Converter grid-connected current finite time convergence method, system, device and medium

Abstract

The application discloses a grid-connected current finite time convergence method, system, device and medium for a converter, comprising the following steps: constructing a finite time current controller of a grid-connected converter in a two-phase dq rotating coordinate system; introducing an immune optimization algorithm into the finite time current controller, and calculating an adaptive factor according to the relationship between the immune optimization algorithm and an immune response system; combining the finite time current controller and the adaptive factor to obtain an immune optimization finite time current controller, parameters of the immune optimization finite time current controller can be adaptively optimized and updated; and using the immune optimization finite time current controller to perform finite time convergence on the grid-connected converter. The application enables the grid-connected current to converge in a limited time, improves the dynamic response performance of the grid-connected current, and when model mismatch or disturbance occurs in the system, the immune optimization algorithm can adaptively optimize and update the parameters of the finite time controller, guaranteeing the good control performance of the current loop and improving the anti-interference capability.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power electronics, and in particular to a grid-connected current finite-time convergence method, system, device and medium for a converter. BACKGROUND

[0002] With the successive proposal of the double carbon target, the development of new energy has reached an unprecedented height. As an intermediate medium between the power grid and new energy, the energy storage system plays an indispensable role, and the selection of the current loop control method of the converter (PCS) as the core device in the energy storage system is particularly important.

[0003] At present, the commonly used current loop control methods for converters include PI control, repetitive control, predictive control, and active disturbance rejection control. Although these control methods improve the grid-connected current performance of the converter from different aspects, they all belong to infinite-time convergence, i.e., the convergence time of the current cannot be calculated, making it impossible to achieve fast finite-time convergence, thereby leading to poor dynamic response performance when the grid side experiences fluctuations and other working conditions.

[0004] Later, in order to improve the dynamic response performance of the grid-connected current, someone proposed using a finite-time controller with better convergence performance to make the grid-connected current converge within a finite time. However, when the model parameter mismatch occurs due to changes in the grid side parameters, the anti-interference ability of the converter will decrease, leading to poor control performance of the device, and in severe cases, even instability of the device.

[0005] Therefore, it is necessary to improve the prior art.

[0006] The above information is given as background information only to assist with an understanding of the present disclosure, and does not constitute admission or recognition that any of the above information constitutes prior art with respect to the present disclosure. SUMMARY

[0007] The present application provides a grid-connected current finite-time convergence method, system, device and medium for a converter to solve the deficiencies of the prior art.

[0008] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0009] In a first aspect, the present application provides a grid-connected current finite-time convergence method for a converter, which comprises:

[0010] S1, constructing the following finite-time current controller for the grid-connected converter in the two-phase dq rotating coordinate system:

[0011]

[0012] wherein, ed and e q are the components of the grid voltage in dq-axes, v d and v q are the components of the AC inverter-side voltage in dq-axes, i d and i q are the components of the inductor-side current in dq-axes, L is the grid-side filter inductance, ω is the grid angular frequency, k s is the gain coefficient, and β is the fractional exponent;

[0013] S2, introducing the immune optimization algorithm into the finite-time current controller, and calculating the following adaptive factor according to the relationship between the immune optimization algorithm and the immune response system:

[0014] γ p = Q{1- τg[Δu(k)]};

[0015] wherein Q = γ1 is the gain, and τ = γ2 / γ1;

[0016] S3, combining the finite-time current controller and the adaptive factor, obtaining the following immune optimization finite-time current controller, parameters of the immune optimization finite-time current controller can be adaptively optimized and updated:

[0017]

[0018] S4, using the immune optimization finite-time current controller to perform finite-time convergence on the grid-connected current.

