 7 A schematic diagram of transient performance of output current and DC side capacitor voltage under parameter mismatch of the traditional finite set model predictive control method provided in an embodiment of the present application; Figure 8 A schematic diagram of the steady-state performance of the output current and the DC side capacitor voltage of the method of the present application under the condition of parameter mismatch provided in an embodiment of the present application; Fig. 9 A schematic diagram of transient performance of output current and DC side capacitor voltage of the method of the present application under parameter mismatch conditions provided in an embodiment of the present application; Fig.10 A system block diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the specific implementation manner of the present application will be clearly and completely described below in conjunction with the drawings in the implementation manner of the present application.

[0018] Please refer to Figure 1 The present application provides a control method for a power converter based on subspace predictive control, the method comprising the steps of: S101, constructing a subspace predictor model of the power converter based on a historical data matrix in a historical time window of the power converter, an output current in a future time window, and an input voltage in a future time window, wherein the historical data matrix includes a historical input voltage and a corresponding historical output current, and the subspace predictor model includes a linear part and a nonlinear part; S102, constructing an objective function of the linear part based on the historical data matrix, and determining a linear part prediction value of the current control cycle based on the objective function; S103, constructing a first nonlinear function of the nonlinear part, and constructing a second nonlinear function of the output current of the power converter according to the linear part prediction value of the current control cycle, estimating the first nonlinear function and the second nonlinear function by using a neural network, and determining the nonlinear part prediction value of the current control cycle; S104, determining a functional relationship for predicting output current in a future time window based on a linear part prediction value of a current control cycle, a nonlinear part prediction value of a current control cycle, and a subspace predictor model; S105, constructing an objective function based on a functional relationship and an expected output current in a preset future time window, and determining an optimal input voltage of a current control cycle based on the objective function; S106. Construct a cost function based on the optimal input voltage of the current control cycle and the voltage vectors corresponding to the various switching states of the power converter. When the value of the cost function is minimum, determine the voltage vector corresponding to the optimal input voltage of the current control cycle, and use the switching state corresponding to the voltage vector as the optimal switching state of the power converter in the current control cycle.

[0019] Exemplarily, the length of the historical time window can be the length of multiple control cycles. By collecting the input voltage and output current of the power converter in each control cycle as data samples, that is, based on each data sample obtained in the historical time window as a historical data matrix, the historical data matrix includes historical input voltages and corresponding historical output currents. The length of the future time window can be the length of multiple control cycles. The historical time window and the future time window can be continuous in time. The power converter can be an inverter.

[0020] Exemplarily, the subspace predictor model can be a model that describes future outputs using past input and output data and future control input data. The controlled power converter is represented as a subspace predictor model. Therefore, the subspace predictor model of the power converter is constructed based on the historical data matrix in the historical time window of the power converter, the output current in the future time window, and the input voltage in the future time window. Due to the continuous operation of the equipment, the frequent switching of the working conditions, and the constant changes of external conditions (such as temperature, humidity, electromagnetic interference, etc.) in the industrial production environment, the parameters inside the equipment tend to drift, and the equipment is also subject to various unpredictable external disturbances, resulting in some errors in nonlinear dynamic information between the model and the actual equipment. Therefore, when constructing the subspace predictor model of the power converter, the nonlinear part of the model is considered in the present application, so that the state of the equipment can be better predicted through the model.

[0021] In this embodiment, by constructing a subspace predictor model of a power converter having a linear part and a nonlinear part, predicting its linear part, and predicting its nonlinear part through a neural network predictor, the predicted output current in the future time window is obtained based on the linear part prediction value, the nonlinear part prediction value and the subspace predictor model solution, and the nonlinear dynamic information of the device captured by the neural network predictor can be integrated into the subspace predictor updated in real time, so as to accurately predict the state of the power converter and reduce the negative impact of parameter mismatch on the control device. By constructing the objective function of the input voltage in the future time window, the optimal input voltage of the current control cycle is determined based on the objective function, and the cost function is constructed with the optimal input voltage of the current control cycle and the voltage vector corresponding to each switch state of the power converter to determine the optimal switch state of the current control cycle of the power converter, the optimal input voltage of the future control input of the power converter can be obtained, the accuracy of the device control can be improved, and the robustness of the device to parameter changes can be enhanced without relying on the physical model and prior knowledge of the controlled object.

