A model predictive control method and system for energy storage commutation device

Abstract

The present invention provides a method and system for predictive control of an energy storage converter model, which includes: establishing a power MPC mathematical model of the energy storage converter at time k+2; establishing an optimal value deviation equation of the output voltage of the energy storage converter according to the filter inductance deviation; obtaining the power MPC mathematical model of the energy storage converter at time k+2 after inductance error supplementation according to the power MPC mathematical model of the energy storage converter at time k+2 and the optimal value deviation equation of the output voltage of the energy storage converter; subtracting the power MPC mathematical model of the energy storage converter at time k+2 after inductance error supplementation from the power MPC mathematical model of the energy storage converter at time k+2 to obtain the inductance observation value equation at time k+2; converting the inductance observation value equation at time k+2 into the inductance observation value equation at time k, and performing real-time correction on the inductance parameter of the MPC control system according to the inductance observation value equation at time k. By implementing the present invention, the prediction error is reduced and the control accuracy is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of model prediction technology, and in particular to a model prediction control method and system for an energy storage commutation device. Background Art

[0002] As one of the key technologies for energy transformation, energy storage technology has received extensive attention in the industry in recent years because it can provide a variety of auxiliary services for the power grid, such as peak load regulation, frequency regulation, and emergency response. In order to achieve friendly grid connection of energy storage systems and provide stable voltage and frequency support for the power grid, it is necessary to carry out research on energy storage converter control strategies.

[0003] At present, in the field of energy storage converter control, double closed-loop control and zero-beat control are mostly used to achieve dynamic response of energy storage voltage and frequency. However, conventional control strategies cannot maintain the stability of energy storage converter control systems under high penetration of distributed power sources. For example, when the energy storage converter faces frequent voltage and frequency adjustment conditions, the switching frequency is high. At this time, conventional control delays will be caused by sampling, calculation, zero-order hold and pulse width modulation (PWM). If the control system cannot suppress this delay in time, it will greatly reduce the system bandwidth and cause the entire control system to become unstable. MPC (Model Predictive Control) is a state variable prediction algorithm that reduces the impact of delays through control algorithms. Since there is no need to consider the number of switching states, it does not affect the control bandwidth of the system, thereby reducing the computing requirements and has been widely used in energy storage converter control systems.

[0004] However, the MPC of the energy storage commutation device is easily affected by the filter inductance parameters. When the filter inductance parameters deviate, the prediction error of the MPC of the energy storage commutation device is large, which affects the stable operation of the energy storage commutation device. Summary of the invention

[0005] Therefore, the technical problem to be solved by the present invention is to overcome the defect in the prior art that the MPC energy storage device is easily affected by the filter inductance parameters, thereby affecting the stable operation of the energy storage device, thereby providing a model predictive control method and system for an energy storage device.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a model predictive control method for an energy storage converter, comprising:

[0008] Based on the two-step model prediction logic, the MPC mathematical model of the energy storage converter power at time k+2 is established;

[0009] Obtaining a filter inductance deviation of an energy storage converter, and establishing an optimal value deviation equation of an output voltage of the energy storage converter according to the filter inductance deviation;

[0010] According to the energy storage converter power MPC mathematical model at time k+2 and the energy storage converter output voltage optimal value deviation equation, the energy storage converter power MPC mathematical model at time k+2 after inductance error supplementation is obtained;

[0011] Subtract the MPC mathematical model of the energy storage converter power at time k+2 after the inductance error is supplemented from the MPC mathematical model of the energy storage converter power at time k+2 to obtain the inductance observation value equation at time k+2;

[0012] The inductance observation value equation at time k+2 is converted into the inductance observation value equation at time k, and the inductance parameters of the MPC control system are corrected in real time according to the inductance observation value equation at time k.