[0019] Further, in the grid-connected current finite-time convergence method, the step S1 comprises:

[0020] establishing the following mathematical model of the grid-connected current in the two-phase dq rotating coordinate system:

[0021]

[0022] wherein e d and e q are the components of the grid voltage in dq-axes, v d and v q are the components of the AC inverter-side voltage in dq-axes, i d and i q are the components of the inductor-side current in dq-axes, L is the grid-side filter inductance, R is the equivalent resistance of the line, ω is the grid angular frequency, and p is the differential operator;

[0023] obtaining the following finite-time current controller according to the mathematical model and the following first-order finite-time controller:

[0024] u = -k sign(x) |x| α k > 0, 0 < a < 1

[0025]

[0026] where e d and e q are the components of grid voltage in dq axis, v d and v q are the components of AC inverter side voltage in dq axis, i d and i q are the components of inductor side current in dq axis, L is the grid side filter inductance, ω is the grid angular frequency, k s is the gain coefficient, and β is the fractional exponential power.

[0027] Further, in the grid-connected current finite-time convergence method of the converter, before the step S4, the method further comprises:

[0028] periodically performing adaptive optimization and updating on the parameters of the immune optimization finite-time current controller.

[0029] Further, in the grid-connected current finite-time convergence method of the converter, before the step S2, the method further comprises:

[0030] pre-establishing the relationship between the immune optimization algorithm and the immune response system.

[0031] In a second aspect, an embodiment of the present application provides a grid-connected current finite-time convergence system of a converter, the system comprising:

[0032] a controller construction module configured to construct the following finite-time current controller of the grid-connected converter in a two-phase dq rotating coordinate system:

[0033]

[0034] where e d and e q are the components of grid voltage in dq axis, v d and v q are the components of AC inverter side voltage in dq axis, i d and i q are the components of inductor side current in dq axis, L is the grid side filter inductance, ω is the grid angular frequency, k s is the gain coefficient, and β is the fractional exponential power.

[0035] a factor calculation module configured to introduce an immune optimization algorithm into the finite-time current controller, and calculate the following adaptive factors according to the relationship between the immune optimization algorithm and the immune response system.

[0036] γ p = Q (1 - τg [Δu (k) ] ) ;

[0037] wherein Q = γ1 is a gain, τ = γ2 / γ1;

[0038] The controller obtains a module for combining the finite time current controller and the adaptive factor to obtain an immune optimization finite time current controller, parameters of the immune optimization finite time current controller being self-adaptive and optimized and updated:

[0039]

[0040] A finite time convergence module is used to perform finite time convergence on the grid-connected converter by using the immune optimization finite time current controller.

[0041] Further, in the converter grid-connected current finite time convergence system, the controller construction module is specifically used for:

[0042] A mathematical model of the grid-connected converter in a two-phase dq rotating coordinate system is established as follows:

[0043]

[0044] wherein e d and e q are components of grid voltage in dq axes, v d and v q are components of AC inverter side voltage in dq axes, i d and i q are components of inductance side current in dq axes, L is a grid side filter inductance, R is a line equivalent resistance, ω is a grid angular frequency, and p is a differential operator;

[0045] According to the mathematical model and a first order finite time controller, a finite time current controller is obtained as follows:

[0046] u = - ksign (x) |x| α , k > 0, 0 < α < 1;

[0047]

[0048] wherein e d and e q are components of grid voltage in dq axes, v d and v q are components of AC inverter side voltage in dq axes, i d and i qis the component of the inductive side current in the dq axis, L is the grid side filter inductance, and ω is the grid angular frequency, k s is a gain coefficient, and β is a fractional exponential power.

[0049] Further, in the grid-connected current finite-time convergence system of the converter, the system further comprises:

[0050] A periodic updating module is configured to periodically perform adaptive optimization and updating on parameters of the immune optimization finite-time current controller.

[0051] Further, in the grid-connected current finite-time convergence system of the converter, the system further comprises:

[0052] A relationship establishing module is configured to pre-establish a relationship between the immune optimization algorithm and the immune response system.

[0053] In a third aspect, an embodiment of the present application provides a computer device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the grid-connected current finite-time convergence method of the converter when executing the computer program.

[0054] In a fourth aspect, an embodiment of the present application provides a storage medium comprising computer executable instructions, which are executed by a computer processor to implement the grid-connected current finite-time convergence method of the converter.