[0022] In one embodiment, Figure 2 As shown, constructing a subspace predictor model of a power converter includes the following steps: S201, defining a nonlinear part of a subspace predictor model; S202. Constructing a subspace predictor model of the power converter based on the historical data matrix in the historical time window of the power converter, the output current in the future time window, the input voltage in the future time window, and the nonlinear part, wherein the linear part of the subspace predictor model is determined based on the coefficient matrix of the subspace predictor model.

[0023] The typical subspace predictor model can be expressed as: ; In one embodiment, the above subspace predictor model is transformed, and both sides of the formula are multiplied by an increment, which represents the incremental form of the independent variable in the formula, and the subspace predictor model of the power converter can be expressed as: ; ; ; ; ; Among them, k represents the current control cycle, represents the length of the historical time window, represents the length of the future time window, represents the predicted output current matrix in the future time window, represents the output current increment matrix of adjacent control cycles in the future time window, and represents the coefficient matrix, represents the input voltage matrix in the future time window, represents the input voltage increment matrix of adjacent control cycles in the future time window, represents the historical data matrix, and Represents the historical input voltage and the corresponding historical output current, Represents the historical input voltage increment matrix and the corresponding historical output current increment matrix of two adjacent control cycles in the historical time window, represents the nonlinear part of the subspace predictor model, represents a single nonlinear term, express A matrix with all 1s in one row and one column; by represents the linear part of the subspace predictor model.

[0024] In this embodiment, a subspace predictor model is constructed through historical input and output data, and the linear part and nonlinear part are taken into account in the model. An unknown residual nonlinear part is introduced into the model, which enhances the model's resistance to unknown interference of the equipment and its ability to compensate for equipment uncertainty.

[0025] In one embodiment, Figure 3 As shown, the objective function of the linear part is constructed based on the historical data matrix, and the linear part prediction value of the current control cycle is determined based on the objective function, including the steps: S301, constructing an objective function of a coefficient matrix of a linear part using an increment of a historical data matrix and a predicted value of a nonlinear part of a previous control cycle; S302, taking partial derivatives of the objective function to obtain a coefficient matrix of the linear part of the current control cycle, and determining a predicted value of the linear part of the current control cycle based on the historical data matrix and the coefficient matrix of the linear part of the current control cycle.

[0026] From the above linear part It can be seen from the expression that represents the historical input voltage increment matrix and the corresponding historical output current increment matrix, represents the input voltage increment matrix in the future time window, so the estimation of the linear part can be understood as the estimation of the coefficient matrix.

[0027] In one embodiment, the coefficient matrix of the objective function It is expressed as: ; ; in, represents the weight factor, express The increment of the historical data matrix at time , express The output current increment at the moment, express The input voltage increment at time Representation coefficient matrix The j-th row of data, represents the coefficient matrix prediction value of the current control cycle, Represents the predicted value of a single nonlinear term in the previous control period; Take partial derivative of the objective function, that is, , then the coefficient matrix prediction value of the linear part of the current control cycle is It is expressed as: ; ; in, represents the step size factor, Represents the coefficient matrix prediction value of the previous control cycle; The coefficient matrix predictions based on the linear part of the current control cycle The linear part prediction value of the current control period is determined by using the historical data matrix.

[0028] In this embodiment, the linear dynamic information of the device is captured by using the subspace predictor model through historical input voltage and output current data, thereby enhancing the real-time prediction capability of the device behavior.

[0029] In one embodiment, Figure 4 As shown, the step of estimating the first nonlinear function and the second nonlinear function by using a neural network to determine the predicted value of the nonlinear part of the current control cycle includes: S401, defining a weight matrix of a neural network, and defining an activation function of the neural network using current as a parameter; S402, constructing a first nonlinear function of a single nonlinear term according to a weight matrix and an activation function; S403, constructing a second nonlinear function of the output current of the power converter according to the linear part prediction value of the current control cycle, the weight matrix and the activation function; S404, using a neural network to estimate the first nonlinear function and the second nonlinear function, an estimated value of the output current of the current control cycle, an estimated value of the weight matrix of the current control cycle, and estimated values ​​of each single nonlinear term of the current control cycle; S405 . Determine a predicted value of the nonlinear part of the current control period based on the estimated values ​​of each single nonlinear item of the current control period.

