Converter robust control method and device based on Kalman filtering

Abstract

The invention discloses a robust control method and device for a converter based on Kalman filtering in the technical field of power electronics. The method comprises the following steps: constructing a robust prediction model for the alternating current side current of the converter; performing approximate estimation on the robust prediction model based on a Kalman filtering direct estimation method to obtain a current prediction model; constructing a voltage recursion model of the neutral-point potential of the direct-current side of the converter; constructing a predictive control cost function based on the current prediction model and the voltage recursion model; at each sampling moment, enumerating the switching state formed by the three phases of the converter, calculating the cost value of each enumeration result based on the predictive control cost function, and replacing the switching state with the minimum value as the control action of the converter at the next moment; according to the method, the system model can be approximated based on the Kalman filtering method, so that accurate control is realized under the condition that the system model does not have accurate priori knowledge or is not accurate.

Description

Technical Field

[0001] The present invention relates to the field of power electronics technology, and in particular to a converter robust control method and device based on Kalman filtering. Background Art

[0002] The advantage of flexible interconnection devices is that they can achieve efficient connection and flexible power transmission between different power grids, and have good adaptability and scalability, and can optimize system performance under various operating environments. Efficient control of flexible interconnection devices is a prerequisite for the large-scale application of flexible interconnection devices.

[0003] Compared with traditional control methods, model predictive control (MPC) has obvious advantages in dealing with multi-objective optimization, nonlinear problems and strict constraints. It can consider multiple objectives simultaneously and directly calculate the global optimal solution through the design of the cost function. However, the accurate solution of the cost function has always been a difficulty of MPC. In this regard, finite set model predictive control (FCS-MPC) has been more widely promoted in practical applications because it better adapts to the discrete switching characteristics of the converter and avoids the complexity of the solution to a certain extent by traversing the solution instead of the direct solution.

[0004] However, the effectiveness of MPC is largely limited by the accuracy of the model. If the system model is biased or incomplete, the control effect will be significantly reduced. In the control of power electronic converters, the dynamic characteristics of the equipment may be affected by factors such as environmental changes, parameter uncertainty or system aging, resulting in model parameter mismatch, which in turn degrades the control conditions. Therefore, a robust model predictive control scheme is needed to correct or approximate the model used. Summary of the invention

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art and to provide a converter robust control method and device based on Kalman filtering. The method based on Kalman filtering approximates the system model, thereby achieving accurate control in the absence of accurate prior knowledge of the system model or an inaccurate system model.

[0006] To achieve the above object, the present invention is implemented by adopting the following technical solutions: In a first aspect, the present invention provides a converter robust control method based on Kalman filtering, comprising: Construct a robust prediction model for the AC side current of the converter; The robust prediction model is approximately estimated based on a Kalman filter direct estimation method to obtain a current prediction model; Construct a voltage recursive model of the midpoint potential on the DC side of the converter; Constructing a prediction control cost function based on the current prediction model and the voltage recursive model; At each sampling moment, the switch states of the three-phase structure of the converter are enumerated, and the cost value of each enumeration result is calculated based on the predictive control cost function, and the switch state with the smallest value is replaced as the control action of the converter at the next moment.

[0007] Optionally, the converter is a three-level ANPC converter, comprising three bridge arms connected in parallel, wherein the bridge arms comprise a switch tube connected in series between the positive and negative electrodes of the DC bus. , switch tube , switch tube And switch tube ; The switch tubes of the three bridge arms And switch tube The common end is connected to the three phases of the AC side bus respectively.

[0008] Optionally, a voltage dividing capacitor is connected in series between the positive and negative poles of the DC busbar. And the voltage divider capacitor The bridge arm also includes a switch tube connected in series and the switch tube The common terminal of the switch tube and the switch tube The switch tube between the common terminals And switch tube , the voltage divider capacitor and the voltage divider capacitor The common terminal and the switch tube and the switch tube The common end is connected to the midpoint potential.

[0009] Optionally, the robust prediction model of the converter AC side current is: ; In the formula, is the current value of the converter AC side, is continuous time, are the input coefficients, input parameters, and the nonlinear function to be estimated.