[0055] Compared with the prior art, the embodiment of the present application has the following beneficial effects:

[0056] The grid-connected current finite-time convergence method, system, device and medium of the converter provided by the embodiment of the present application use the finite-time controller with better convergence performance, and use the immune optimization algorithm to improve the performance of the finite-time controller, so that not only the grid-connected current can converge in a limited time, thereby improving the dynamic response performance of the grid-connected current, but also when the system has model mismatch or disturbance, the immune optimization algorithm can perform adaptive optimization and update the parameters of the finite-time controller, thereby ensuring the good control performance of the current loop and improving the anti-interference ability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0058] Figure 1is a flowchart of a grid-connected current finite time convergence method of a converter provided by embodiment one of the present application;

[0059] Figure 2 is a topology diagram of a neutral point clamped (NPC) three-level LCL grid-connected converter provided by embodiment one of the present application;

[0060] Figure 3 is a relationship diagram of an immune optimization algorithm and an immune response system provided by embodiment one of the present application;

[0061] Figure 4 is a grid-connected converter current loop structure block diagram of an immune finite time current controller provided by embodiment one of the present application;

[0062] Figure 5 is an immune algorithm response diagram provided by embodiment one of the present application;

[0063] Figure 6 is a functional module schematic diagram of a grid-connected current finite time convergence system of a converter provided by embodiment two of the present application;

[0064] Figure 7 is a structural schematic diagram of a computer device provided by embodiment three of the present application. DETAILED DESCRIPTION

[0065] In order to make the objectives, characteristics and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the embodiments described below are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0066] In the description of the present application, it should be understood that when one component is considered to be "connected" to another component, it can be directly connected to the other component or there can be a component arranged in the middle. When one component is considered to be "arranged on" another component, it can be directly arranged on the other component or there can be a component arranged in the middle.

[0067] In addition, the terms "long", "short", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only used to facilitate the description of the present application, and do not indicate or imply that the device or element referred to must have this particular orientation, be constructed to operate in this particular orientation, and cannot be understood as a limitation of the present application.

[0068] The technical solutions of the present application will be further described below with reference to the drawings and through specific embodiments.

[0069] Embodiment one

[0070] In view of the defects of the existing grid-connected technology of the converter, the applicant, based on rich practical experience and professional knowledge in designing and manufacturing in this field for many years, and combined with the use of theory, actively researches and innovates, in order to create a technology that can solve the defects in the prior art, so that the grid-connected technology of the converter is more practical. After continuous research, design, and repeated trial and improvement of samples and improvement, the present application with practical value is finally created.

[0071] Please refer to Figure 1 , the embodiment of the present application provides a grid-connected current finite time convergence method of a converter, which comprises:

[0072] S1, constructing the following finite time current controller of the grid-connected converter in the two-phase dq rotating coordinate system:

[0073]

[0074] Wherein, e d and e q are the components of the grid voltage in the dq axis, v d and v q are the components of the AC inverter side voltage in the dq axis, i d and i q are the components of the inductance side current in the dq axis, L is the grid side filter inductance, ω is the grid angular frequency, k s is the gain coefficient, and β is the fractional exponential power;

[0075] It should be noted that in the present embodiment, the grid-connected converter refers to a neutral point clamped (NPC) three-level LCL grid-connected converter, and its topology diagram is shown in Figure 2 .

[0076] In the present embodiment, the step of S1 can be further refined to include the following steps:

[0077] The following mathematical model of the grid-connected converter in the two-phase dq rotating coordinate system is established:

[0078]

[0079] Wherein, e d and e q are the components of the grid voltage in the dq axis, v d and v q are the components of the AC inverter side voltage in the dq axis, i d and i qLet ω be the component of the inductor-side current on the dq axis, L be the grid-side filter inductance, R be the line equivalent resistance, ω be the grid angular frequency, and p be the differential operator.

[0080] Based on the mathematical model and the following first-order finite-time controller, the following finite-time current controller is obtained:

[0081] u = -ksign(x)|x| α k > 0, 0 < α < 1;

[0082]

[0083] Among them, e d and e q Let v be the component of the grid voltage on the dq axis. d and v q i represents the component of the AC inverter-side voltage along the dq axis. d and i q Let L be the inductor-side current component along the dq axis, L be the grid-side filter inductance, ω be the grid angular frequency, and k be the inductor-side current component along the dq axis. s β is the gain coefficient, and β is the fractional exponent.