[0030] In this embodiment, the nonlinear part is estimated by using a neural network.

[0031] Specifically, the first nonlinear function of a single nonlinear term d is expressed as: ; ; in, represents the activation function of the neural network, satisfy , represents the optimal weight matrix, and is a normal number, represents the minimum error of the nonlinear term estimate, and is a positive constant.

[0032] The second nonlinear function of the output current of the power converter is expressed as: ; ; in, represents the output current of the power converter, represents the linear part prediction value of the current control cycle, represents the weight matrix of the neural network, represents the activation function of the neural network, represents the minimum error of the nonlinear term estimate, represents the minimum error of output current estimation; The above nonlinear function is estimated using a neural network predictor. The first nonlinear function and the second nonlinear function are estimated using a neural network, and the estimated value of the nonlinear term d is and the estimated value of the output current of the power converter Respectively expressed as: ; ; ; in, , , represents a positive coefficient, represents the estimated value of the linear part prediction value, represents the estimated value of the weight matrix of the neural network, represents the error between the estimated value of the output current of the power converter and the actual output current of the power converter, represents the derivative of the estimated value of the weight matrix of the neural network; The estimated value of the nonlinear term d , estimated value of output current of power converter and the derivative of the estimate of the weight matrix Perform first-order Euler discretization respectively and obtain the following expressions: ; in, represents the estimated value of the output current in the current control cycle, represents the estimated value of the weight matrix of the current control cycle, represents the estimated value of the linear part prediction value of the current control period, represents the length of the control cycle, represents the estimated value of a single nonlinear term for the current control period; The nonlinear part of the subspace predictor model predicted value for the current control period It is expressed as: .

[0033] In this embodiment, a neural network is used to estimate the nonlinear part based on the predicted value of the linear part that has been obtained, and the neural network predictor and the subspace predictor are combined. Based on the weight matrix update and error compensation mechanism of the neural network, online approximation of dynamic nonlinear disturbances is achieved, thereby capturing the nonlinear dynamic information and linear dynamic information of the equipment, and improving the model's ability to handle unknown disturbances and nonlinear transformations, especially when the equipment maintains high control performance in the case of parameter mismatch.

[0034] In one embodiment, the step of determining a functional relationship for predicting output current in a future time window based on a linear part prediction value of a current control cycle, a nonlinear part prediction value of the current control cycle, and a subspace predictor model includes: Substitute the linear part prediction value of the current control cycle and the nonlinear part prediction value of the current control cycle into the expression of the subspace predictor model to obtain the predicted output current matrix in the future time window The functional relationship is: ; ; ; in, Represents an m×m dimensional matrix, the diagonal elements of the matrix are 1, and the remaining elements are 0. m represents the data dimension of the system input and output.

[0035] In this embodiment, based on the linear part prediction value and the nonlinear part prediction value obtained as described above, an expression of a complete subspace predictor model can be obtained.

[0036] In order to obtain the optimal input voltage of the device, an objective function of the input voltage is constructed, that is, the objective function is constructed based on the functional relationship of the predicted output current and the expected output current in a preset future time window, and the optimal input voltage of the current control cycle is determined based on the objective function.

[0037] In one embodiment, the predicted output current matrix The objective function is constructed by using the functional relationship of the expected output current matrix in the preset future time window. It is expressed as: ; ; in, represents the expected output current matrix within the preset future time window, represents the expected output current at time k+1 obtained based on vector angle extrapolation, represents the input voltage matrix in the future time window, and represents a diagonal weight matrix.

[0038] From the above objective function, it can be seen that if the input voltage can be optimized, the optimization of the objective function needs to satisfy the following two points as much as possible: Formula It means to reduce the deviation between the future output current and the expected output current as much as possible to ensure that the output current can be consistent with the expected output current. The formula This means that the rate of change of the input voltage is reduced as much as possible, the adjustment range of the input voltage is made as small as possible, and the number of switching times of the switching state of the power converter is reduced, thereby reducing the switching frequency.