[0010] Optionally, performing approximate estimation on the robust prediction model based on a Kalman filter direct estimation method to obtain the current prediction model includes: set up , build a state observation model: ; In the formula, are state observations and observation matrices, , , is the measurement error, is the measurement error The covariance of is the sampling time; The Kalman filter expression is constructed based on the state observation model: ; ; ; In the formula, For the The covariance matrix of the sampling time errors is, For the The covariance matrix of the sampling moments The predicted value of For the The sampling time is used to observe the state The predicted value of is the Kalman gain; According to the Kalman filter expression The current prediction model can be obtained by estimating and discretizing the robust prediction model: ; In the formula, is the sampling period, For the The current value of the converter AC side at a sampling moment.

[0011] Optionally, the voltage recursion model of the midpoint potential of the DC side of the converter is: ; In the formula, For the The midpoint potential voltage at each sampling moment is is the sampling period, For the The midpoint potential current value at each sampling moment is: is the capacitance value of the capacitors on both sides of the midpoint potential; ; In the formula, For the The converter with sampling time The total switching state of the phase output, For the The AC side of the converter at each sampling moment Phase current value.

[0012] Optionally, the predictive control cost function is: ; In the formula, For the The converter AC side current reference value and current value at each sampling moment, is the weight factor, For the The midpoint potential voltage at the sampling moment.

[0013] In a second aspect, the present invention provides a converter robust control device based on Kalman filtering, comprising: A current model building module is configured to build a robust prediction model of the AC side current of the converter; A current model estimation module is configured to perform approximate estimation on the robust prediction model based on a Kalman filter direct estimation method to obtain a current prediction model; A voltage model building module is configured to build a voltage recursive model of the midpoint potential of the DC side of the converter; A cost function construction module, configured to construct a prediction control cost function based on the current prediction model and the voltage recursive model; The switch state calculation module is configured to enumerate the switch states of the three-phase converter at each sampling moment, and calculate the cost value of each enumeration result based on the predictive control cost function, and replace the switch state with the smallest value as the control action of the converter at the next moment.

[0014] In a third aspect, the present invention provides an electronic device, including a processor and a storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps according to the above method.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a converter robust control method and device based on Kalman filtering, which is applied to the control of a converter of a flexible interconnected device. The traditional model predictive control can better handle the control problem of multiple targets, but it is dependent on the system model. The present invention improves it and uses a method based on Kalman filtering to approximate the system model, thereby achieving accurate control when there is no accurate prior knowledge of the system model or the system model is inaccurate. The present invention can be widely used in the field of power electronic converter control. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a circuit diagram of a three-level ANPC converter provided by an embodiment of the present invention; Figure 2 is a flow chart of a converter robust control method based on Kalman filtering provided by an embodiment of the present invention; Figure 3 is a logic schematic diagram of a converter robust control method based on Kalman filtering provided by an embodiment of the present invention; Figure 4 It is a schematic diagram comparing three-phase current and its error between the traditional method provided by the embodiment of the present invention and the method of the embodiment of the present invention. DETAILED DESCRIPTION

[0018] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0019] Embodiment 1:

[0020] like Figure 1 As shown, an embodiment of the present invention provides a three-level ANPC converter, including three bridge arms ANPC1, ANPC2, and ANPC3 connected in parallel, wherein the bridge arm includes a switch tube connected in series between the positive and negative electrodes of the DC side bus. , switch tube , switch tube And switch tube ; The switch tubes of the three bridge arms And switch tube The common end is connected to the three phases of the AC bus. A voltage dividing capacitor is also connected in series between the positive and negative poles of the DC bus. And the voltage divider capacitor The bridge arm also includes a switch tube connected in series And switch tube The common terminal and the switch tube And switch tube The switch tube between the common terminals And switch tube , voltage divider capacitor And the voltage divider capacitor The common terminal and switch tube And switch tube The common end is connected to the midpoint potential O .

[0021] like Figure 2 and 3 As shown, for the above three-level ANPC converter, an embodiment of the present invention provides a converter robust control method based on Kalman filtering, comprising the following steps: Step S1: construct a robust prediction model for the AC side current of the converter.

[0022] The use of model predictive control requires understanding of the mathematical model of the system. This embodiment assumes that there is no accurate understanding of the mathematical model of the system and uses the Kalman filter solution for approximation to improve the robustness of the control. The initial model is given as follows: The robust prediction model of the converter AC side current is: ; In the formula, is the current value of the converter AC side, is continuous time, are the input coefficients, input parameters, and the nonlinear function to be estimated.

