A photovoltaic DC microgrid bus voltage stabilization control method and system

Abstract

The present invention discloses a photovoltaic DC microgrid bus voltage stabilization control method and system, which relates to the fields of power electronics technology and control technology, including establishing a generalized reduced-order average model of DC-DC and selecting state variables; using feedback linearization technology to process the nonlinear characteristics of the system and convert the nonlinear system into a linear form; integrating backstepping control, sliding mode control and fixed time theory to design a fixed time adaptive backstepping sliding mode; improving the intermediate control law according to the output of the fixed time adaptive backstepping sliding mode to control the bus voltage stability. The present invention realizes the precise convergence and stabilization of the bus voltage within a fixed time, converts a complex system into a more controllable linear system form, and improves the design efficiency and performance of the controller; the fixed time adaptive backstepping sliding mode observer can quickly and accurately estimate the power change of the CPL, provide real-time feedback for the controller, and enhance the controller's adaptability and robustness to disturbances.

Description

Technical Field

[0001] The present invention relates to the field of power electronics technology and control technology, and in particular to a method and system for stabilizing bus voltage control of a photovoltaic direct current microgrid. Background Art

[0002] With the development of new energy technologies, the penetration rate of distributed power sources in DC microgrids continues to increase. As an important renewable energy source, photovoltaics have large output voltage fluctuations and unstable power output, which poses a challenge to the bus voltage stability of DC microgrids. Traditional control methods such as PI control are difficult to effectively stabilize the DC bus voltage when facing the negative impedance characteristics of constant power loads (CPL). Therefore, a new control strategy is needed to solve this problem. Summary of the invention

[0003] In view of the above existing problems, the purpose of the present invention is to provide a photovoltaic DC microgrid bus voltage stability control method, which adopts a fixed-time adaptive backstepping sliding mode strategy to achieve accurate convergence and rapid stabilization of the bus voltage under various disturbances. A generalized reduced-order average model of DC-DC is established, and the nonlinear characteristics of the system are processed by feedback linearization technology to convert the system into a more controllable linear form. Fixed-time adaptive backstepping sliding mode design integrates backstepping control, sliding mode control and fixed-time theory to design a fixed-time adaptive backstepping sliding mode. The system is converted into a Brunovsky standard form through coordinate transformation, and the intermediate control law and sliding mode surface are designed in combination with the observation results of the adaptive backstepping sliding mode to achieve accurate control and rapid stabilization of the bus voltage. Specifically, it includes the steps of defining tracking error, designing virtual control law, selecting sliding mode surface and reaching law, etc., to ensure that the system state quantity converges to the expected value within a fixed time.

[0004] In order to solve the above technical problems, a photovoltaic DC microgrid bus voltage stability control method is proposed, including:

[0005] A generalized reduced-order average model of DC-DC is established and state variables are selected. Feedback linearization technology is used to handle the nonlinear characteristics of the system and convert the nonlinear system into a linear form. Backstepping control, sliding mode control and fixed-time theory are integrated to design a fixed-time adaptive backstepping sliding mode. According to the output of the fixed-time adaptive backstepping sliding mode, the intermediate control law is improved to control the bus voltage stability.

[0006] As a preferred solution of a photovoltaic DC microgrid bus voltage stabilization control method described in the present invention, wherein: the generalized reduced-order average model of DC-DC includes: load current It is expressed as:

[0007] ,

[0008] in, is the output voltage of the DC-DC converter, is the DC microgrid load impedance, is the DC microgrid load power.

[0009] Each phase of the converter The inductance value is the same. According to Kirchhoff's law, the DC-DC converter state equation is:

[0010] ,

[0011] ,

[0012] in, is the input voltage of the DC-DC converter, is the filter inductor, is the filter capacitor, is the i-th current flowing through the inductor The current, is the total input current, is the duty cycle of the i-th phase, t is the time variable, is the current flowing through the inductor L.

[0013] The sliding mode observer model is designed, and the DC-DC converter reduced-order average model is obtained according to the DC-DC converter state equation:

[0014] ,

[0015] According to the DC-DC converter reduced-order average model, the state variables are selected , .

[0016] Feedback linearization technology is used to deal with the nonlinear characteristics of the system and transform the nonlinear system into a linear form.

