Photovoltaic grid-connected inverter parameter multi-objective optimization design method, system and device and storage medium

Abstract

The invention discloses a photovoltaic grid-connected inverter parameter multi-objective optimization design method, system and device and a storage medium, and belongs to the technical field of new energy grid connection, and the method comprises the steps: constructing a control frame of a photovoltaic grid-connected inverter system, and the control frame comprises a filter and a controller; establishing a harmonic content model for representing the harmonic content of the output current of the system and a transfer function model for representing the dynamic response characteristics of the system based on the control framework; preliminarily determining a feasible region of filter parameters according to the electrical indexes, and determining a final feasible region of the filter and controller parameters based on a transfer function model; establishing a multi-dimensional optimization objective of the system, and solving a multi-dimensional optimization objective function to obtain optimal parameter configuration of the system; the method can achieve the collaborative optimization of harmonic suppression, dynamic adjustment time, steady-state control precision and filter cost, balance performance and economy, flexibly adapt to different engineering priority requirements, can be directly applied to engineering design, and improves the comprehensive operation performance of a photovoltaic grid-connected system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy grid connection, and in particular to a photovoltaic grid-connected inverter parameter multi-objective optimization design method, system, device and storage medium. BACKGROUND

[0002] As the core device connecting photovoltaic array and power grid, the parameter setting of photovoltaic grid-connected inverter deeply affects the grid-connected quality of photovoltaic power and the safe operation of power system. At present, there are many problems in the design of inverter parameters: firstly, the parameter design of controller and filter often lacks coordination, and separately optimizing the controller tends to ignore the harmonic suppression ability of the filter, and only optimizing the filter is difficult to guarantee the dynamic response speed, so that the contradiction between harmonic suppression and fast response is difficult to resolve; secondly, the existing schemes mostly focus on a single performance index, or increase the cost of the filter to strengthen harmonic control, or cause overshoot oscillation to pursue response speed, and it is difficult to coordinate the comprehensive needs of dynamic characteristics, steady-state accuracy and economy. Therefore, developing a parameter setting method that can optimize multiple performance indicators and strictly follow the operation constraints has become a key issue to improve the adaptability of photovoltaic grid-connected inverters. SUMMARY

[0003] In view of the above problems, the present application is proposed. Therefore, the present application provides a photovoltaic grid-connected inverter parameter multi-objective optimization design method, system, device and storage medium to solve the problems of insufficient coupling of controller and filter parameters, single optimization target and incomplete constraint conditions in the existing photovoltaic grid-connected inverter parameter design.

[0004] To solve the above technical problems, the present application provides the following technical scheme: a photovoltaic grid-connected inverter parameter multi-objective optimization design method, comprising: constructing a control framework of a photovoltaic grid-connected inverter system, the control framework comprising a filter and a controller; establishing a harmonic content model for representing the harmonic content of the system output current and a transfer function model for representing the dynamic response characteristics of the system based on the control framework; determining the feasible region of the filter parameters according to the electrical indicators, and determining the final feasible region of the filter and controller parameters based on the transfer function model; establishing a multi-dimensional optimization target of the system, and solving the multi-dimensional optimization target function to obtain the optimal parameter configuration of the system.

[0005] As a preferred scheme of the photovoltaic grid-connected inverter parameter multi-objective optimization design method, the control framework of the photovoltaic grid-connected inverter system comprises an LCL type filter, and the LCL type filter comprises an inverter side inductance , a grid side inductance and a filter capacitor C; The controller adopts a proportional-resonant controller, input signals of the proportional-resonant controller include a grid-side inductance The deviation of the current command value and the measured value in the αβ coordinate system, and the feedback coefficient The adjusted filter capacitor C in the αβ coordinate system.