[0084] It should be noted that finite-time control aims to ensure that the system's state converges to an equilibrium point within a finite time. Therefore, the first consideration should be the design principle of finite-time stability. The definition of finite-time stability is given below. Consider the system:

[0085]

[0086] Where, f:U→R n Let x be a function that is continuous with respect to x on an open region U, and U contains the origin. The solution x = 0 of the system is finite-time stable if and only if the system is stable and convergent in finite time.

[0087] Finite-time convergence means that for any solution x0, there exists a continuous function g(x) such that the solution x(t,x0) of the system satisfies: when t∈[0,g(x0)), x(t,x0)∈U\{0} and When t > g(x0), x(t,x0) = 0, then the above system is globally finite-time stable. A general first-order finite-time controller takes the following form:

[0088] u = -ksign(x)|x| α k>0, 0<α<1.

[0089] In addition, the embodiment is based on finite time stability as a theory, and a finite time current controller of a grid-connected converter vector control system is designed (taking a q-axis current loop as an example). According to the mathematical model and a first-order finite time controller as follows, a q-axis finite time current controller can be obtained:

[0090]

[0091] Since the mathematical models of the d-axis and q-axis current loops are similar, finally, the current loop controller of the grid-connected converter can be obtained as follows:

[0092]

[0093] When the power grid is disturbed, the model parameters will be mismatched, and the control effect of the current loop will be reduced. Therefore, the embodiment introduces an immune optimization algorithm to adaptively update the parameters β of the finite time current controller, so as to ensure the control performance of the current loop.

[0094] S2, introducing the immune optimization algorithm into the finite time current controller, and calculating the following adaptive factor according to the relationship between the immune optimization algorithm and the immune response system:

[0095] γ p = Q{1- τg[Δu(k)]};

[0096] Wherein, Q = γ1 is a gain, τ = γ2 / γ1;

[0097] In the embodiment, before the step of S2, the method further comprises the following steps:

[0098] The relationship between the immune optimization algorithm and the immune response system is established in advance.

[0099] It should be noted that the immune system is a distributed autonomous system with strong robustness and adaptability in a large number of disturbances and uncertain environments, which can accurately identify, respond moderately and effectively exclude non-self components (such as viruses and various pathogens) invading the body and self cells that have undergone mutations, so as to maintain the diversity of antibodies and immune balance.

[0100] The immune optimization algorithm is introduced into the finite time current controller, and the relationship between the immune optimization algorithm and the immune response system is as shown in Figure 3 .

[0101] According to the corresponding relationship between the immune optimization algorithm and the immune response system in Figure 3 , the number of antigens of the kth generation is e(k), the concentration of Th cells generated by antigen stimulation is CTh(k), the concentration of inhibitory Ts cells is CTs(k), and the stimulation u(k) received by B cells is:

[0102] u(k) = C Th (k) - C Ts (k);

[0103] C Th (k) = γ1·e(k);

[0104] C Ts (k) = γ2·g[Δu(k)]e(k);

[0105] Let u(k) be the output of the controller, then the feedback control law is as follows:

[0106] u(k) = γ1·e(k) - γ2·g[Δu(k)]e(k) = γ p ·e(k);

[0107] wherein,

[0108] γ p = Q{1 - τg[Δu(k)]}.

[0109] The above formula is an adaptive factor obtained by the immune algorithm, Q = γ1 is a gain, and τ = γ2 / γ1.

[0110] The parameter Q controls the reaction speed, and the parameter τ controls the stability effect and plays an inhibitory role. According to the correspondence between the immune optimization algorithm and the immune response system described in the Figure 3 , the influence of the antigen concentration change on the antibody production is studied, and g[Δu(k)] = 1 - exp(Δu 2 / a)

[0111] is taken.

[0112] Wherein, a determines the input-output relationship and output curve of the nonlinear function g[Δu(k)], and plays an important adjusting role on the adaptive controller parameters. The grid-connected converter current loop structure block diagram of the immune finite-time current controller is as shown in Figure 4 . The immune algorithm response is as shown in Figure 5 .