[0017] As a preferred solution of a photovoltaic DC microgrid bus voltage stabilization control method described in the present invention, wherein: the feedback linearization technology processing system includes constructing a DC-DC converter state equation based on feedback linearization technology and expressing it as:

[0018] ,

[0019] in: Represents the energy stored in the capacitor and inductor of the DC-DC converter, Represents the total input power, and the derivative of the DC-DC converter state equation is:

[0020] ,

[0021] , , , They are the results of differentiating the original values, and the results of the differentiation are expressed in standard form:

[0022] ,

[0023] in: is the mismatch disturbance, To match the disturbance, is the intermediate control law:

[0024] ,

[0025] in, The standard form of energy stored in the capacitor and inductor of the DC-DC converter after derivation is: is the standard form of the total input power after differentiation, and d is the duty cycle.

[0026] As a preferred solution of a photovoltaic DC microgrid bus voltage stability control method described in the present invention, wherein: the load current includes using MPC to perform current prediction to improve estimation accuracy, by considering improving adaptive parameters Dynamically adjust current prediction:

[0027] ,

[0028] in, represents the state of the system at time t, is the load current value at time t, is the input voltage value at time t.

[0029] Enhance the adaptability of the model:

[0030] ,

[0031] ,

[0032] in, For the moment A is the state vector, A is the state transfer matrix, which expresses the relationship between system states. B is the control input matrix, which represents the influence of control input on the state. is the control input vector and the control strategy at time t. is the nonlinear factor, and k is the adjustment gain.

[0033] Deviation based on load current estimation: ,in, is the expected load current value, To predict the load current value, optimize the objective function:

[0034] ,

[0035] in, is the objective function, which indicates the loss value that needs to be optimized in the entire control process. T is the evaluation time window size, j is the variable index, According to the adaptive parameters The adjusted weighting matrix, To control the change of input and increase the control accuracy used. The weight matrix of the control input constrains the change of the control input.

[0036] Introduce adaptive law to update parameters :

[0037] ,

[0038] According to the solution of the optimization problem, the optimal control input is calculated as , combined with historical status updates , future prediction of the load current with adaptive current estimation:

[0039] ,

[0040] Update the load current estimate:

[0041] ,

[0042] Will Substitute the initial load current into the generalized reduced-order average model of the DC-DC, where is the updated weight for the deviation of the load current estimate, is the update weight when updating the load current estimate.

[0043] As a preferred solution of a photovoltaic DC microgrid bus voltage stability control method described in the present invention, wherein: the fixed time adaptive backstepping sliding mode includes: To improve, define Tracking error :

[0044] ,

[0045] in, for The reference value to be tracked is expressed as:

[0046] ,

[0047] in: , They are , reference value.

[0048] Design virtual control law based on system characteristics for:

[0049] ,

[0050] in: and is a positive constant, s1 and s2 are virtual control law coefficients, , . is the symbolic function, for The derivative of .

[0051] definition The tracking error is:

[0052] ,

[0053] The tracking error The derivative of is expressed as:

[0054] ,

[0055] Among them, q1 and q2 are error coefficients.

[0056] As a preferred solution of a photovoltaic DC microgrid bus voltage stability control method described in the present invention, the fixed-time adaptive backstepping sliding mode also includes selecting a sliding mode surface and a reaching law so that the system state quantity converges to an expected value within a fixed time. The selected fixed-time adaptive backstepping sliding mode surface is expressed as:

[0057] ,

[0058] in, , and is a positive constant, s is the backstepping sliding surface function, t is the upper limit of the integral time, is the time constant.

[0059] When the system state converges to the neighborhood of the sliding surface, there exists , when the fixed-time adaptive backstepping sliding mode observer converges, the tracking error The derivative of is expressed as:

[0060] ,

[0061] in, is the derivative of the backstepping sliding surface function.

[0062] As a preferred solution of a photovoltaic DC microgrid bus voltage stabilization control method described in the present invention, the improved intermediate control law includes: according to the result of the derivation of the DC-DC converter state equation and the tracking error The derivative result of the fixed-time adaptive backstepping sliding mode observer improves the intermediate control law for:

[0063] ,

[0064] The newly obtained improved intermediate control law By inputting the intermediate control law u, the state equation of the fixed-time adaptive backstepping sliding mode observer is obtained, which makes the DC bus voltage converge accurately within a fixed time.

