阅读说明：本技术 一种储能换流装置模型预测控制方法及系统 (Energy storage commutation device model prediction control method and system ) 是由 符平 喻林波 张建伟 周自林 冯国强 于 2021-09-15 设计创作，主要内容包括：本发明提供的一种储能换流器模型预测控制方法及系统,该方法包括：建立k+2时刻储能换流器功率MPC数学模型；根据滤波电感偏差量建立储能换流器输出电压最优值偏差方程；根据k+2时刻储能换流器功率MPC数学模型及储能换流器输出电压最优值偏差方程,得到电感误差补充后的k+2时刻储能换流器功率MPC数学模型；将电感误差补充后的k+2时刻储能换流器功率MPC数学模型与k+2时刻储能换流器功率MPC数学模型作差,得到k+2时刻电感观测值方程；将k+2时刻电感观测值方程转换为k时刻电感观测值方程,根据k时刻电感观测值方程对MPC控制系统电感参数进行实时修正。通过实施本发明,减少预测误差,保证控制精度。(The invention provides a model prediction control method and a system for an energy storage converter, wherein the method comprises the following steps: establishing a power MPC mathematical model of the energy storage converter at the moment k + 2; establishing an optimal value deviation equation of the output voltage of the energy storage converter according to the filter inductance deviation amount; obtaining a k +2 moment energy storage converter power MPC mathematical model after inductance error supplement according to the k +2 moment energy storage converter power MPC mathematical model and an energy storage converter output voltage optimal value deviation equation; subtracting the k +2 moment energy storage converter power MPC mathematical model after the inductance error is supplemented with the k +2 moment energy storage converter power MPC mathematical model to obtain a k +2 moment inductance observed value equation; and converting the k +2 moment inductance observed value equation into a k moment inductance observed value equation, and correcting the inductance parameters of the MPC control system in real time according to the k moment inductance observed value equation. By implementing the method and the device, the prediction error is reduced, and the control precision is ensured.)

1. A model predictive control method for an energy storage converter is characterized by comprising the following steps:

establishing a power MPC mathematical model of the energy storage converter at the moment k +2 based on the two-step model prediction logic;

acquiring a filter inductance deviation amount of the energy storage converter, and establishing an optimal value deviation equation of the output voltage of the energy storage converter according to the filter inductance deviation amount;

obtaining a k +2 moment energy storage converter power MPC mathematical model after inductance error supplement according to a k +2 moment energy storage converter power MPC mathematical model and an energy storage converter output voltage optimal value deviation equation;

subtracting the k +2 moment energy storage converter power MPC mathematical model after the inductance error is supplemented with the k +2 moment energy storage converter power MPC mathematical model to obtain a k +2 moment inductance observed value equation;

and converting the k +2 moment inductance observed value equation into a k moment inductance observed value equation, and correcting the inductance parameters of the MPC control system in real time according to the k moment inductance observed value equation.

2. The energy storage converter model prediction control method according to claim 1, wherein the establishing of the k +2 moment energy storage converter power MPC mathematical model based on the two-step model prediction logic comprises:

establishing an energy storage converter mathematical model;

obtaining a network side voltage equation of the mathematical model of the energy storage converter according to the mathematical model of the energy storage converter;

obtaining instantaneous active power and reactive power mathematical models according to a network side voltage equation of the energy storage converter mathematical model;

establishing an instantaneous active power and reactive power change rate mathematical model according to the instantaneous active power and reactive power mathematical model;

obtaining an MPC mathematical model of the energy storage converter according to a network side voltage equation and an instantaneous active power and reactive power change rate mathematical model of the energy storage converter mathematical model;

discretizing the mathematical model of the power MPC of the energy storage converter, and adopting two-step model prediction logic to obtain the mathematical model of the power MPC of the energy storage converter at the k +2 moment.