[0006] As a preferred scheme of the photovoltaic grid-connected inverter parameter multi-objective optimization design method, wherein: the harmonic content model for representing the harmonic content of the system output current and the transfer function model for representing the dynamic response characteristic of the system based on the control framework include: marking the inverter-side inductance , the grid-side inductance , the filter capacitor C, the proportional coefficient of the proportional-resonant controller, the resonant coefficient , the parameters to be optimized, establishing the mathematical model of the harmonic content and the transfer function, and being expressed as: Among them, is the transfer function model, is the output harmonic content, is the DC voltage of the inverter, is the first kind of Bessel function, and are integers of different parity, is the modulation degree, is the amplitude of the inverter output current, is the carrier angular frequency, is the fundamental angular frequency, , is the imaginary unit, is the harmonic angular frequency, is the proportional gain, is the controller delay, is the pulse width modulation equivalent link, is the resonant gain, is the bandwidth.

[0007] As a preferred scheme of the photovoltaic grid-connected inverter parameter multi-objective optimization design method, wherein: the electrical indicators include the ripple current, the inductive reactive power and the capacitive reactive power, the feasible region of the filter parameter is preliminarily determined according to the electrical indicators, the final feasible region of the filter and the controller parameters is determined based on the transfer function model, including: the feasible region of the inverter-side inductance and the grid-side inductance is calculated according to the ripple current and the inductive reactive power, and the feasible region of the filter capacitor C is calculated according to the capacitive reactive power.​ Calculating the inverter-side inductance by ensuring that the poles are located in the left half plane , grid-side inductance , filter capacitor C, feedback coefficient , proportional resonant controller proportional coefficient , resonance coefficient feasible region.

[0008] As a preferred scheme of the photovoltaic grid-connected inverter parameter multi-objective optimization design method, the system multi-dimensional optimization objective includes a first objective, a second objective, a third objective, and a fourth objective. The first objective is to reduce the output harmonic content, and the output harmonic content is constrained within a first threshold. The second objective is to control the steady-state accuracy, and the steady-state accuracy is constrained within a second threshold. The third objective is to control the dynamic response, and the dynamic adjustment time is constrained within a third threshold. The fourth objective is to reduce the filter economic cost, and the total value of the LCL type filter is minimized based on the price coefficient of the inductance and the capacitance.

[0009] As a preferred scheme of the photovoltaic grid-connected inverter parameter multi-objective optimization design method, the system optimal parameter configuration obtained by solving the multi-dimensional optimization objective function includes: If the current output harmonic content is within the first threshold, the evaluation value of the first objective is If the current output harmonic content is not within the first threshold, the evaluation value of the first objective is ; If the current steady-state accuracy is within the second threshold, the evaluation value of the second objective is If the current steady-state accuracy is not within the second threshold, the evaluation value of the second objective is ; If the current dynamic adjustment time is within the third threshold, the evaluation value of the third objective is If the current dynamic adjustment time is not within the third threshold, the evaluation value of the third objective is ; The price coefficient of the inductance and the capacitance is preset, and if the total value of the LCL type filter is lowest and the inverter-side inductance is greater than the grid-side inductance , the evaluation value of the fourth objective is , otherwise ; The evaluation values of the four objectives are added to obtain a comprehensive evaluation value, and the system parameter configuration obtained by optimization is output.

[0010] As a preferred scheme of the photovoltaic grid-connected inverter parameter multi-objective optimization design method, wherein: the steady accuracy, dynamic adjustment time and total value are represented as: Wherein, is the coordinate current converted from the three-phase current output by the inverter is the coordinate current converted from the three-phase current output by the inverter is the instruction value of is the instruction value of is the coordinate current converted from the three-phase current output by the inverter is the coordinate current converted from the three-phase current output by the inverter is the instruction value of is the instruction value of is the dominant pole of is the dominant pole of is the inductance price coefficient, is the capacitance price coefficient.

[0011] Another object of the present application is to provide a photovoltaic grid-connected inverter parameter multi-objective optimization design system.