[0039] Predicted output current Substitute the expression into the objective function , it can be seen that the future behavior of the device is determined by the future input voltage increment matrix Therefore, by taking the partial derivative of the objective function with respect to the input voltage increment, the input voltage increment matrix can be obtained: .

[0040] When According to the objective function Get the input voltage increment matrix in the future time window for: ; ; ; Because each control cycle of the power converter can only act on one input voltage, The sum of the first matrix element of and the optimal input voltage of the previous control cycle is used as the optimal input voltage to the current control cycle. .

[0041] In this embodiment, the minimization error between the future output current predicted by the subspace predictor model and the desired output current is used as the optimization goal, and the optimal value of the future input voltage of the power converter is derived. This optimal value is used as the input voltage of the current control cycle, which can improve the accuracy of device control and achieve good controllability of the device in the event of device parameter mismatch.

[0042] A cost function is constructed with the optimal input voltage of the current control cycle and the voltage vectors corresponding to the various switching states of the power converter. When the value of the cost function is minimum, the voltage vector corresponding to the optimal input voltage of the current control cycle is determined, and the switching state corresponding to the voltage vector is used as the optimal switching state of the power converter in the current control cycle, thereby realizing control of the power converter.

[0043] In one embodiment, since different switching states of the power converter output different voltage vectors, a limited number of switching states correspond to a limited number of discrete voltage vectors. The cost function is constructed with the optimal input voltage of the current control cycle and the voltage vectors corresponding to each switching state of the power converter. The expression is: ; in, It represents the voltage vector corresponding to each switching state of the power converter, and n represents n switching states.

[0044] Based on the cost function Calculate the optimal input voltage for each voltage vector and the current control cycle When the square of the absolute value is the smallest, that is, the value of the cost function is the smallest, it indicates that the optimal input voltage of the current control cycle is closest to the corresponding voltage vector, and then the voltage vector corresponding to the optimal input voltage of the current control cycle is determined, and the corresponding switching state is determined by the voltage vector, and the switching state is used as the optimal switching state of the power converter in the current control cycle, thereby achieving precise control of the power converter.

[0045] In this embodiment, the cost function is constructed based on the voltage vectors corresponding to each switching state, and these voltage vectors are selected based on a finite discrete method. In another embodiment, the voltage vector can be modulated by a space vector on the control variable within a continuous space range to obtain a continuous voltage vector, thereby further improving the smoothness and accuracy of the device output and reducing switching losses and harmonic distortion.

[0046] like Figure 5 As shown, a system block diagram of a power converter based on subspace predictive control of an embodiment of the present application. Taking an inverter as an example, the input voltage and output current data of the inverter are collected as a historical data matrix, and a subspace predictor model of the inverter is constructed based on the historical data matrix. The linear part of the model is estimated to obtain a linear part prediction value, and a neural network is used to estimate the nonlinear part of the model to obtain a nonlinear part prediction value. Based on the linear part prediction value and the nonlinear part prediction value, a complete subspace predictor model can be obtained, and then the output current in the future time window can be predicted. According to the predicted output current in the future time window and the expected output current in the future time window, an objective function of the input voltage in the future time window is constructed, and the optimal input voltage of the current control cycle is determined according to the objective function. The cost function is constructed with the optimal input voltage of the current control cycle and the voltage vector corresponding to each switch state of the inverter. When the value of the cost function is the smallest, the voltage vector corresponding to the optimal input voltage of the current control cycle is determined, and the switch state corresponding to the voltage vector is used as the optimal switch state of the current control cycle of the inverter, thereby realizing the control of the inverter.

[0047] In a specific embodiment of the present application, a midpoint clamped three-level inverter is used as a control device, and its variables are shown in the following table:

[0048] Figure 6 This is a schematic diagram of the steady-state performance of the output current and the DC side capacitor voltage under the condition of parameter mismatch of the traditional finite set model predictive control method. Figure 7 The transient performance diagram of the output current and DC side capacitor voltage of the traditional finite set model predictive control method under parameter mismatch. Figure 8 Schematic diagram of steady-state performance of output current and DC side capacitor voltage under parameter mismatch of the method of the present application, Fig. 9 The transient performance diagram of the output current and the DC side capacitor voltage under the parameter mismatch condition of the method of the present application is shown in the figure. The load inductance value under the parameter mismatch condition is 80% of the standard inductance. Indicates the amplitude of the reference current. Steady-state performance indicates that the reference current amplitude is always maintained at 12A. Transient performance indicates that the reference current suddenly changes from 8A to 12A. a 、ib and i c Represents three-phase current, v p and v l Indicates the upper capacitor voltage and lower capacitor voltage on the DC side of the inverter. Figure 6 and Figure 7 It can be seen that under the condition of parameter mismatch, the total harmonic distortion (THD) of the output current of the traditional control scheme reaches 4.65%, showing large current fluctuations and poor steady-state performance. Figure 8 and Fig. 9 It can be seen that under the same parameter mismatch condition, the control scheme of the present application can not only effectively deal with load inductance deviation, but also resist external disturbances, maintain system stability and high performance, and significantly reduce the THD of the output current to only 1.97%. This result shows that the control scheme of the present application improves the output current quality by 57.63% compared with the traditional control method. This further verifies that the scheme proposed in the present application has extremely high robustness in dealing with load parameter mismatch, and can effectively improve the output current quality, so that the equipment still has excellent control performance and reliability under complex working conditions.

[0049] An embodiment of the present application also provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods for controlling a power converter based on subspace predictive control are implemented.

[0050] Fig.10 It is a schematic diagram of the hardware structure of a computer device provided in an embodiment of the present application. Fig.10 The computer device shown includes: a processor 1001, a communication interface 1002, a memory 1003 and a communication bus 1004. The processor 1001, the communication interface 1002 and the memory 1003 communicate with each other via the communication bus 1004. Fig.10 The connection method between the processor 1001, the communication interface 1002, and the memory 1003 shown is merely exemplary. During implementation, the processor 1001, the communication interface 1002, and the memory 1003 may also be connected to each other in communication with each other using other connection methods besides the communication bus 1004.

[0051] The memory 1003 can be used to store a computer program, which may include instructions and data to implement the steps of any of the above methods for controlling a power converter based on subspace predictive control. In an embodiment of the present application, the memory 1003 may be various types of storage media, such as random access memory (RAM), read only memory (ROM), non-volatile RAM (NVRAM), programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), flash memory, optical storage, and registers. The memory 1003 may include a hard disk and / or a memory.

[0052] The processor 1001 may be a general-purpose processor, which may be a processor that performs specific steps and / or operations by reading and executing a computer program (e.g., a computer program) stored in a memory (e.g., memory 1003). The general-purpose processor may use data stored in the memory (e.g., memory 1003) during the execution of the steps and / or operations. The general-purpose processor may be, for example, but not limited to, a central processing unit (CPU). In addition, the processor 1001 may also be a dedicated processor, which may be a processor specially designed to perform specific steps and / or operations. The dedicated processor may be, for example, but not limited to, an ASIC and an FPGA. In addition, the processor 1001 may also be a combination of multiple processors, such as a multi-core processor.

[0053] The communication interface 1002 may include an input / output (I / O) interface, a physical interface, and a logical interface for interconnecting devices within the network device, as well as an interface for interconnecting the network device with other devices (e.g., network devices). The communication network may be Ethernet, a radio access network (RAN), a wireless local area network (WLAN), etc. The communication interface 1002 may be a module, a circuit, a transceiver, or any device capable of implementing communication.

[0054] In the implementation process, each step of the above method can be completed by an integrated logic circuit of the hardware in the processor 1001 or an instruction in the form of software. The method disclosed in conjunction with the embodiment of the present application can be directly embodied as a hardware processor for execution, or a combination of hardware and software modules in the processor for execution. The software module can be located in a mature storage medium in the art such as a random access memory flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 1003, and the processor 1001 reads the information in the memory 1003 and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it is not described in detail here.

[0055] The above description of the embodiments is to facilitate the understanding and application of the present application by those skilled in the art. It is obvious that those familiar with the art can easily make various modifications to the above embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present application is not limited to the above embodiments, and improvements and modifications made to the present application by those skilled in the art based on the disclosure of the present application should be within the scope of protection of the present application.