3. The energy storage converter model prediction control method according to claim 1, characterized in that the mathematical model of the energy storage converter power MPC at the time k +2 is:

wherein P (k +2) is active power at a time k +2 under the two-phase stationary alpha beta coordinate system, Q (k +2) is reactive power at a time k +2 under the two-phase stationary alpha beta coordinate system, P (k +1) is active power at a time k +1 under the two-phase stationary alpha beta coordinate system, Q (k +1) is reactive power at a time k +1 under the two-phase stationary alpha beta coordinate system, and T (k +1) is reactive power at a time k +1 under the two-phase stationary alpha beta coordinate system, and_sfor controlling the period, R_fFor internal resistance, L, of the energy-storing converter_fFor the internal inductance of the energy-storage converter, omega is the angular frequency of the power grid, U_αIs an energy storage converter under a two-phase static alpha coordinate systemOutput voltage U_βIs the output voltage, e, of the energy storage converter in a two-phase stationary beta coordinate system_α(k +1) is the grid side voltage of the energy storage converter at the moment of k +1 under the two-phase static alpha coordinate system, e_βAnd (k +1) is the voltage of the grid side of the energy storage converter at the moment of k +1 under the two-phase static beta coordinate system.

4. The model predictive control method for the energy storage converter according to claim 1, characterized in that the deviation equation of the optimal value of the output voltage of the energy storage converter is as follows:

wherein, Delta U_α(k +1) is the optimal value of the output voltage of the energy storage converter at the moment of k +1 under the two-phase static alpha coordinate system, and delta U_β(k +1) is the optimal value of the output voltage of the energy storage converter at the moment of k +1 under the two-phase static beta coordinate system, and delta L_fAs a filter inductance deviation, i_β(k +1) is the output current of the energy storage converter at the moment of k +1 under the two-phase static beta coordinate system, i_α(k +1) is output current of the energy storage converter at the moment of k +1 under the two-phase static alpha coordinate system, and epsilon_P(k +1) is an active power step function at the moment k +1 under a two-phase stationary alpha beta coordinate system, and epsilon_QAnd (k +1) is a reactive power step function at the moment k +1 under a two-phase stationary alpha beta coordinate system.

5. The model predictive control method for the energy storage converter according to claim 1, characterized in that the mathematical model of the k +2 moment energy storage converter power MPC after the inductance error is supplemented is:

wherein, P_m(k +2) is active power after the inductance error at the k +2 moment is supplemented under a two-phase static alpha beta coordinate system, and Q is_m(k +2) is reactive power after the inductance error at the k +2 moment is supplemented under a two-phase static alpha beta coordinate system, and L is_fgTo filter electricitySense of actual value, L_fmFor filter inductance parameter settings, L_fg＝L_fm+ΔL_f。

6. The predictive control method for the energy storage converter model according to claim 1, wherein an inductance observed value equation at the time k is as follows:

wherein L is_f0The inductance observations.

7. The predictive control method of an energy storage converter model according to claim 2, characterized in that the mathematical model of the energy storage converter is:

wherein i_αIs the output current, i, of the energy storage converter under the two-phase static alpha coordinate system_βFor two-phase stationary beta-coordinate system energy-storage converter output current, e_αIs the network side voltage, e of the energy storage converter under the two-phase static alpha coordinate system_βThe voltage of the network side of the energy storage converter under the two-phase static beta coordinate system.

8. An energy storage converter model predictive control system, comprising:

the first model establishing module is used for establishing a power MPC mathematical model of the energy storage converter at the moment of k +2 based on the two-step model prediction logic;

the second model establishing module is used for acquiring the filter inductance deviation amount of the energy storage converter and establishing an optimal value deviation equation of the output voltage of the energy storage converter according to the filter inductance deviation amount;

the third model establishing module is used for obtaining a k +2 moment energy storage converter power MPC mathematical model after inductance error supplement according to the k +2 moment energy storage converter power MPC mathematical model and the energy storage converter output voltage optimal value deviation equation;

the fourth model establishing module is used for making a difference between the k +2 moment energy storage converter power MPC mathematical model after the inductance error is supplemented and the k +2 moment energy storage converter power MPC mathematical model to obtain a k +2 moment inductance observed value equation;

and the control module is used for converting the k +2 moment inductance observed value equation into a k moment inductance observed value equation and correcting the inductance parameters of the MPC control system in real time according to the k moment inductance observed value equation.

9. A computer readable storage medium storing computer instructions for causing the computer to perform the method of model predictive control of an energy storage converter according to any one of claims 1 to 7.

10. A computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the energy storage converter model predictive control method according to any one of claims 1 to 7.

Technical Field

The invention relates to the technical field of model prediction, in particular to a model prediction control method and system for an energy storage commutation device.

Background

As an energy storage technology, which is one of key technologies of energy revolution, the energy storage technology has received wide attention in the industry in recent years because it can provide a variety of auxiliary services such as peak shaving, frequency modulation, emergency response and the like for the power grid. In order to realize friendly grid connection of an energy storage system and provide stable voltage and frequency support for a power grid, research on a control strategy of an energy storage converter needs to be developed.