[0013] Optionally, the two-step model prediction logic is used to establish a power MPC mathematical model of the energy storage converter at time k+2, including:

[0014] Establish the mathematical model of energy storage converter;

[0015] Obtaining a grid-side voltage equation of the energy storage converter mathematical model according to the energy storage converter mathematical model;

[0016] According to the grid-side voltage equation of the energy storage converter mathematical model, the instantaneous active and reactive power mathematical models are obtained;

[0017] Establish instantaneous active and reactive power change rate mathematical model based on instantaneous active and reactive power mathematical model;

[0018] According to the grid-side voltage equation of the energy storage converter mathematical model and the instantaneous active and reactive power change rate mathematical model, the energy storage converter power MPC mathematical model is obtained;

[0019] The energy storage converter power MPC mathematical model is discretized, and the two-step model prediction logic is adopted to obtain the energy storage converter power MPC mathematical model at time k+2.

[0020] Optionally, the MPC mathematical model of the energy storage converter power at time k+2 is:

[0021]

[0022] Among them, P(k+2) is the active power at time k+2 in the two-phase stationary ɑβ coordinate system, Q(k+2) is the reactive power at time k+2 in the two-phase stationary ɑβ coordinate system, P(k+1) is the active power at time k+1 in the two-phase stationary ɑβ coordinate system, Q(k+1) is the reactive power at time k+1 in the two-phase stationary ɑβ coordinate system, T sis the control period, R f is the internal resistance of the energy storage converter, L f is the internal inductance of the energy storage converter, ω is the grid angular frequency, U α is the output voltage of the energy storage converter in the two-phase stationary ɑ coordinate system, U β is the output voltage of the energy storage converter in the two-phase stationary β coordinate system, e α (k+1) is the grid-side voltage of the energy storage converter at time k+1 in the two-phase stationary ɑ coordinate system, e β (k+1) is the grid-side voltage of the energy storage converter at time k+1 in the two-phase stationary β coordinate system.

[0023] Optionally, the optimal value deviation equation of the energy storage converter output voltage is:

[0024]

[0025] Among them, ΔU α (k+1) is the optimal output voltage of the energy storage converter at time k+1 in the two-phase stationary ɑ coordinate system, ΔU β (k+1) is the optimal value of the output voltage of the energy storage converter at time k+1 in the two-phase stationary β coordinate system, ΔL f is the filter inductance deviation, i β (k+1) is the output current of the energy storage converter at time k+1 in the two-phase stationary β coordinate system, i α (k+1) is the output current of the energy storage converter at time k+1 in the two-phase stationary ɑ coordinate system, ε P (k+1) is the active power step function at time k+1 in the two-phase stationary ɑβ coordinate system, ε Q (k+1) is the reactive power step function at time k+1 in the two-phase stationary ɑβ coordinate system.

[0026] Optionally, the MPC mathematical model of the energy storage converter power at time k+2 after the inductance error is supplemented is:

[0027]

[0028] Among them, P m (k+2) is the active power after the inductance error is compensated at time k+2 in the two-phase stationary ɑβ coordinate system, Q m (k+2) is the reactive power after the inductance error is supplemented at time k+2 in the two-phase stationary ɑβ coordinate system, L fg is the actual value of the filter inductance, L fm is the filter inductance parameter setting value, L fg =L fm +ΔL f .

[0029] Optionally, the inductance observation value equation at time k is:

[0030]

[0031] Among them, L f0 is the observed value of inductance.

[0032] Optionally, the energy storage converter mathematical model is:

[0033]

[0034] Among them, i α is the output current of the energy storage converter in the two-phase stationary ɑ coordinate system, i β is the output current of the two-phase stationary β coordinate system energy storage converter, e α is the grid-side voltage of the energy storage converter in the two-phase stationary ɑ coordinate system, e β is the grid-side voltage of the energy storage converter in the two-phase stationary β coordinate system.

[0035] In a second aspect, an embodiment of the present invention provides a model predictive control system for an energy storage converter, comprising:

[0036] The first model building module is used to build a power MPC mathematical model of the energy storage converter at time k+2 based on a two-step model prediction logic;

[0037] The second model building module is used to obtain the filter inductance deviation of the energy storage converter, and establish the optimal value deviation equation of the output voltage of the energy storage converter according to the filter inductance deviation;

[0038] The third model building module is used to obtain the energy storage converter power MPC mathematical model at time k+2 after the inductance error is supplemented according to the energy storage converter power MPC mathematical model at time k+2 and the energy storage converter output voltage optimal value deviation equation;

[0039] The fourth model building module is used to make a difference between the energy storage converter power MPC mathematical model at time k+2 after the inductance error is supplemented and the energy storage converter power MPC mathematical model at time k+2, so as to obtain the inductance observation value equation at time k+2;

[0040] The control module is used to convert the inductance observation value equation at time k+2 into the inductance observation value equation at time k, and to perform real-time correction on the inductance parameters of the MPC control system according to the inductance observation value equation at time k.