[0113] S3, in combination with the finite-time current controller and the adaptive factor, an immune optimization finite-time current controller is obtained, parameters of the immune optimization finite-time current controller can be adaptively optimized and updated:

[0114]

[0115] It should be noted that according to the step of S3, the value of a determines the adaptive factor γ pThe size of the adaptive controller parameter β also determines the control performance of the finite time current controller of the converter when the working condition changes. s According to the reciprocal relationship and the parameter value experience of the immune algorithm, when a is 0.003-0.006, τ is 0.5, and Q is 1.2, the adaptive controller parameter β can be smoothly changed according to the model parameter change, so as to ensure the control performance of the finite time current controller.

[0116] When the dq-axis mathematical model is mismatched due to the change of the grid-side parameter caused by uncertain factors, the original controller parameter is used, which causes the control performance of the output grid-side current to decrease or even be unstable. At this time, the immune algorithm performs adaptive parameter calculation and optimization according to the detection error, so that the controller parameter is adaptively matched to the current working condition.

[0117] S4, using the immune optimization finite time current controller to perform finite time convergence on the grid-connected converter.

[0118] In this embodiment, before the step S4, the method further includes the following steps:

[0119] Periodically performing adaptive optimization on the parameters of the immune optimization finite time current controller and updating.

[0120] It should be noted that the optimization function is run every certain period of time, so as to ensure that the controller parameter is re-optimized according to the current working condition when the working condition (load) changes.

[0121] The grid-connected current finite time convergence method provided by the embodiment of the application uses the finite time controller with better convergence performance, and uses the immune optimization algorithm to improve the performance of the finite time controller, so that not only the grid-connected current can converge in a finite time, thereby improving the dynamic response performance of the grid-connected current, but also when the system is mismatched or disturbed, the immune optimization algorithm can perform adaptive optimization and update the parameters of the finite time controller, thereby ensuring the good control performance of the current loop and improving the anti-interference ability of the system.

[0122] Embodiment two

[0123] Please refer to the accompanying drawings Figure 6 The functional module schematic diagram of the grid-connected current finite time convergence system provided by the embodiment of the application is shown in FIG. 2. The system is suitable for executing the grid-connected current finite time convergence method provided by the embodiment of the application. The system specifically includes the following modules.

[0124] The controller construction module 201 is used to construct the following finite time current controller of the grid-connected converter in the two-phase dq rotating coordinate system.

[0125]

[0126] wherein e d and e q are the components of the grid voltage in dq axes, v d and v q are the components of the AC inverter-side voltage in dq axes, i d and i q are the components of the inductor-side current in dq axes, L is the grid-side filter inductance, ω is the grid angular frequency, k s is the gain coefficient, and β is the fractional exponential power;

[0127] a factor calculation module 202, configured to introduce an immune optimization algorithm into the finite-time current controller, and calculate the following adaptive factors according to a relationship between the immune optimization algorithm and an immune response system:

[0128] γ p = Q{1- τg[Δu(k)]};

[0129] wherein Q = γ1 is a gain, and τ = γ2 / γ1;

[0130] a controller obtaining module 203, configured to combine the finite-time current controller and the adaptive factors to obtain an immune optimization finite-time current controller, parameters of the immune optimization finite-time current controller being capable of adaptive optimization and update:

[0131]

[0132] a finite-time convergence module 204, configured to perform finite-time convergence on the grid-connected converter by using the immune optimization finite-time current controller.

[0133] Preferably, the controller construction module 201 is specifically configured to:

[0134] establish the following mathematical model of the grid-connected converter in a two-phase dq rotating coordinate system:

[0135]

[0136] wherein e d and e q are the components of the grid voltage in dq axes, v d and v q are the components of the AC inverter-side voltage in dq axes, i d and i q are the components of the inductor-side current in dq axes, L is the grid-side filter inductance, R is the equivalent resistance of the line, ω is the grid angular frequency, and p is a differential operator;

[0137] According to the mathematical model and the following first-order finite time controller, the following finite time current controller is obtained:

[0138] u = -ksign(x)|x| α k>0, 0<α<1;

[0139]

[0140] wherein e d and e q are components of the grid voltage in the dq axis, v d and v q are components of the AC inverter side voltage in the dq axis, i d and i q are components of the inductance side current in the dq axis, L is the grid side filter inductance, ω is the grid angular frequency, k s is the gain coefficient, and β is the fractional exponential power.