[0012] To solve the above technical problems, the present application provides the following technical scheme: a photovoltaic grid-connected inverter parameter multi-objective optimization design system, comprising: a framework modeling module for constructing a control framework of a photovoltaic grid-connected inverter system, the control framework comprising a filter and a controller; a model establishing module for establishing a harmonic content model for characterizing the harmonic content of the system output current and a transfer function model for characterizing the dynamic response characteristics of the system based on the control framework; a parameter feasible region determining module for preliminarily determining the feasible region of the filter parameters according to the electrical indicators and determining the final feasible region of the filter and controller parameters based on the transfer function model; an optimization module for establishing multi-dimensional optimization objectives of the system and solving the multi-dimensional optimization objective functions to obtain the optimal parameter configuration of the system.

[0013] The present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of the photovoltaic grid-connected inverter parameter multi-objective optimization design method when executing the computer program.

[0014] The present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of the photovoltaic grid-connected inverter parameter multi-objective optimization design method.

[0015] The beneficial effects of the present application: the present application can significantly reduce harmonic pollution to meet the power quality requirements of the power grid, quickly respond to photovoltaic power fluctuations to reduce transient process fluctuations, improve steady-state control accuracy to ensure current tracking accuracy, effectively control filter costs, balance performance and economy, and solve the limitations of single target optimization.

[0016] The present application can adjust the weight distribution mechanism of the multi-objective setting of harmonic suppression, dynamic response, steady-state accuracy and cost control, set the penalty value when the target is not achieved, and can flexibly adapt to the priority requirements under different engineering applications to realize targeted optimization. The present application is suitable for photovoltaic grid-connected inverters of different capacities and different control strategies, and does not need to repeatedly adjust the optimization framework for specific scenes, and can be directly applied to engineering design. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0018] Figure 1 A method schematic diagram of a photovoltaic grid-connected inverter parameter multi-objective optimization design method provided by an embodiment of the present application.

[0019] Figure 2 A method flowchart schematic diagram of a photovoltaic grid-connected inverter parameter multi-objective optimization design method provided by an embodiment of the present application.

[0020] Figure 3 A dynamic response waveform diagram of the output current of the photovoltaic grid-connected inverter after optimization of the photovoltaic grid-connected inverter parameter multi-objective optimization design method provided by an embodiment of the present application.

[0021] Figure 4 A harmonic spectrum diagram of the a-phase output current of the photovoltaic grid-connected inverter at 100A after optimization of the photovoltaic grid-connected inverter parameter multi-objective optimization design method provided by an embodiment of the present application.

[0022] Figure 5 A harmonic spectrum diagram of the a-phase output current of the photovoltaic grid-connected inverter at 150A after optimization of the photovoltaic grid-connected inverter parameter multi-objective optimization design method provided by an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part 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 should belong to the protection scope of the present application.

[0024] Embodiment 1, refer to Figures 1-2 For an embodiment of the present application, the embodiment provides a photovoltaic grid-connected inverter parameter multi-objective optimization design method, comprising: S100: constructing a control framework of a photovoltaic grid-connected inverter system, the control framework comprising a filter and a controller; Further, constructing the control framework of the photovoltaic grid-connected inverter system comprises: the filter adopts an LCL type filter, the LCL type filter comprising an inverter side inductance , a grid side inductance and a filter capacitor C; The controller adopts a proportional resonant controller, the input signal of the proportional resonant controller comprising the deviation of the current command value and the measured value of the grid side inductance in the αβ coordinate system, and the current feedback signal of the filter capacitor C adjusted by the feedback coefficient .

[0025] S200: based on the control framework, establishing a harmonic content model for representing the harmonic content of the system output current and a transfer function model for representing the dynamic response characteristics of the system; Further, based on the control framework, establishing the harmonic content model for representing the harmonic content of the system output current and the transfer function model for representing the dynamic response characteristics of the system comprises: marking the inverter side inductance , the grid side inductance , the filter capacitor C, the proportional coefficient and the resonance coefficient of the proportional resonant controller in the system as parameters to be optimized, establishing the mathematical models of the harmonic content and the transfer function, and represented as: wherein, is the transfer function model, is the output harmonic content, is the DC voltage of the inverter, is the first kind of Bessel function, and are integers of different parity, is the modulation degree, is the output current amplitude of the inverter, ωc is the carrier angular frequency, ωc is the carrier angular frequency, , j is the imaginary unit, ωr is the resonant angular frequency, Kp is the proportional gain, Tc is the controller time delay, Kp is the proportional gain, Kp is the proportional gain, Kp is the proportional gain.