[0023] If you can By making an accurate estimate, it is possible to implement model predictive control based on the estimated model, so the Kalman filter direct estimation method is designed to approximate the above model.

[0024] Step S2: Approximately estimate the robust prediction model based on the Kalman filter direct estimation method to obtain the current prediction model. The specific steps include: set up , build a state observation model: ; In the formula, are state observations and observation matrices, , , is the measurement error, is the measurement error The covariance of is the sampling time; Construct the Kalman filter expression based on the state observation model: ; ; ; In the formula, For the The covariance matrix of the sampling time errors is, For the The covariance matrix of the sampling moments The predicted value of For the The sampling time is used to observe the state The predicted value of is the Kalman gain; According to the Kalman filter expression The current prediction model can be obtained by estimating and discretizing the robust prediction model: ; In the formula, is the sampling period, For the The current value of the converter AC side at a sampling moment.

[0025] Step S3: construct a voltage recursive model of the midpoint potential on the DC side of the converter.

[0026] For general ANPC converters, in addition to accurate control of the AC current, the control objective is also to balance the DC capacitor voltage. To this end, it is necessary to establish a DC capacitor voltage recursive model. Assume that the upper and lower capacitor voltages are ,set up represents the midpoint potential of ANPC, assuming that the DC side capacitors have the same value and their capacitance is , the voltage recursive model of the midpoint potential on the DC side of the converter is: ; In the formula, For the The midpoint potential voltage at each sampling moment is is the sampling period, For the The midpoint potential current value at each sampling moment is: is the capacitance value of the capacitors on both sides of the midpoint potential; ; In the formula, For the The converter with sampling time The total switching state of the phase output, For the The AC side of the converter at each sampling moment Phase current value.

[0027] Step S4: construct a prediction control cost function based on the current prediction model and the voltage recursive model.

[0028] The main objectives of the present invention are current estimation without model, midpoint potential balance, and voltage balance of the DC bus. The design predictive control cost function is: ; In the formula, For the The converter AC side current reference value and current value at each sampling moment, is the weight factor, For the The midpoint potential voltage at each sampling moment. The weight factor is adjusted according to the actual system.

[0029] Step S5: At each sampling moment, enumerate the switch states of the three-phase structure of the converter, and calculate the cost value of each enumeration result based on the predictive control cost function, and replace the switch state with the smallest value as the control action of the converter at the next moment.

[0030] This embodiment provides a converter robust control method based on Kalman filtering. In actual online control, the signals that need to be measured at each moment are the current bus current on the AC side and the midpoint potential voltage on the DC side. First, the Kalman filter parameters are updated according to the error between the current sampling and the estimated value at the previous moment. Then, the updated Kalman filter parameters are used to calculate and obtain the parameters of each system model after real-time correction using the calculation method of this article. Finally, the corrected model and the existing midpoint potential model are substituted into the predictive control for simultaneous calculation. By enumerating and traversing the optimization method, the optimal switch state at the next moment is predicted, and the obtained optimal switch state is output to the next control cycle. Repeat the above process in each control cycle to achieve accurate control.

[0031] like Figure 4 As shown, a schematic diagram comparing three-phase current and its error between the conventional method and the method of the embodiment of the present invention is shown. The total harmonic current distortion THD and error of the method of the embodiment are both smaller than those of the conventional method.

[0032] Embodiment 2:

[0033] The embodiment of the present invention provides a converter robust control device based on Kalman filtering, comprising: A current model building module is configured to build a robust prediction model of the AC side current of the converter; A current model estimation module is configured to perform approximate estimation on the robust prediction model based on a Kalman filter direct estimation method to obtain a current prediction model; A voltage model building module is configured to build a voltage recursive model of the midpoint potential of the DC side of the converter; A cost function building module is configured to build a predictive control cost function based on a current prediction model and a voltage recursive model; The switch state calculation module is configured to enumerate the switch states of the three-phase converter at each sampling moment, and calculate the cost value of each enumeration result based on the predictive control cost function, and replace the switch state with the smallest value as the control action of the converter at the next moment.