[0041] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the energy storage converter model predictive control method described in the first aspect of the embodiment of the present invention.

[0042] In a fourth aspect, an embodiment of the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the energy storage converter model predictive control method described in the first aspect of the embodiment of the present invention by executing the computer instructions.

[0043] The technical solution of the present invention has the following advantages:

[0044] The energy storage converter model prediction control method provided by the present invention includes: establishing a power MPC mathematical model of the energy storage converter at time k+2 based on a two-step model prediction logic; obtaining a 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; obtaining a power MPC mathematical model of the energy storage converter at time k+2 supplemented with an inductance error according to the power MPC mathematical model of the energy storage converter at time k+2 and the optimal value deviation equation of the output voltage of the energy storage converter; subtracting the power MPC mathematical model of the energy storage converter at time k+2 supplemented with an inductance error from the power MPC mathematical model of the energy storage converter at time k+2 to obtain an inductance observation value equation at time k+2; converting the inductance observation value equation at time k+2 into an inductance observation value equation at time k, and performing real-time correction on the inductance parameter of the MPC control system according to the inductance observation value equation at time k. By solving the inductance observation value equation at time k, the inductance parameter of the MPC control system is corrected in real time according to the inductance observation value at time k, thereby reducing the prediction error and ensuring the control accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 A flowchart of a specific example of the energy storage converter model predictive control method in an embodiment of the present invention;

[0047] Figure 2 This is a circuit topology diagram of an energy storage converter according to an embodiment of the present invention;

[0048] Figure 3 This is a block diagram of the MPC inductance observation control of the energy storage converter according to an embodiment of the present invention;

[0049] Figure 4 It is a principle block diagram of a specific example of the energy storage converter model predictive control system in an embodiment of the present invention;

[0050] Figure 5 A composition diagram of a specific example of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0053] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, it can also be the internal connection of two components, it can be a wireless connection, or it can be a wired connection. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0054] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0055] The embodiment of the present invention provides a model predictive control method for an energy storage converter, such as Figure 1 As shown, the following steps are included:

[0056] Step S1: Based on the two-step model prediction logic, establish the energy storage converter power MPC mathematical model at time k+2.

[0057] In a specific embodiment, the power MPC mathematical model of the energy storage converter at time k+2 is established specifically through the following steps.

[0058] Step S11: Establishing a mathematical model of the energy storage converter.

[0059] Step S12: obtaining a grid-side voltage equation of the energy storage converter mathematical model according to the energy storage converter mathematical model.

[0060] Step S13: Obtaining instantaneous active and reactive power mathematical models according to the grid-side voltage equation of the energy storage converter mathematical model.

[0061] Step S14: Establishing a mathematical model of instantaneous active and reactive power change rate according to the mathematical model of instantaneous active and reactive power.

[0062] Step S15: Obtain the energy storage converter power MPC mathematical model according to the grid-side voltage equation of the energy storage converter mathematical model and the instantaneous active and reactive power change rate mathematical model.

[0063] Step S16: Discretize the energy storage converter power MPC mathematical model, adopt a two-step model prediction logic, and obtain the energy storage converter power MPC mathematical model at time k+2.