Claims

1. A control method for a power converter based on subspace predictive control, characterized in that: The method comprises: Constructing a subspace predictor model of the power converter based on a historical data matrix in a historical time window of the power converter, an output current in a future time window, and an input voltage in a future time window, wherein the historical data matrix includes a historical input voltage and a corresponding historical output current, and the subspace predictor model includes a linear part and a nonlinear part; Constructing the objective function of the linear part based on the historical data matrix, and determining the linear part prediction value of the current control cycle based on the objective function; Constructing a first nonlinear function of the nonlinear part, and constructing a second nonlinear function of the output current of the power converter according to the linear part prediction value of the current control cycle, estimating the first nonlinear function and the second nonlinear function by using a neural network, and determining the nonlinear part prediction value of the current control cycle; Determine a functional relationship for predicting output current in a future time window based on the linear part prediction value of the current control cycle, the nonlinear part prediction value of the current control cycle, and a subspace predictor model; Constructing an objective function with the functional relationship and the expected output current in a preset future time window, and determining an optimal input voltage of a current control cycle based on the objective function; A cost function is constructed using the optimal input voltage of the current control cycle and the voltage vectors corresponding to the various switching states of the power converter. When the value of the cost function is minimum, the voltage vector corresponding to the optimal input voltage of the current control cycle is determined, and the switching state corresponding to the voltage vector is used as the optimal switching state of the power converter in the current control cycle.

2. The control method of a power converter based on subspace predictive control according to claim 1, wherein the subspace predictor model of the power converter is constructed, comprising: defining a nonlinear portion of the subspace predictor model; A subspace predictor model of the power converter is constructed based on the historical data matrix within the historical time window of the power converter, the output current within the future time window, the input voltage within the future time window, and the nonlinear part, wherein the linear part of the subspace predictor model is determined based on the coefficient matrix of the subspace predictor model.

3. The control method of a power converter based on subspace predictive control according to claim 2, characterized in that: The constructing of the subspace predictor model of the power converter comprises: The subspace predictor model of the power converter is expressed as: ； ； ； ； ； Among them, k represents the current control cycle, represents the length of the historical time window, represents the length of the future time window, represents the predicted output current matrix in the future time window, represents the output current increment matrix of adjacent control cycles in the future time window, and represents the coefficient matrix, represents the input voltage matrix in the future time window, represents the input voltage increment matrix of adjacent control cycles in the future time window, represents the historical data matrix, and Represents the historical input voltage and the corresponding historical output current, Represents the historical input voltage increment matrix and the corresponding historical output current increment matrix of two adjacent control cycles in the historical time window, represents the nonlinear part of the subspace predictor model, represents a single nonlinear term, express A matrix with all 1s in one row and one column; by represents the linear part of the subspace predictor model.

4. The control method of a power converter based on subspace predictive control according to claim 2, characterized in that: The step of constructing the objective function of the linear part based on the historical data matrix and determining the linear part prediction value of the current control cycle based on the objective function includes: The objective function of the coefficient matrix of the linear part is constructed by using the increment of the historical data matrix and the predicted value of the nonlinear part of the previous control cycle; The objective function is partially derived to obtain a coefficient matrix of the linear part of the current control cycle, and a predicted value of the linear part of the current control cycle is determined based on the historical data matrix and the coefficient matrix of the linear part of the current control cycle.

5. The control method of a power converter based on subspace predictive control according to claim 3, characterized in that: The step of constructing the objective function of the linear part based on the historical data matrix and determining the linear part prediction value of the current control cycle based on the objective function includes: The objective function of the coefficient matrix of the linear part It is expressed as: ； ； in, represents the weight factor, express The increment of the historical data matrix at time , express The output current increment at the moment, express The input voltage increment at time Representation coefficient matrix The j-th row of data, represents the coefficient matrix prediction value of the current control cycle, Represents the predicted value of a single nonlinear term in the previous control period; make , then the coefficient matrix prediction value of the linear part of the current control cycle is It is expressed as: ； ； in, represents the step size factor, Represents the coefficient matrix prediction value of the previous control cycle; The coefficient matrix predictions based on the linear part of the current control cycle The linear part prediction value of the current control period is determined by using the historical data matrix.