[0141] Preferably, the system further comprises:

[0142] a periodic updating module for periodically performing adaptive optimization and updating of parameters of the immune optimization finite time current controller.

[0143] Preferably, the system further comprises:

[0144] a relationship establishing module for pre-establishing a relationship between the immune optimization algorithm and the immune response system.

[0145] The converter grid-connected current finite time convergence system provided by the embodiment of the application adopts a finite time controller with better convergence performance and an immune optimization algorithm to improve the performance of the finite time controller, so that not only the grid-connected current can converge in a finite time, thereby improving the dynamic response performance of the grid-connected current, but also when the system is subjected to model mismatch or disturbance, the immune optimization algorithm can perform adaptive optimization and update the parameters of the finite time controller, thereby ensuring good control performance of the current loop and improving the anti-interference ability of the system.

[0146] The system described above can execute the method provided by any embodiment of the application and has the corresponding functional modules and beneficial effects of the method.

[0147] Embodiment Three

[0148] Figure 7 Fig. 3 is a structural schematic diagram of a computer device provided by Embodiment Three of the application. Figure 7 Fig. 3 is a block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the application. Figure 7The computer device 12 shown is but one example of a computing device that should not limit the scope of functionality or use of the embodiments of the application.

[0149] As shown Figure 7 The computer device 12 is shown in the form of a generic computing device. The components of computer device 12 can include, but are not limited to, one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including system memory 28 to processing unit 16.

[0150] Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics bus, a processor or local bus using any of a variety of bus architectures. By way of example, these architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0151] Computer device 12 typically includes a variety of computer system readable media. Such media can be any available media that is located either in or out of the computer device 12, such as volatile and non-volatile media, removable and non-removable media.

[0152] The system memory 28 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Computer device 12 can further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, a storage system 34 can be provided for reading from and writing to non-removable, non-volatile magnetic media (e.g., a "hard drive"). Figure 7 not shown, a magnetic hard disk drive for reading from and writing to non-removable, non-volatile magnetic media (e.g., a "hard drive"). Although not specifically shown, such Figure 7 In alternative embodiments, a magnetic hard disk drive, a solid state drive (SSD) which is a non- volatile computer storage media, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic medium (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk (e.g., a CD-ROM, DVD-ROM or other optical media) can be provided. In these instances, each drive can be connected to the bus 18 by one or more data media interfaces. The memory 28 can include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the application.

[0153] Program / utility 40 having a set of program modules 42 can be stored in memory 28 by way of example, such program modules 42 include an operating system, one or more application programs, other program modules, and program data, each or some combination thereof, which can include implementation of the network environment as in each of the above examples or some combination thereof. Program modules 42 generally carry out the functions and / or methodologies of embodiments of the application as described herein.

[0154] Computer device 12 can also communicate with one or more external devices 14 such as a keyboard or pointing device, a display 24, etc.; one or more devices that enable a user to interact with computer device 12; and / or one or more devices that enable computer device 12 to communicate with one or more other computing devices. Such communication can be via input / output (I / O) interfaces 22. Still yet, computer device 12 can communicate with one or more networks, such as a local area network (LAN), a general wide area network (WAN), and / or a public network such as the Internet, via network adapter 20. As depicted, network adapter 20 communicates with the other components of computer device 12 via bus 18. It should be appreciated that although not shown, other hardware and / or software modules could be used in conjunction with computer device 12. Examples, include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc. Figure 7 Other hardware and / or software modules are suitable for use with computer device 12 in conjunction with embodiments of the application, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

[0155] Processing unit 16 performs various functions and data processing by running programs stored in system memory 28, such as implementing the current transformer grid-connected current finite-time convergence method provided by embodiments of the application.