[0064] In the embodiment of the present invention, Figure 2 As shown, U dc is the DC bus voltage of the energy storage; U abc 、i abc Output AC three-phase voltage and current for energy storage converter; abc is the three-phase voltage on the grid side; R f , L f , C f Constitute an LC filter circuit; L g , R g Considering that the grid-side electromotive force is a three-phase balanced sinusoidal characteristic, the mathematical model of the energy storage converter in the two-phase stationary ɑβ coordinate system is:

[0065]

[0066] Among them, i α is the output current of the energy storage converter in the two-phase stationary ɑ coordinate system, i β is the output current of the two-phase stationary β coordinate system energy storage converter, e α is the grid-side voltage of the energy storage converter in the two-phase stationary ɑ coordinate system, e β is the grid-side voltage of the energy storage converter in the two-phase stationary β coordinate system, L f is the internal inductance of the energy storage converter, U α is the output voltage of the energy storage converter in the two-phase stationary ɑ coordinate system, U β is the output voltage of the energy storage converter in the two-phase stationary β coordinate system, R f is the internal resistance of the energy storage converter.

[0067] Ideally, the grid-side voltage can be expressed as:

[0068]

[0069] In formula (2), E is the grid voltage amplitude; ω is the grid angular frequency. The grid voltage change rate in the two-phase stationary ɑβ coordinate system is:

[0070]

[0071] According to the instantaneous power theory, the instantaneous active and reactive power expressions in the two-phase stationary ɑβ coordinate system can be obtained as follows:

[0072]

[0073] The instantaneous active and reactive power change rate expression is:

[0074]

[0075] Substituting equation (2) and equation (3) into equation (5), we can obtain:

[0076]

[0077] In order to obtain the power MPC mathematical model of the energy storage converter, equation (6) is discretized and a two-step model prediction scheme is adopted. The active and reactive MPC mathematical models of the energy storage converter at time k+2 can be obtained as follows:

[0078]

[0079] Among them, P(k+2) is the active power at time k+2 in the two-phase stationary ɑβ coordinate system, Q(k+2) is the reactive power at time k+2 in the two-phase stationary ɑβ coordinate system, P(k+1) is the active power at time k+1 in the two-phase stationary ɑβ coordinate system, Q(k+1) is the reactive power at time k+1 in the two-phase stationary ɑβ coordinate system, T s is the control period, R f is the internal resistance of the energy storage converter, L f is the internal inductance of the energy storage converter, ω is the grid angular frequency, U α is the output voltage of the energy storage converter in the two-phase stationary ɑ coordinate system, U β is the output voltage of the energy storage converter in the two-phase stationary β coordinate system, e α (k+1) is the grid-side voltage of the energy storage converter at time k+1 in the two-phase stationary ɑ coordinate system, e β (k+1) is the grid-side voltage of the energy storage converter at time k+1 in the two-phase stationary β coordinate system.

[0080] In order to achieve fast active and reactive response of energy storage converter and improve transient characteristics of control system, the value function with equal weights of active and reactive power is adopted as follows:

[0081] Jk+2 =[P * (k+2)-P(k+2)] 2 +[Q * (k+2)-Q(k+2)] 2 (8)

[0082] In formula (8): P * (k+2), Q * (k+2) is the reference value of active and reactive power at time k+2.

[0083] The system variables are predicted through a two-cycle delay compensation strategy to obtain an advance control effect, thereby offsetting the delay effect and ensuring the stable operation of the energy storage converter.

[0084] Step S2: Obtain the energy storage converter filter inductance deviation, and establish the energy storage converter output voltage optimal value deviation equation according to the filter inductance deviation.

[0085] In a specific embodiment, the energy storage converter MPC control system is more sensitive to parameter errors, and the parameter errors mainly come from the filter inductance L f Parameter offset, the actual value of the filter inductance can be expressed as:

[0086] L fg =L fm +ΔL f (9)

[0087] In formula (9): L fm ΔL is the filter inductance parameter setting value; f is the filter inductance deviation. f The deviation of the optimal value of the output voltage caused by parameter offset is:

[0088]

[0089] Among them, ΔU α (k+1) is the optimal output voltage of the energy storage converter at time k+1 in the two-phase stationary ɑ coordinate system, ΔU β (k+1) is the optimal value of the output voltage of the energy storage converter at time k+1 in the two-phase stationary β coordinate system, ΔL f is the filter inductance deviation, i β (k+1) is the output current of the energy storage converter at time k+1 in the two-phase stationary β coordinate system, i α (k+1) is the output current of the energy storage converter at time k+1 in the two-phase stationary ɑ coordinate system, ε P (k+1) is the active power step function at time k+1 in the two-phase stationary ɑβ coordinate system, ε Q (k+1) is the reactive power step function at time k+1 in the two-phase stationary ɑβ coordinate system.