[0065] Another object of the present invention is to provide a photovoltaic DC microgrid bus voltage stability control system, which aims to solve key problems such as nonlinear characteristic processing, voltage stability, dynamic load adaptation, control accuracy and response speed, system model uncertainty and control strategy complexity. By establishing a DC-DC generalized reduced-order average model and using feedback linearization technology to simplify nonlinear control problems, combined with MPC current prediction to improve control accuracy and system adaptability, a fixed-time adaptive backstepping sliding mode controller is designed to ensure that the voltage is stable within a fixed time. In addition, the system adopts a modular design to reduce the complexity and implementation difficulty of the overall control strategy, so that the photovoltaic DC microgrid can still maintain efficient and stable operation when facing various load changes and external interference.

[0066] As a preferred solution of a photovoltaic DC microgrid bus voltage stability control system described in the present invention, it is characterized by including a DC-DC generalized reduced-order average model establishment module, a feedback linearization processing module, an MPC current prediction module, a fixed-time adaptive backstepping sliding mode design module and an improved intermediate control law module.

[0067] The DC-DC generalized reduced-order average model establishment module selects state variables and establishes a load current expression and a DC-DC converter state equation.

[0068] The feedback linearization processing module constructs a DC-DC converter state equation using feedback linearization technology, derives the state equation and converts it into a standard form, thereby simplifying the control strategy design.

[0069] The MPC current prediction module uses MPC to perform current prediction, dynamically adjust parameters, optimize objective functions, calculate optimal control inputs, improve load current estimation accuracy, and perform model adaptation.

[0070] The fixed-time adaptive backstepping sliding mode design module defines the tracking error, designs the virtual control law, selects the sliding surface and the reaching law, calculates the derivative of the tracking error, and designs the fixed-time adaptive backstepping sliding mode controller so that the system state quantity converges to the expected value within a fixed time.

[0071] The improved intermediate control law module combines the derivative result of the DC-DC converter state equation and the derivative of the tracking error to obtain an improved intermediate control law, which is input into the system to make the DC bus voltage converge accurately within a fixed time and control the bus voltage to be stable.

[0072] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the method for stabilizing the bus voltage of a photovoltaic DC microgrid are implemented.

[0073] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the method for stabilizing the bus voltage of a photovoltaic DC microgrid are implemented.

[0074] The beneficial effects of the present invention are as follows: a fixed-time adaptive backstepping sliding mode control strategy is adopted to achieve accurate convergence and stabilization of the bus voltage within a fixed time, thereby improving the dynamic response speed and stability of the system; the nonlinear characteristics of the system are processed by feedback linearization technology, and the complex system is converted into a more controllable linear system form, thereby improving the design efficiency and performance of the controller; the fixed-time adaptive backstepping sliding mode observer can quickly and accurately estimate the power change of the CPL, provide real-time feedback for the controller, and enhance the adaptability and robustness of the controller to disturbances. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0076] Figure 1 An overall flow chart of a photovoltaic DC microgrid bus voltage stabilization control method provided by an embodiment of the present invention.

[0077] Figure 2 An equivalent circuit diagram of a photovoltaic DC microgrid providing a method for stabilizing bus voltage of a photovoltaic DC microgrid according to an embodiment of the present invention.

[0078] Figure 3A system solution flow chart of a photovoltaic DC microgrid bus voltage stability control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0079] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0080] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0081] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive with other embodiments, either individually or selectively.

[0082] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0083] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships 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 direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0084] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0085] Example 1, reference Figure 1-Figure 2 , which is the first embodiment of the present invention, and provides a photovoltaic DC microgrid bus voltage stabilization control method, comprising:

[0086] S1: Establish a generalized reduced-order average model of DC-DC and select state variables.

[0087] Furthermore, the load current It is expressed as:

[0088] ,

[0089] in, is the output voltage of the DC-DC converter, is the DC microgrid load impedance, is the DC microgrid load power.

[0090] Each phase of the converter The inductance value is the same. According to Kirchhoff's law, the DC-DC converter state equation is:

[0091] ,

[0092] ,

[0093] in, is the input voltage of the DC-DC converter, is the filter inductor, is the filter capacitor, is the i-th current flowing through the inductor The current, is the total input current, is the duty cycle of the i-th phase, t is the time variable, is the current flowing through the inductor L.

[0094] The sliding mode observer model is designed, and the DC-DC converter reduced-order average model is obtained according to the DC-DC converter state equation:

[0095] ,

[0096] According to the DC-DC converter reduced-order average model, the state variables are selected , .