At present, in the field of energy storage converter control, the dynamic response of energy storage voltage and frequency is realized by mostly adopting double closed-loop control and dead-beat control. But the conventional control strategy cannot maintain the stability of the energy storage converter control system under the high permeability of the distributed power supply. For example, when the energy storage converter faces the condition of frequent voltage and frequency adjustment, the switching frequency is high, and the conventional control delay is caused by sampling, calculating, zero-order holding and Pulse Width Modulation (PWM). If the control system cannot suppress the delay in time, the system bandwidth is greatly reduced, and the whole control system is unstable. MPC (MPC model Predictive control) is a state variable prediction algorithm, the delay influence is reduced by a control algorithm, and the control bandwidth of the system is not influenced because the number of switch states does not need to be considered, so that the operation requirement is reduced, and the MPC (MPC model Predictive control) is widely applied to an energy storage converter control system.

However, the energy storage commutation device MPC is easily affected by the filter inductance parameter, and when the filter inductance parameter has a deviation, the prediction error of the energy storage commutation device MPC is large, which affects the stable operation of the energy storage commutation device MPC.

Disclosure of Invention

Therefore, the technical problem to be solved by the present invention is to overcome the defect that the stability of the energy storage commutation device is affected by the filter inductance parameter of the energy storage commutation device MPC in the prior art, so as to provide a model prediction control method and system for the energy storage commutation device MPC.

In order to achieve the purpose, the invention provides the following technical scheme:

in a first aspect, an embodiment of the present invention provides a method for model predictive control of an energy storage converter, including:

establishing a power MPC mathematical model of the energy storage converter at the moment k +2 based on the two-step model prediction logic;

acquiring a filter inductance deviation amount of the energy storage converter, and establishing an optimal value deviation equation of the output voltage of the energy storage converter according to the filter inductance deviation amount;

obtaining a k +2 moment energy storage converter power MPC mathematical model after inductance error supplement according to a k +2 moment energy storage converter power MPC mathematical model and an energy storage converter output voltage optimal value deviation equation;

subtracting the k +2 moment energy storage converter power MPC mathematical model after the inductance error is supplemented with the k +2 moment energy storage converter power MPC mathematical model to obtain a k +2 moment inductance observed value equation;

and converting the k +2 moment inductance observed value equation into a k moment inductance observed value equation, and correcting the inductance parameters of the MPC control system in real time according to the k moment inductance observed value equation.

Optionally, the establishing a mathematical model of the energy storage converter power MPC at the k +2 moment based on the two-step model prediction logic includes:

establishing an energy storage converter mathematical model;

obtaining a network side voltage equation of the mathematical model of the energy storage converter according to the mathematical model of the energy storage converter;

obtaining instantaneous active power and reactive power mathematical models according to a network side voltage equation of the energy storage converter mathematical model;

establishing an instantaneous active power and reactive power change rate mathematical model according to the instantaneous active power and reactive power mathematical model;

obtaining an MPC mathematical model of the energy storage converter according to a network side voltage equation and an instantaneous active power and reactive power change rate mathematical model of the energy storage converter mathematical model;

discretizing the mathematical model of the power MPC of the energy storage converter, and adopting two-step model prediction logic to obtain the mathematical model of the power MPC of the energy storage converter at the k +2 moment.

Optionally, the mathematical model of the power MPC of the energy storage converter at the time k +2 is as follows:

wherein P (k +2) is active power at a time k +2 under the two-phase stationary alpha beta coordinate system, Q (k +2) is reactive power at a time k +2 under the two-phase stationary alpha beta coordinate system, P (k +1) is active power at a time k +1 under the two-phase stationary alpha beta coordinate system, Q (k +1) is reactive power at a time k +1 under the two-phase stationary alpha beta coordinate system, and T (k +1) is reactive power at a time k +1 under the two-phase stationary alpha beta coordinate system, and_sfor controlling the period, R_fFor internal resistance, L, of the energy-storing converter_fFor the internal inductance of the energy-storage converter, omega is the angular frequency of the power grid, U_αIs the output voltage, U, of the energy storage converter under the two-phase static alpha coordinate system_βIs the output voltage, e, of the energy storage converter in a two-phase stationary beta coordinate system_α(k +1) is the grid side voltage of the energy storage converter at the moment of k +1 under the two-phase static alpha coordinate system, e_βAnd (k +1) is the voltage of the grid side of the energy storage converter at the moment of k +1 under the two-phase static beta coordinate system.