[0090] From formula (10), we can see that the deviation of the filter inductance parameters will cause the deviation of the optimal control variable, resulting in the wrong voltage vector output by the energy storage converter, which seriously affects the MPC control accuracy. f >0, that is, when the filter inductance parameter setting value is smaller than the actual value, it will have a greater impact on the MPC optimal control.

[0091] Step S3: According to the energy storage converter power MPC mathematical model at time k+2 and the energy storage converter output voltage optimal value deviation equation, the energy storage converter power MPC mathematical model at time k+2 after inductance error supplementation is obtained.

[0092] In a specific embodiment, according to equations (7) and (9), the MPC mathematical model of the energy storage converter power at time k+2 after the inductance error is supplemented can be obtained as follows:

[0093]

[0094] Among them, P m (k+2) is the active power after the inductance error is compensated at time k+2 in the two-phase stationary ɑβ coordinate system, Q m (k+2) is the reactive power after the inductance error is supplemented at time k+2 in the two-phase stationary ɑβ coordinate system, L fg is the actual value of the filter inductance, L fm is the filter inductance parameter setting value, L fg =L fm +ΔL f .

[0095] Step S4: Subtract the MPC mathematical model of the energy storage converter power at time k+2 after the inductance error is supplemented from the MPC mathematical model of the energy storage converter power at time k+2 to obtain the inductance observation value equation at time k+2.

[0096] In a specific embodiment, taking active power as an example, the active power at time k+2 after the inductance error is supplemented in formula (11) is subtracted from the active power at time k+2 in formula (7), and the result is:

[0097]

[0098] In formula (12): L f0 is the observed value of inductance.

[0099] Step S5: converting the inductance observation value equation at time k+2 into the inductance observation value equation at time k, and performing real-time correction on the inductance parameters of the MPC control system according to the inductance observation value equation at time k.

[0100] In a specific embodiment, the inductance observation value of the energy storage converter MPC control system at the current time, i.e., time k, is:

[0101]

[0102] According to the above analysis and Figure 3 As shown in the energy storage converter MPC inductance observation control block diagram, it can be seen that L f0 The inductance parameters of the MPC control system are corrected in real time to reduce prediction errors and ensure control accuracy.

[0103] The energy storage converter model prediction control method provided by the present invention includes: establishing a power MPC mathematical model of the energy storage converter at time k+2 based on a two-step model prediction logic; obtaining a 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; obtaining a power MPC mathematical model of the energy storage converter at time k+2 supplemented with an inductance error according to the power MPC mathematical model of the energy storage converter at time k+2 and the optimal value deviation equation of the output voltage of the energy storage converter; subtracting the power MPC mathematical model of the energy storage converter at time k+2 supplemented with an inductance error from the power MPC mathematical model of the energy storage converter at time k+2 to obtain an inductance observation value equation at time k+2; converting the inductance observation value equation at time k+2 into an inductance observation value equation at time k, and performing real-time correction on the inductance parameter of the MPC control system according to the inductance observation value equation at time k. By solving the inductance observation value equation at time k, the inductance parameter of the MPC control system is corrected in real time according to the inductance observation value at time k, thereby reducing the prediction error and ensuring the control accuracy.

[0104] The embodiment of the present invention also provides a model predictive control system for an energy storage converter, such as Figure 4 As shown, including:

[0105] The first model building module 1 is used to build the MPC mathematical model of the energy storage converter power at time k+2 based on the two-step model prediction logic. For details, please refer to the relevant description of step S1 in the above embodiment, which will not be repeated here.

[0106] The second model building module 2 is used to obtain the energy storage converter filter inductance deviation, and establish the energy storage converter output voltage optimal value deviation equation according to the filter inductance deviation. For details, please refer to the relevant description of step S2 in the above embodiment, which will not be repeated here.

[0107] The third model building module 3 is used to obtain the energy storage converter power MPC mathematical model at time k+2 after the inductance error is supplemented according to the energy storage converter power MPC mathematical model at time k+2 and the energy storage converter output voltage optimal value deviation equation. For details, please refer to the relevant description of step S3 in the above embodiment, which will not be repeated here.