[0097] Feedback linearization technology is used to deal with the nonlinear characteristics of the system and transform the nonlinear system into a linear form.

[0098] It should be noted that the use of MPC for current prediction improves the estimation accuracy by considering the improvement of adaptive parameters Dynamically adjust current prediction:

[0099] ,

[0100] in, represents the state of the system at time t, is the load current value at time t, is the input voltage value at time t.

[0101] Enhance the adaptability of the model:

[0102] ,

[0103] ,

[0104] in, For the moment A is the state vector, A is the state transfer matrix, which expresses the relationship between system states. B is the control input matrix, which represents the influence of control input on the state. is the control input vector and the control strategy at time t. is the nonlinear factor, and k is the adjustment gain.

[0105] Deviation based on load current estimation: ,in, is the expected load current value, To predict the load current value, optimize the objective function:

[0106] ,

[0107] in, is the objective function, which indicates the loss value that needs to be optimized in the entire control process. T is the evaluation time window size, j is the variable index, According to the adaptive parameters The adjusted weighting matrix, To control the change of input and increase the control accuracy used. The weight matrix of the control input constrains the change of the control input.

[0108] Introduce adaptive law to update parameters :

[0109] ,

[0110] According to the solution of the optimization problem, the optimal control input is calculated as , combined with historical status updates , future prediction of the load current with adaptive current estimation:

[0111] ,

[0112] Update the load current estimate:

[0113] ,

[0114] Will Substitute the initial load current into the generalized reduced-order average model of the DC-DC, where is the updated weight for the deviation of the load current estimate, is the update weight when updating the load current estimate.

[0115] S2: Use feedback linearization technology to deal with the nonlinear characteristics of the system and convert the nonlinear system into a linear form.

[0116] Furthermore, the DC-DC converter state equation based on feedback linearization technology is expressed as:

[0117] ,

[0118] in: Represents the energy stored in the capacitor and inductor of the DC-DC converter, Represents the total input power, and the derivative of the DC-DC converter state equation is:

[0119] ,

[0120] , , , They are the results of differentiating the original values, and the results of the differentiation are expressed in standard form:

[0121] ,

[0122] in: is the mismatch disturbance, To match the disturbance, is the intermediate control law:

[0123] ,

[0124] in, The standard form of energy stored in the capacitor and inductor of the DC-DC converter after derivation is: is the standard form of the total input power after differentiation, and d is the duty cycle.

[0125] S3: Integrate backstepping control, sliding mode control and fixed-time theory to design a fixed-time adaptive backstepping sliding mode.

[0126] Furthermore, for the intermediate control law To improve, define Tracking error :

[0127] ,

[0128] in, for The reference value to be tracked is expressed as:

[0129] ,

[0130] in: , They are , reference value.

[0131] Design virtual control law based on system characteristics for:

[0132] ,

[0133] in: and is a positive constant, s1 and s2 are virtual control law coefficients, , . is the symbolic function, for The derivative of .

[0134] definition The tracking error is:

[0135] ,

[0136] The tracking error The derivative of is expressed as:

[0137] ,

[0138] Among them, q1 and q2 are error coefficients.

[0139] The sliding surface and reaching law are selected to make the system state quantity converge to the expected value within a fixed time. The selected fixed-time adaptive backstepping sliding surface is expressed as:

[0140] ,

[0141] in, , and is a positive constant, s is the backstepping sliding surface function, t is the upper limit of the integral time, is the time constant.

[0142] When the system state converges to the neighborhood of the sliding surface, there exists , when the fixed-time adaptive backstepping sliding mode observer converges, the tracking error The derivative of is expressed as:

[0143] ,

[0144] in, is the derivative of the backstepping sliding surface function.

[0145] S4: According to the output of the fixed-time adaptive backstepping sliding mode, the intermediate control law is improved to control the bus voltage stability.

[0146] Furthermore, according to the results of the derivation of the DC-DC converter state equation and the tracking error The derivative result of the fixed-time adaptive backstepping sliding mode observer improves the intermediate control law for:

[0147] ,

[0148] The newly obtained improved intermediate control law By inputting the intermediate control law u, the state equation of the fixed-time adaptive backstepping sliding mode observer is obtained, which makes the DC bus voltage converge accurately within a fixed time.