Optionally, the energy storage converter output voltage optimal value deviation equation is as follows:

wherein, Delta U_α(k +1) is the optimal value of the output voltage of the energy storage converter at the moment of k +1 under the two-phase static alpha coordinate system, and delta U_β(k +1) is the optimal value of the output voltage of the energy storage converter at the moment of k +1 under the two-phase static beta coordinate system, and delta L_fAs a filter inductance deviation, i_β(k +1) is the output current of the energy storage converter at the moment of k +1 under the two-phase static beta coordinate system, i_α(k +1) is output current of the energy storage converter at the moment of k +1 under the two-phase static alpha coordinate system, and epsilon_P(k +1) is an active power step function at the moment k +1 under a two-phase stationary alpha beta coordinate system, and epsilon_QAnd (k +1) is a reactive power step function at the moment k +1 under a two-phase stationary alpha beta coordinate system.

Optionally, the mathematical model of the k +2 moment energy storage converter power MPC after the inductance error is supplemented is as follows:

wherein, P_m(k +2) is active power after the inductance error at the k +2 moment is supplemented under a two-phase static alpha beta coordinate system, and Q is_m(k +2) is reactive power after the inductance error at the k +2 moment is supplemented under a two-phase static alpha beta coordinate system, and L is_fgIs the actual value of the filter inductance, L_fmFor filter inductance parameter settings, L_fg＝L_fm+ΔL_f。

Optionally, the inductance observed value equation at the time k is:

wherein L is_f0The inductance observations.

Optionally, the energy storage converter mathematical model is:

wherein i_αIs the output current, i, of the energy storage converter under the two-phase static alpha coordinate system_βFor two-phase stationary beta-coordinate system energy-storage converter output current, e_αIs the network side voltage, e of the energy storage converter under the two-phase static alpha coordinate system_βThe voltage of the network side of the energy storage converter under the two-phase static beta coordinate system.

In a second aspect, an embodiment of the present invention provides an energy storage converter model prediction control system, including:

the first model establishing module is used for establishing a power MPC mathematical model of the energy storage converter at the moment of k +2 based on the two-step model prediction logic;

the second model establishing module is used for acquiring the filter inductance deviation amount of the energy storage converter and establishing an optimal value deviation equation of the output voltage of the energy storage converter according to the filter inductance deviation amount;

the third model establishing module is used for obtaining a k +2 moment energy storage converter power MPC mathematical model after inductance error supplement according to the k +2 moment energy storage converter power MPC mathematical model and the energy storage converter output voltage optimal value deviation equation;

the fourth model establishing module is used for making a difference between the k +2 moment energy storage converter power MPC mathematical model after the inductance error is supplemented and the k +2 moment energy storage converter power MPC mathematical model to obtain a k +2 moment inductance observed value equation;

and the control module is used for converting the k +2 moment inductance observed value equation into a k moment inductance observed value equation and correcting the inductance parameters of the MPC control system in real time according to the k moment inductance observed value equation.

In a third aspect, the present invention provides a computer-readable storage medium, where the computer-readable storage medium stores computer instructions for causing the computer to execute the energy storage converter model prediction control method according to the first aspect of the present invention.

In a fourth aspect, an embodiment of the present invention provides a computer device, including: the energy storage converter model prediction control method comprises a memory and a processor, wherein the memory and the processor are connected with each other in a communication mode, the memory stores computer instructions, and the processor executes the computer instructions so as to execute the energy storage converter model prediction control method according to the first aspect of the embodiment of the invention.

The technical scheme of the invention has the following advantages:

the invention provides a model prediction control method of an energy storage converter, which comprises the following steps: establishing a power MPC mathematical model of the energy storage converter at the moment k +2 based on the two-step model prediction logic; acquiring the deviation amount of the filter inductance of the energy storage converter, and establishing an optimal value deviation equation of the output voltage of the energy storage converter according to the deviation amount of the filter inductance; obtaining a k +2 moment energy storage converter power MPC mathematical model after inductance error supplement according to the k +2 moment energy storage converter power MPC mathematical model and an energy storage converter output voltage optimal value deviation equation; subtracting the k +2 moment energy storage converter power MPC mathematical model after the inductance error is supplemented with the k +2 moment energy storage converter power MPC mathematical model to obtain a k +2 moment inductance observed value equation; and converting the k +2 moment inductance observed value equation into a k moment inductance observed value equation, and correcting the inductance parameters of the MPC control system in real time according to the k moment inductance observed value equation. By solving the k-time inductance observation value equation and correcting the inductance parameters of the MPC control system in real time according to the k-time inductance observation value, the prediction error is reduced and the control precision is ensured.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