[0108] The fourth model building module 4 is used to subtract the energy storage converter power MPC mathematical model at time k+2 after the inductance error is supplemented from the energy storage converter power MPC mathematical model at time k+2 to obtain the inductance observation value equation at time k+2. For details, please refer to the relevant description of step S4 in the above embodiment, which will not be repeated here.

[0109] The control module 5 is used to convert the inductance observation value equation at time k+2 into the inductance observation value equation at time k, and to perform real-time correction on the inductance parameter of the MPC control system according to the inductance observation value equation at time k. For details, please refer to the relevant description of step S5 in the above embodiment, which will not be repeated here.

[0110] The embodiment of the present invention also provides a computer device, such as Figure 5 As shown, the device may include a processor 61 and a memory 62, wherein the processor 61 and the memory 62 may be connected via a bus or other means. Figure 5 Take the example of connecting via a bus.

[0111] The processor 61 may be a central processing unit (CPU). The processor 61 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0112] The memory 62 is a non-transient computer-readable storage medium that can be used to store non-transient software programs, non-transient computer executable programs and modules, such as the corresponding program instructions / modules in the embodiment of the present invention. The processor 61 executes various functional applications and data processing of the processor by running the non-transient software programs, instructions and modules stored in the memory 62, that is, the energy storage converter model predictive control method in the above method embodiment is implemented.

[0113] The memory 62 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function; the data storage area may store data created by the processor 61, etc. In addition, the memory 62 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 62 may optionally include a memory remotely arranged relative to the processor 61, and these remote memories may be connected to the processor 61 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, an intranet, a mobile communication network, and combinations thereof.

[0114] One or more modules are stored in the memory 62, and when executed by the processor 61, the energy storage converter model predictive control method provided by the embodiment of the present invention is executed.

[0115] For details of the above computer equipment, please refer to Figure 1-Figure 3 The corresponding related descriptions and effects in the illustrated embodiments can be understood and will not be repeated here.

[0116] Those skilled in the art can understand that the implementation of all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.

[0117] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from these are still within the scope of protection of the invention.

Claims

1. A model predictive control method for an energy storage converter, characterized in that: include: Based on the two-step model prediction logic, the MPC mathematical model of the energy storage converter power at time k+2 is established; Obtaining a filter inductance deviation of an energy storage converter, and establishing an optimal value deviation equation of an output voltage of the energy storage converter according to the filter inductance deviation; According to the energy storage converter power MPC mathematical model at time k+2 and the energy storage converter output voltage optimal value deviation equation, the energy storage converter power MPC mathematical model at time k+2 after inductance error supplementation is obtained; Subtract the MPC mathematical model of the energy storage converter power at time k+2 after the inductance error is supplemented from the MPC mathematical model of the energy storage converter power at time k+2 to obtain the inductance observation value equation at time k+2; The inductance observation value equation at time k+2 is converted into the inductance observation value equation at time k, and the inductance parameters of the MPC control system are corrected in real time according to the inductance observation value equation at time k.

2. The energy storage converter model predictive control method according to claim 1, characterized in that: The two-step model prediction logic is used to establish the energy storage converter power MPC mathematical model at time k+2, including: Establish the mathematical model of energy storage converter; Obtaining a grid-side voltage equation of the energy storage converter mathematical model according to the energy storage converter mathematical model; According to the grid-side voltage equation of the energy storage converter mathematical model, the instantaneous active and reactive power mathematical models are obtained; Establish instantaneous active and reactive power change rate mathematical model based on instantaneous active and reactive power mathematical model; According to the grid-side voltage equation of the energy storage converter mathematical model and the instantaneous active and reactive power change rate mathematical model, the energy storage converter power MPC mathematical model is obtained; The energy storage converter power MPC mathematical model is discretized, and the two-step model prediction logic is adopted to obtain the energy storage converter power MPC mathematical model at time k+2.