[0156] Embodiment four

[0157] Embodiment four of the application provides a computer readable storage medium, which stores computer executable instructions, the instructions are executed by a processor to implement the current transformer grid-connected current finite-time convergence method provided by all embodiments of the application:

[0158] Any combination of one or more computer readable medium can be utilized. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium can be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0159] A computer readable signal medium can include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal can take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium can be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0160] Program code embodied on a computer readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0161] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In an embodiment, the present application is directed to computer program products comprising machine-readable media for carrying or having machine-executable instructions or programs

[0162] The above description of the embodiments has been provided for the purpose of illustration and description. It is not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. Individual elements or features of a particular embodiment are generally not limited to that particular embodiment, but are interchangeable with other embodiments in accordance with the disclosure, to the extent not otherwise explicitly stated. In many instances, similar elements and / or features can be utilized wherever technically possible and practicable. Such alterations, modifications, and improvements are intended to be part of this disclosure, and are intended to be within the scope of the disclosure.

[0163] The example embodiments are provided so that this disclosure will be thorough, and will fully convey the scope to those who are skilled in the art. Numerous specific details are set forth such as examples of specific parts, devices, and methods, to provide a thorough understanding of the embodiments of the present disclosure. It will be apparent to those skilled in the art that specific details need not be employed, that example embodiments can be practiced in many different

[0164] Herein, professional terms are used only for the purpose of describing particular example embodiments, and are not intended to be limiting. The singular forms "a," "an," and "the" used herein are intended to mean "one or more" unless the context clearly indicates otherwise. The terms "comprises," "comprising," "includes," and "including" used herein are intended to be inclusive and therefore specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order

[0165] When an element or layer is referred to as being "on", "engaged to", "connected to" or "coupled to" another element or layer, it can be directly on, engaged, connected or coupled to the other element or layer, or intervening elements or layers can be present. In contrast, when an element or layer is referred to as being "directly on", "directly engaged to", "directly connected to" or "directly coupled to" another element or layer, there are no intervening elements or layers present. Other words used to describe the relationship between elements should be interpreted in a like fashion (e.g., "between" versus "directly between", "adjacent" versus "directly adjacent", etc.). The term "and / or" as used herein refers to and encompasses any and all combinations of one or more of the associated listed items. Although the terms first, second, third, etc. can be used herein to describe various elements, components, regions, layers and / or sections, these elements, components, regions, layers and / or sections should not be limited by these terms. These terms can be only used to distinguish one element, component, region, layer or section from another element, component, region, layer or section. Terms such as "first", "second", and other numerical terms when used herein do not imply a sequence or order unless clearly indicated by the context. Thus, a first element, component, region, layer or section discussed below could be termed a second element, component, region, layer or section without departing from the teachings of the example embodiments.

[0166] Spatially relative terms, such as "inner", "outer", "beneath", "below", "lower", "above", "upper", and the like, can be used herein for ease of description to describe one element or feature's relationship to another element(s) or feature(s) as illustrated in the figures. Spatially relative terms can be intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as "below" or "beneath" other elements or features would then be oriented "above" the other elements or features. Thus, the example term "below" can encompass both an orientation of above and below. The device can be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.

Claims

1. A grid-connected current finite-time convergence method for a current transformer, characterized in that, The method comprises: S1, constructing a following grid converter in a two-phase dq rotating coordinate system as follows: where e d and e q are the components of the grid voltage in the dq axes, v d and v q are the components of the AC inverter-side voltage in the dq axes, i d and i q are the components of the inductor-side current in the dq axes, L is the grid-side filter inductance, ω is the grid angular frequency, k s is the gain coefficient, and β is the fractional exponential power; S2, introducing an immune optimization algorithm into the finite-time current controller, and calculating the following adaptive factor according to the relationship between the immune optimization algorithm and the immune response system: gamma p = Q (1 - tau g [Delta u (k)]); Wherein, Q=γ1 is a gain, τ=γ2 / γ1; S3, combining the finite-time current controller and the adaptive factor, obtaining an immune optimization finite-time current controller, the parameters of the immune optimization finite-time current controller can be adaptively optimized and updated: S4, using the immune optimization finite-time current controller to perform finite-time convergence on the following grid converter.