6. The control method of a power converter based on subspace predictive control according to claim 4, characterized in that: The first nonlinear function and the second nonlinear function are estimated by using a neural network to determine a nonlinear part prediction value of a current control cycle, including: Define the weight matrix of the neural network and define the activation function of the neural network with current as parameter; Constructing a first nonlinear function of a single nonlinear term according to the weight matrix and the activation function; Constructing a second nonlinear function of the output current of the power converter according to the linear part prediction value of the current control cycle, the weight matrix and the activation function; Using a neural network to estimate the first nonlinear function and the second nonlinear function, an estimated value of the output current of the current control cycle, an estimated value of the weight matrix of the current control cycle, and estimated values ​​of each single nonlinear term of the current control cycle; Based on the estimated values ​​of each single nonlinear term of the current control cycle, the predicted value of the nonlinear part of the current control cycle is determined.

7. The control method of a power converter based on subspace predictive control according to claim 5, characterized in that: The first nonlinear function and the second nonlinear function are estimated by using a neural network to determine a nonlinear part prediction value of a current control cycle, including: The first nonlinear function of a single nonlinear term d is expressed as: ； ； The second nonlinear function of the output current of the power converter is expressed as: ； in, represents the output current of the power converter, represents the linear part prediction value of the current control cycle, represents the weight matrix of the neural network, represents the activation function of the neural network, represents the minimum error of the nonlinear term estimation, represents the minimum error of output current estimation; The first nonlinear function and the second nonlinear function are estimated using a neural network, and the estimated value of the nonlinear term d is and the estimated value of the output current of the power converter Respectively expressed as: ； ； ； in, , , represents a positive coefficient, represents the estimated value of the linear part prediction value, represents the estimated value of the weight matrix of the neural network, represents the error between the estimated value of the output current of the power converter and the actual output current of the power converter, represents the derivative of the estimated value of the weight matrix of the neural network; The estimated value of the nonlinear term d , estimated value of output current of power converter and the derivative of the estimate of the weight matrix Perform first-order Euler discretization respectively and obtain the following expressions: ； in, represents the estimated value of the output current in the current control cycle, represents the estimated value of the weight matrix of the current control cycle, represents the estimated value of the linear part prediction value of the current control period, represents the length of the control cycle, represents the estimated value of a single nonlinear term for the current control period; The nonlinear part of the subspace predictor model predicted value for the current control period It is expressed as: 。 8. The control method of a power converter based on subspace predictive control according to claim 7, characterized in that: The step of determining a functional relationship for predicting the output current in a future time window based on the linear part prediction value of the current control cycle, the nonlinear part prediction value of the current control cycle, and the subspace predictor model includes: Substitute the linear part prediction value of the current control cycle and the nonlinear part prediction value of the current control cycle into the expression of the subspace predictor model to obtain the predicted output current matrix in the future time window: The functional relationship is: ； ； ； in, represents an m×m dimensional matrix, the diagonal elements of the matrix are 1, and the remaining elements are 0, and m represents the data dimension of the system input and output; The step of constructing an objective function based on the functional relationship and the expected output current in a preset future time window, and determining the optimal input voltage of the current control cycle based on the objective function includes: The predicted output current matrix The objective function is constructed by using the functional relationship of the expected output current matrix in the preset future time window. It is expressed as: ； ； in, represents the expected output current matrix within the preset future time window, represents the expected output current at time k+1 obtained based on vector angle extrapolation, represents the input voltage matrix in the future time window, and represents the diagonal weight matrix; when According to the objective function Get the input voltage increment matrix in the future time window for: ； ； ； by The sum of the first matrix element of and the optimal input voltage of the previous control cycle is used as the optimal input voltage to the current control cycle. .

9. The control method of a power converter based on subspace predictive control according to claim 8, characterized in that: The cost function is constructed based on the optimal input voltage of the current control cycle and the voltage vector corresponding to each switch state of the power converter, including: The cost function is constructed by using the optimal input voltage of the current control cycle and the voltage vector corresponding to each switching state of the power converter. The expression is: ； in, represents the voltage vector corresponding to each switching state of the power converter, and n represents n switching states; Based on the cost function Calculate the optimal input voltage for each voltage vector and the current control cycle The square of the absolute value between and is used to determine the voltage vector corresponding to the minimum value, and the switching state corresponding to the voltage vector is used as the optimal switching state of the power converter in the current control cycle.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method for controlling a power converter based on subspace predictive control as described in any one of claims 1 to 9 are implemented.

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