3. The energy storage converter model predictive control method according to claim 1, characterized in that: The MPC mathematical model of the energy storage converter power at time k+2 is: Among them, P(k+2) is the active power at time k+2 in the two-phase stationary ɑβ coordinate system, Q(k+2) is the reactive power at time k+2 in the two-phase stationary ɑβ coordinate system, P(k+1) is the active power at time k+1 in the two-phase stationary ɑβ coordinate system, Q(k+1) is the reactive power at time k+1 in the two-phase stationary ɑβ coordinate system, T s is the control period, R f is the internal resistance of the energy storage converter, L f is the internal inductance of the energy storage converter, ω is the grid angular frequency, U α is the output voltage of the energy storage converter in the two-phase stationary ɑ coordinate system, U β is the output voltage of the energy storage converter in the two-phase stationary β coordinate system, e α (k+1) is the grid-side voltage of the energy storage converter at time k+1 in the two-phase stationary ɑ coordinate system, e β (k+1) is the grid-side voltage of the energy storage converter at time k+1 in the two-phase stationary β coordinate system.

4. The energy storage converter model predictive control method according to claim 1, characterized in that: The optimal value deviation equation of the energy storage converter output voltage is: Among them, ΔU α (k+1) is the optimal output voltage of the energy storage converter at time k+1 in the two-phase stationary ɑ coordinate system, ΔU β (k+1) is the optimal value of the output voltage of the energy storage converter at time k+1 in the two-phase stationary β coordinate system, ΔL f is the filter inductance deviation, i β (k+1) is the output current of the energy storage converter at time k+1 in the two-phase stationary β coordinate system, i α (k+1) is the output current of the energy storage converter at time k+1 in the two-phase stationary ɑ coordinate system, ε P (k+1) is the active power step function at time k+1 in the two-phase stationary ɑβ coordinate system, ε Q (k+1) is the reactive power step function at time k+1 in the two-phase stationary ɑβ coordinate system.

5. The energy storage converter model predictive control method according to claim 1, characterized in that: The MPC mathematical model of the energy storage converter power at time k+2 after the inductance error is compensated is: Among them, P m (k+2) is the active power after the inductance error is compensated at time k+2 in the two-phase stationary ɑβ coordinate system, Q m (k+2) is the reactive power after the inductance error is supplemented at time k+2 in the two-phase stationary ɑβ coordinate system, L fg is the actual value of the filter inductance, L fm is the filter inductance parameter setting value, L fg =L fm +ΔL f .

6. The energy storage converter model predictive control method according to claim 1, characterized in that: The equation for the inductance observation value at time k is: Among them, L f0 is the observed value of inductance.

7. The energy storage converter model predictive control method according to claim 2, characterized in that: The mathematical model of the energy storage converter is: Among them, i α is the output current of the energy storage converter in the two-phase stationary ɑ coordinate system, i β is the output current of the two-phase stationary β coordinate system energy storage converter, e α is the grid-side voltage of the energy storage converter in the two-phase stationary ɑ coordinate system, e β is the grid-side voltage of the energy storage converter in the two-phase stationary β coordinate system.

8. A model predictive control system for an energy storage converter, characterized in that: include: The first model building module is used to build a power MPC mathematical model of the energy storage converter at time k+2 based on a two-step model prediction logic; The second model building module is used to obtain the filter inductance deviation of the energy storage converter, and establish the optimal value deviation equation of the output voltage of the energy storage converter according to the filter inductance deviation; The third model building module is used to obtain the energy storage converter power MPC mathematical model at time k+2 after the inductance error is supplemented according to the energy storage converter power MPC mathematical model at time k+2 and the energy storage converter output voltage optimal value deviation equation; The fourth model building module is used to make a difference between the energy storage converter power MPC mathematical model at time k+2 after the inductance error is supplemented and the energy storage converter power MPC mathematical model at time k+2, so as to obtain the inductance observation value equation at time k+2; The control module is used to convert the inductance observation value equation at time k+2 into the inductance observation value equation at time k, and to perform real-time correction on the inductance parameters of the MPC control system according to the inductance observation value equation at time k.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the energy storage converter model predictive control method as described in any one of claims 1-7.

10. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the energy storage inverter model predictive control method as described in any one of claims 1-7 by executing the computer instructions.

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