[0149] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0150] Embodiment 2, the second embodiment of the present invention, is different from the previous embodiment in that:

[0151] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0152] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0153] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0154] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0155] Example 3, reference Figure 3 , which is the third embodiment of the present invention, provides a photovoltaic DC microgrid bus voltage stability control system, including a DC-DC generalized reduced-order average model building module, a feedback linearization processing module, an MPC current prediction module, a fixed-time adaptive backstepping sliding mode design module and an improved intermediate control law module.

[0156] The DC-DC generalized reduced-order average model building module selects state variables and builds load current expressions and DC-DC converter state equations.

[0157] The feedback linearization processing module uses feedback linearization technology to construct the DC-DC converter state equation, derives the state equation and converts it into a standard form to simplify the control strategy design.

[0158] The MPC current prediction module uses MPC to predict current, dynamically adjust parameters, optimize objective functions, calculate optimal control inputs, improve load current estimation accuracy, and perform model adaptation.

[0159] The fixed-time adaptive backstepping sliding mode design module defines the tracking error, designs the virtual control law, selects the sliding surface and reaching law, calculates the derivative of the tracking error, and designs the fixed-time adaptive backstepping sliding mode controller so that the system state quantity converges to the expected value within a fixed time.

[0160] The improved intermediate control law module combines the derivative results of the DC-DC converter state equation and the derivative of the tracking error to obtain an improved intermediate control law, which is input into the system to make the DC bus voltage converge accurately within a fixed time and control the bus voltage to be stable.

[0161] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for stabilizing bus voltage of a photovoltaic DC microgrid, characterized in that: include, Establish a generalized reduced-order average model of DC-DC and select state variables; Use feedback linearization technology to deal with the nonlinear characteristics of the system and transform the nonlinear system into a linear form; Integrate backstepping control, sliding mode control and fixed time theory to design fixed time adaptive backstepping sliding mode; According to the output of fixed-time adaptive backstepping sliding mode, the intermediate control law is improved to control the bus voltage stability; The fixed-time adaptive backstepping sliding mode includes improving the intermediate control law u and defining the tracking error e1 of y1: e1=y1-y 1d Among them, y 1d The reference value to be tracked by y1 is expressed as: Among them, i Lref 、V inref i L 、V in Reference value of Design virtual control law according to system characteristics 2d for: Among them: α1 and β1 are positive constants, s1 and s2 are virtual control law coefficients, 0<s1<1, s2>1; sig is the sign function, for y 1d The derivative of Define the tracking error of y2 as: <h2 style=";text-align:left;direction:ltr">e2 = y2 - y<h2 style=";text-align:left;direction:ltr"> 2d Then the derivative of the tracking error e1 is expressed as: Among them, q1 and q2 are error coefficients; The fixed-time adaptive backstepping sliding mode further includes selecting a sliding mode surface and a reaching law so that the system state quantity converges to an expected value within a fixed time. The selected fixed-time adaptive backstepping sliding mode surface is expressed as: Among them, α0, α2 and β2 are positive constants, s is the backstepping sliding surface function, t is the integral time upper line, and τ is the time constant; When the system state converges to the neighborhood of the sliding surface, there exists When the fixed-time adaptive backstepping sliding mode observer converges, the derivative of the tracking error e2 is expressed as: in, is the derivative of the backstepping sliding surface function; The improved intermediate control law includes obtaining the fixed-time adaptive backstepping sliding mode observer improved intermediate control law u according to the result of the derivation of the DC-DC converter state equation and the result of the derivative of the tracking error e2. eq for: The newly obtained improved intermediate control law u eq By inputting the intermediate control law u, the state equation of the fixed-time adaptive backstepping sliding mode observer is obtained, which makes the DC bus voltage converge accurately within a fixed time.

2. A photovoltaic DC microgrid bus voltage stabilization control method as claimed in claim 1, characterized in that: The generalized reduced-order average model of the DC-DC includes the load current i o It is expressed as: Among them, v o is the output voltage of the DC-DC converter, R s is the DC microgrid load impedance, P D is the DC microgrid load power; Each phase converter has L i The inductance value is the same. According to Kirchhoff's law, the DC-DC converter state equation is: Among them, V in is the input voltage of the DC-DC converter, L i (i=1,2) is the filter inductance, C f is the filter capacitor, i Li is the i-th current flowing through the inductor L i The current, i in is the total input current, d i is the duty cycle of the i-th phase, t is the time variable, i L is the current flowing through the inductor L; The sliding mode observer model is designed, and the DC-DC converter reduced-order average model is obtained according to the DC-DC converter state equation: According to the DC-DC converter reduced-order average model, the state variable i is selected L 、v o ; Feedback linearization technology is used to deal with the nonlinear characteristics of the system and transform the nonlinear system into a linear form.