Fig. 1 is a flowchart of a specific example of a predictive control method for an energy storage converter model according to an embodiment of the present invention;

FIG. 2 is a circuit topology diagram of an energy storage converter according to an embodiment of the present invention;

fig. 3 is a block diagram illustrating an inductance observation control of an energy storage converter MPC according to an embodiment of the present invention;

fig. 4 is a schematic block diagram of a specific example of a model predictive control system for an energy storage converter in an embodiment of the invention;

fig. 5 is a block diagram of a specific example of a computer device according to an embodiment of the present invention.

Detailed Description

The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.

In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.

In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

The embodiment of the invention provides a model prediction control method for an energy storage converter, which comprises the following steps as shown in figure 1:

step S1: and establishing a power MPC mathematical model of the energy storage converter at the moment k +2 based on the two-step model prediction logic.

In an embodiment, the mathematical model of the power MPC of the energy storage converter at the time k +2 is specifically established by the following steps.

Step S11: and establishing a mathematical model of the energy storage converter.

Step S12: and obtaining a network side voltage equation of the mathematical model of the energy storage converter according to the mathematical model of the energy storage converter.

Step S13: and obtaining instantaneous active power and reactive power mathematical models according to a network side voltage equation of the mathematical model of the energy storage converter.

Step S14: and establishing an instantaneous active power and reactive power change rate mathematical model according to the instantaneous active power and reactive power mathematical model.

Step S15: and obtaining the power MPC mathematical model of the energy storage converter according to a network side voltage equation and the instantaneous active and reactive power change rate mathematical model of the energy storage converter mathematical model.

Step S16: discretizing the mathematical model of the power MPC of the energy storage converter, and adopting two-step model prediction logic to obtain the mathematical model of the power MPC of the energy storage converter at the k +2 moment.

In an embodiment of the present invention, as shown in FIG. 2, U_dcIs the voltage of the energy storage direct current side bus; u shape_abc、i_abcOutputting alternating three-phase voltage and current for the energy storage converter; e.g. of the type_abcThe voltage is three-phase voltage at the network side; r_f、L_f、C_fForming an LC filter circuit; l is_g、R_gIs an equivalent load. Considering that the network-side electromotive force is a three-phase balanced sinusoidal characteristic, the mathematical model of the energy storage converter under the two-phase stationary alpha beta coordinate system is as follows:

wherein i_αIs the output current, i, of the energy storage converter under the two-phase static alpha coordinate system_βFor two-phase stationary beta-coordinate system energy-storage converter output current, e_αIs the network side voltage, e of the energy storage converter under the two-phase static alpha coordinate system_βIs the voltage, L, of the network side of the energy storage converter under a two-phase static beta coordinate system_fFor internal inductance, U, of energy-storing converters_αIs the output voltage, U, of the energy storage converter under the two-phase static alpha coordinate system_βFor the output voltage, R, of the energy-storage converter in a two-phase stationary beta-coordinate system_fIs an internal resistor of the energy storage converter.

Ideally the net side voltage can be expressed as:

in formula (2): e is the grid side voltage amplitude; and omega is the angular frequency of the power grid. The network side voltage change rate under the two-phase static alpha beta coordinate system is as follows:

according to the instantaneous power theory, the instantaneous active and reactive power expression under a two-phase static alpha beta coordinate system can be obtained as follows:

the instantaneous active power and reactive power change expression is as follows:

by substituting formula (2) or formula (3) into formula (5), it is possible to obtain:

in order to obtain the power MPC mathematical model of the energy storage converter, discretization treatment is carried out on the formula (6), a two-step model prediction scheme is adopted, and the active MPC mathematical model and the reactive MPC mathematical model of the energy storage converter at the moment of k +2 can be obtained as follows:

wherein P (k +2) is active power at a time k +2 under the two-phase stationary alpha beta coordinate system, Q (k +2) is reactive power at a time k +2 under the two-phase stationary alpha beta coordinate system, P (k +1) is active power at a time k +1 under the two-phase stationary alpha beta coordinate system, Q (k +1) is reactive power at a time k +1 under the two-phase stationary alpha beta coordinate system, and T (k +1) is reactive power at a time k +1 under the two-phase stationary alpha beta coordinate system, and_sfor controlling the period, R_fFor internal resistance, L, of the energy-storing converter_fFor the internal inductance of the energy-storage converter, omega is the angular frequency of the power grid, U_αIs the output voltage, U, of the energy storage converter under the two-phase static alpha coordinate system_βIs the output voltage, e, of the energy storage converter in a two-phase stationary beta coordinate system_α(k +1) is the grid side voltage of the energy storage converter at the moment of k +1 under the two-phase static alpha coordinate system, e_βAnd (k +1) is the voltage of the grid side of the energy storage converter at the moment of k +1 under the two-phase static beta coordinate system.

In order to realize the active and reactive quick response of the energy storage converter and improve the transient characteristic of a control system, the active and reactive same weight value functions are adopted as follows:

J_k+2＝[P^*(k+2)-P(k+2)]²+[Q^*(k+2)-Q(k+2)]² (8)

in formula (8): p^*(k+2)、Q^*And (k +2) is an active power reference value and a reactive power reference value at the moment of k + 2.

And predicting system variables through a two-period delay compensation strategy to obtain an advanced control effect, so that delay influence is counteracted, and stable operation of the energy storage converter is ensured.

Step S2: and acquiring the deviation amount of the filter inductance of the energy storage converter, and establishing an optimal value deviation equation of the output voltage of the energy storage converter according to the deviation amount of the filter inductance.

In one embodiment, the MPC control system is sensitive to parameter errors mainly caused by the filter inductor L_fThe parameter offset, the actual value of the filter inductance, can be expressed as:

L_fg＝L_fm+ΔL_f (9)

in formula (9): l is_fmSetting a filter inductance parameter; Δ L_fIs the filter inductance deviation. Because L is_fThe deviation of the optimal value of the output voltage caused by parameter deviation is as follows:

wherein, Delta U_α(k +1) is the optimal value of the output voltage of the energy storage converter at the moment of k +1 under the two-phase static alpha coordinate system, and delta U_β(k +1) is in a two-phase stationary beta coordinate systemOptimal value of output voltage of energy storage converter at k +1 moment, delta L_fAs a filter inductance deviation, i_β(k +1) is the output current of the energy storage converter at the moment of k +1 under the two-phase static beta coordinate system, i_α(k +1) is output current of the energy storage converter at the moment of k +1 under the two-phase static alpha coordinate system, and epsilon_P(k +1) is an active power step function at the moment k +1 under a two-phase stationary alpha beta coordinate system, and epsilon_QAnd (k +1) is a reactive power step function at the moment k +1 under a two-phase stationary alpha beta coordinate system.

From the formula (10): the parameter deviation of the filter inductance can cause the deviation of the optimal control variable, so that the energy storage converter outputs wrong voltage vectors, and the control precision of the MPC is seriously influenced. Especially Delta L_fIf the value is more than 0, namely the set value of the filter inductance parameter is less than the actual value, the optimal control of the MPC is more influenced.

Step S3: and obtaining the k +2 moment energy storage converter power MPC mathematical model after the inductance error is supplemented according to the k +2 moment energy storage converter power MPC mathematical model and the energy storage converter output voltage optimal value deviation equation.

In an embodiment, according to equations (7) and (9), the mathematical model of the k +2 moment energy storage converter power MPC after the inductance error is supplemented is obtained as follows:

wherein, P_m(k +2) is active power after the inductance error at the k +2 moment is supplemented under a two-phase static alpha beta coordinate system, and Q is_m(k +2) is reactive power after the inductance error at the k +2 moment is supplemented under a two-phase static alpha beta coordinate system, and L is_fgIs the actual value of the filter inductance, L_fmFor filter inductance parameter settings, L_fg＝L_fm+ΔL_f。

Step S4: and (4) subtracting the k +2 moment energy storage converter power MPC mathematical model after the inductance error is supplemented with the k +2 moment energy storage converter power MPC mathematical model to obtain a k +2 moment inductance observed value equation.

In a specific embodiment, taking the active power as an example, subtracting the active power at the k +2 moment in the equation (7) from the active power at the k +2 moment after the inductance error compensation in the equation (11) to obtain:

in formula (12): l is_f0The inductance observations.