2. The grid current finite-time convergence method for a current transformer according to claim 1, characterized in that, The step S1 comprises: Establishing a following grid converter in a two-phase dq rotating coordinate system as follows: where e d and e q are the components of the grid voltage in dq axes, v d and v q are the components of the AC inverter-side voltage in dq axes, i d and i q are the components of the inductor-side current in dq axes, L is the grid-side filter inductance, R is the line equivalent resistance, ω is the grid angular frequency, and p is the differential operator; According to the mathematical model and the following first-order finite-time controller, the following finite-time current controller is obtained: u = -k sign(x) \x\ / (1 + a \x\ )1+α α k > 0, 0 < a < 1 where e d and e q are the components of the grid voltage in the dq axes, v d and v q are the components of the AC inverter-side voltage in the dq axes, i d and i q are the components of the inductor-side current in the dq axes, L is the grid-side filter inductance, ω is the grid angular frequency, k s is the gain coefficient, and β is the fractional exponential power.

3. The grid current finite-time convergence method for a current transformer according to claim 1, characterized in that, Before the step S4, the method further comprises: Periodically adaptively optimizing and updating the parameters of the immune optimization finite-time current controller.

4. The grid current finite-time convergence method for a current transformer according to claim 1, characterized in that, Before the step S2, the method further comprises: Pre-establishing the relationship between the immune optimization algorithm and the immune response system.

5. A grid-connected current limiter finite-time convergence system, characterized in that, The system comprises: A controller construction module for constructing a following grid converter in a two-phase dq rotating coordinate system as follows: where e d and e q are the components of the grid voltage in the dq axes, v d and v q are the components of the AC inverter-side voltage in the dq axes, i d and i q are the components of the inductor-side current in the dq axes, L is the grid-side filter inductance, ω is the grid angular frequency, k s is the gain coefficient, and β is the fractional exponential power; A factor calculation module for introducing an immune optimization algorithm into the finite-time current controller, and calculating the following adaptive factor according to the relationship between the immune optimization algorithm and the immune response system: gamma p = Q (1 - τg [Δu (k)] ); Wherein, Q=γ1 is a gain, τ=γ2 / γ1; A controller obtaining module for combining the finite-time current controller and the adaptive factor, obtaining an immune optimization finite-time current controller, the parameters of the immune optimization finite-time current controller can be adaptively optimized and updated: A finite-time convergence module for using the immune optimization finite-time current controller to perform finite-time convergence on the following grid converter.

6. The current source grid-tie current finite-time convergence system of claim 5, wherein, The controller construction module is specifically configured to: Establish a following grid converter in a two-phase dq rotating coordinate system as follows: where e d and e q are the components of the grid voltage in the dq axes, v d and v q are the components of the AC inverter-side voltage in the dq axes, i d and i q are the components of the inductor-side current in the dq axes, L is the grid-side filter inductance, R is the line equivalent resistance, ω is the grid angular frequency, and p is the differential operator; According to the mathematical model and the following first-order finite-time controller, the following finite-time current controller is obtained: u = -k sign(x) \x\ / (1 + a \x\ )a α k > 0, 0 < a < 1 where e d and e q are the components of the grid voltage in the dq axes, v d and v q are the components of the AC inverter-side voltage in the dq axes, i d and i q are the components of the inductor-side current in the dq axes, L is the grid-side filter inductance, ω is the grid angular frequency, k s is the gain coefficient, and β is the fractional exponential power.

7. The current source grid-tie current finite-time convergence system of claim 5, wherein, The system further comprises: A periodic updating module for periodically adaptively optimizing and updating the parameters of the immune optimization finite-time current controller.

8. The current source grid-tie current finite-time convergence system of claim 5, wherein, The system further comprises: A relationship establishing module for pre-establishing the relationship between the immune optimization algorithm and the immune response system. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to implement the converter grid current finite-time convergence method of any one of claims 1-4.

10. A storage medium containing computer executable instructions executed by a computer processor to implement the converter grid current finite-time convergence method of any one of claims 1-4.

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