[0034] Embodiment three:

[0035] Based on the first embodiment, the embodiment of the present invention provides an electronic device, including a processor and a storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instructions to execute the steps according to the above method.

[0036] Embodiment 4:

[0037] Based on the first embodiment, the embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented.

[0038] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0039] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0040] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0041] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0042] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A converter robust control method based on Kalman filtering, characterized in that: include: Construct a robust prediction model for the AC side current of the converter; The robust prediction model is approximately estimated based on a Kalman filter direct estimation method to obtain a current prediction model; Construct a voltage recursive model of the midpoint potential on the DC side of the converter; Constructing a prediction control cost function based on the current prediction model and the voltage recursive model; At each sampling moment, the switch states of the three-phase structure of the converter are enumerated, and the cost value of each enumeration result is calculated based on the predictive control cost function, and the switch state with the smallest value is replaced as the control action of the converter at the next moment.

2. The converter robust control method based on Kalman filtering according to claim 1, characterized in that: The converter is a three-level ANPC converter, comprising three bridge arms connected in parallel, each of which comprises a switch tube connected in series between the positive and negative electrodes of the DC bus. , switch tube , switch tube And switch tube ; The switch tubes of the three bridge arms And switch tube The common end is connected to the three phases of the AC side bus respectively.

3. The converter robust control method based on Kalman filtering according to claim 2 is characterized in that: A voltage dividing capacitor is also connected in series between the positive and negative poles of the DC busbar. And the voltage divider capacitor The bridge arm also includes a and the switch tube The common terminal of the switch tube and the switch tube The switch tube between the common terminals And switch tube , the voltage divider capacitor and the voltage divider capacitor The common terminal and the switch tube and the switch tube The common end is connected to the midpoint potential.

4. The converter robust control method based on Kalman filtering according to claim 1, characterized in that: The robust prediction model of the converter AC side current is: ； In the formula, is the current value of the converter AC side, is continuous time, are the input coefficients, input parameters, and the nonlinear function to be estimated.

5. The converter robust control method based on Kalman filtering according to claim 4 is characterized in that: The method of performing approximate estimation on the robust prediction model based on the Kalman filter direct estimation method to obtain the current prediction model includes: set up , build a state observation model: ； In the formula, are state observations and observation matrices, , , is the measurement error, is the measurement error The covariance of is the sampling time; The Kalman filter expression is constructed based on the state observation model: ； ； ； In the formula, For the The covariance matrix of the sampling time errors is, For the The covariance matrix of the sampling moments The predicted value of For the The sampling time is used to observe the state The predicted value of is the Kalman gain; According to the Kalman filter expression The current prediction model can be obtained by estimating and discretizing the robust prediction model: ； In the formula, is the sampling period, For the The current value of the converter AC side at a sampling moment.

6. The converter robust control method based on Kalman filtering according to claim 1, characterized in that: The voltage recursive model of the midpoint potential on the DC side of the converter is: ； In the formula, For the The midpoint potential voltage at each sampling moment, is the sampling period, For the The midpoint potential current value at each sampling moment is: is the capacitance value of the capacitors on both sides of the midpoint potential; ； In the formula, For the The converter with sampling time The total switching state of the phase output, For the The AC side of the converter at each sampling moment Phase current value.

7. The converter robust control method based on Kalman filtering according to claim 1, characterized in that: The predictive control cost function is: ； In the formula, For the The converter AC side current reference value and current value at each sampling moment, is the weight factor, For the The midpoint potential voltage at the sampling moment.

8. A converter robust control device based on Kalman filtering, characterized in that: include: A current model building module is configured to build a robust prediction model of the AC side current of the converter; A current model estimation module is configured to perform approximate estimation on the robust prediction model based on a Kalman filter direct estimation method to obtain a current prediction model; A voltage model building module is configured to build a voltage recursive model of the midpoint potential of the DC side of the converter; A cost function construction module, configured to construct a prediction control cost function based on the current prediction model and the voltage recursive model; The switch state calculation module is configured to enumerate the switch states of the three-phase converter at each sampling moment, and calculate the cost value of each enumeration result based on the predictive control cost function, and replace the switch state with the smallest value as the control action of the converter at the next moment.

9. An electronic device, characterized in that: including processor and storage medium; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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