3. A photovoltaic DC microgrid bus voltage stabilization control method as claimed in claim 2, characterized in that: The feedback linearization technology processing system includes constructing a DC-DC converter state equation based on feedback linearization technology and expressing it as: Where y1 represents the energy stored in the capacitor and inductor of the DC-DC converter, y2 represents the total input power, and the derivative of the DC-DC converter state equation is: l L 、v o They are the results of differentiating the original values, and the results of the differentiation are expressed in standard form: Among them, f1 is the mismatch disturbance, f2 is the matching disturbance, and u is the intermediate control law: in, The standard form of energy stored in the capacitor and inductor of the DC-DC converter after derivation is: is the standard form of the total input power after differentiation, and d is the duty cycle.

4. A photovoltaic DC microgrid bus voltage stabilization control method as claimed in claim 3, characterized in that: The load current includes using MPC to perform current prediction to improve estimation accuracy and dynamically adjusting the current prediction by considering an improved adaptive parameter θ(t): Among them, x(t) represents the state of the system at time t, i old (t) is the load current value at time t, v o (t) is the input voltage value at time t; Enhance the adaptability of the model: x(t+1)=Ax(t)+Bh(t)+g(x(t)) g(x(t))=k×θ(t)×(i o (t)-v o (t)) Among them, x(t+1) is the state vector at time t+1, A is the state transfer matrix, which expresses the relationship between system states; B is the control input matrix, which represents the influence of control input on the state; h(t) is the control input vector, the control strategy at time t; g(x(t)) is the nonlinear factor, and k is the adjustment gain; Deviation based on load current estimation: in, is the expected load current value, i o (t) is the predicted load current value, and the optimization objective function is: Among them, J is the objective function, which represents the loss value that needs to be optimized in the whole control process; T is the evaluation time window size, j is the variable index, Q(θ(t+k)) is the weighting matrix adjusted according to the adaptive parameter θ, Δh(t+k) is the change in the control input, which increases the control accuracy used; H is the weight matrix of the control input, which limits the change of the control input; Introduce an adaptive law to update the parameters θ(t): θ(t+1)=θ(t)+γ·∈(t) According to the solution of the optimization problem, the optimal control input is calculated as h * (t) = argmin h J, while combining the historical state update E, adaptively estimates the future prediction of the current load current: i o (t)=E(Ax(t)+Bh * (t)+g(x(t))) Update the load current estimate: i o (t+1)=i o (t)+λ·∈(t) will i o (t+1) is substituted into the initial load current of the generalized reduced-order average model of DC-DC, where γ is the update weight of the deviation of the load current estimate, and λ is the update weight when updating the load current estimate.

5. A system using a photovoltaic DC microgrid bus voltage stabilization control method as claimed in any one of claims 1 to 4, characterized in that: It includes DC-DC generalized reduced-order average model building module, feedback linearization processing module, MPC current prediction module, fixed-time adaptive backstepping sliding mode design module and improved intermediate control law module; The DC-DC generalized reduced-order average model establishment module selects state variables and establishes a load current expression and a DC-DC converter state equation; The feedback linearization processing module constructs the DC-DC converter state equation using feedback linearization technology, derives the state equation and converts it into a standard form to simplify the control strategy design; The MPC current prediction module uses MPC to perform current prediction, dynamically adjust parameters, optimize objective functions, calculate optimal control inputs, improve load current estimation accuracy, and perform model adaptation; The fixed-time adaptive backstepping sliding mode design module defines the tracking error, designs the virtual control law, selects the sliding surface and the reaching law, calculates the derivative of the tracking error, and designs the fixed-time adaptive backstepping sliding mode controller so that the system state quantity converges to the expected value within a fixed time; The improved intermediate control law module combines the derivative result of the DC-DC converter state equation and the derivative of the tracking error to obtain an improved intermediate control law, which is input into the system to make the DC bus voltage converge accurately within a fixed time and control the bus voltage to be stable.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of a photovoltaic DC microgrid bus voltage stabilization control method described in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a photovoltaic DC microgrid bus voltage stabilization control method described in any one of claims 1 to 4 are implemented.

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