Step S5: and converting the k +2 moment inductance observed value equation into a k moment inductance observed value equation, and correcting the inductance parameters of the MPC control system in real time according to the k moment inductance observed value equation.

In one embodiment, the inductance observed value of the energy storage converter MPC control system at the current time, that is, at the time k, is:

as can be seen from the above analysis and the block diagram for observing and controlling the inductance of the MPC of the energy storage converter shown in fig. 3, L is the maximum value_f0And (3) the inductance parameters of the MPC control system are corrected in real time, so that prediction errors are reduced and the control precision is ensured.

The invention provides a model prediction control method of an energy storage converter, which comprises the following steps: establishing a power MPC mathematical model of the energy storage converter at the moment k +2 based on the two-step model prediction logic; acquiring the deviation amount of the filter inductance of the energy storage converter, and establishing an optimal value deviation equation of the output voltage of the energy storage converter according to the deviation amount of the filter inductance; obtaining a k +2 moment energy storage converter power MPC mathematical model after inductance error supplement according to the k +2 moment energy storage converter power MPC mathematical model and an energy storage converter output voltage optimal value deviation equation; subtracting the k +2 moment energy storage converter power MPC mathematical model after the inductance error is supplemented with the k +2 moment energy storage converter power MPC mathematical model to obtain a k +2 moment inductance observed value equation; and converting the k +2 moment inductance observed value equation into a k moment inductance observed value equation, and correcting the inductance parameters of the MPC control system in real time according to the k moment inductance observed value equation. By solving the k-time inductance observation value equation and correcting the inductance parameters of the MPC control system in real time according to the k-time inductance observation value, the prediction error is reduced and the control precision is ensured.

An embodiment of the present invention further provides a model predictive control system for an energy storage converter, as shown in fig. 4, including:

and the first model establishing module 1 is used for establishing a power MPC mathematical model of the energy storage converter at the moment of k +2 based on the two-step model prediction logic. For details, refer to the related description of step S1 in the above embodiment, and are not described herein again.

And the second model establishing module 2 is used for acquiring the filter inductance deviation of the energy storage converter and establishing an optimal value deviation equation of the output voltage of the energy storage converter according to the filter inductance deviation. For details, refer to the related description of step S2 in the above embodiment, and are not described herein again.

And the third model establishing module 3 is used for obtaining the k +2 moment energy storage converter power MPC mathematical model after inductance error supplement according to the k +2 moment energy storage converter power MPC mathematical model and the energy storage converter output voltage optimal value deviation equation. For details, refer to the related description of step S3 in the above embodiment, and are not described herein again.

And the fourth model establishing module 4 is used for subtracting the k +2 moment energy storage converter power MPC mathematical model after the inductance error is supplemented with the k +2 moment energy storage converter power MPC mathematical model to obtain a k +2 moment inductance observed value equation. For details, refer to the related description of step S4 in the above embodiment, and are not described herein again.

And the control module 5 is used for converting the k +2 moment inductance observed value equation into a k moment inductance observed value equation and correcting the inductance parameters of the MPC control system in real time according to the k moment inductance observed value equation. . For details, refer to the related description of step S5 in the above embodiment, and are not described herein again.

An embodiment of the present invention further provides a computer device, as shown in fig. 5, the device may include a processor 61 and a memory 62, where the processor 61 and the memory 62 may be connected by a bus or in another manner, and fig. 5 takes the connection by the bus as an example.

The processor 61 may be a Central Processing Unit (CPU). The Processor 61 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof.

The memory 62, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as the corresponding program instructions/modules in embodiments of the present invention. The processor 61 executes various functional applications and data processing of the processor by running non-transitory software programs, instructions and modules stored in the memory 62, that is, the method for predictive control of the energy storage converter model in the above method embodiment is implemented.

The memory 62 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 61, and the like. Further, the memory 62 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 62 may optionally include memory located remotely from the processor 61, and these remote memories may be connected to the processor 61 via a network. Examples of such networks include, but are not limited to, the internet, intranets, mobile communication networks, and combinations thereof.

One or more modules are stored in memory 62 and, when executed by processor 61, perform the energy storage converter model predictive control method provided by the practice of the invention.

The details of the computer device can be understood by referring to the corresponding descriptions and effects in the embodiments shown in fig. 1 to fig. 3, and are not described herein again.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program that can be stored in a computer-readable storage medium and that when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.

It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of the invention may be made without departing from the spirit or scope of the invention.