[0026] S300: preliminarily determine the feasible region of the filter parameter according to the electrical index, and determine the final feasible region of the filter and the controller parameter based on the transfer function model; Further, the electrical index includes the ripple current, the inductive reactive power and the capacitive reactive power, the feasible region of the filter parameter is preliminarily determined according to the electrical index, and the final feasible region of the filter and the controller parameter is determined based on the transfer function model, including: calculating the feasible region of the inverter-side inductance Lr and the grid-side inductance Lg according to the ripple current and the inductive reactive power, and calculating the feasible region of the filter capacitor C according to the capacitive reactive power; The feasible region of the inverter-side inductance Lr, the grid-side inductance Lg, the filter capacitor C, the feedback coefficient Kf, the proportional coefficient of the proportional-resonant controller Kp, and the resonant coefficient Kr is calculated by ensuring that the poles are located in the left half plane.

[0027] S400: establish a multi-dimensional optimization target of the system, and solve the multi-dimensional optimization target function to obtain the optimal parameter configuration of the system.

[0028] Further, the establishment of the multi-dimensional optimization target of the system includes: a first target, a second target, a third target and a fourth target; The first target is to reduce the output harmonic content, and the output harmonic content is constrained within a first threshold; The second target is the steady-state control accuracy, and the steady-state accuracy is constrained within a second threshold; The third target is the dynamic response, and the dynamic adjustment time is constrained within a third threshold; The fourth target is to reduce the economic cost of the filter, and the total value of the LCL type filter is minimized based on the price coefficient of the inductance and the capacitance.

[0029] Further, solving the multi-dimensional optimization target function to obtain the optimal parameter configuration of the system includes: If the current output harmonic content is within the first threshold, the evaluation value of the first target is ​​​​​​​, if the current output harmonic content is not within the first threshold, the evaluation value of the first target ; if the current steady-state accuracy is within the second threshold, the evaluation value of the second target , if the current steady-state accuracy is not within the second threshold, the evaluation value of the second target ; if the current dynamic regulation time is within the third threshold, the evaluation value of the third target , if the current dynamic regulation time is not within the third threshold, the evaluation value of the third target ; preset price coefficients of inductance and capacitance, if the total value of the LCL filter is the lowest and the value of the inductance on the inverter side is greater than the value of the inductance on the grid side , the evaluation value of the fourth target , otherwise ; add the evaluation values of the four targets to obtain a comprehensive evaluation value, and output the system parameter configuration finally optimized through an optimization algorithm.

[0030] In an optional embodiment, an improved particle swarm optimization algorithm is used to solve the multi-dimensional optimization target, and the specific steps include: initialize the photovoltaic grid-connected inverter; calculate the allowed value range of the filter and controller parameters in the grid-connected inverter; initialize the particle swarm optimization algorithm parameters, randomly generate the positions and speeds of initial particles in the allowed value range of the parameters, and each particle represents a possible parameter configuration; if the output harmonic content of the current parameter configuration is within the first threshold, the evaluation value of the first target , if the output harmonic content of the current parameter configuration is not within the first threshold, the evaluation value of the first target ; if the current steady-state accuracy is within the second threshold, the evaluation value of the second target , if the current steady-state accuracy is not within the second threshold, the evaluation value of the second target ; if the current dynamic regulation time is within the third threshold, the evaluation value of the third target , if the current dynamic regulation time is not within the third threshold, the evaluation value of the third target ; preset price coefficients of inductance and capacitance, if the total value of the LCL filter is the lowest and the value of the inductance on the inverter side is greater than the value of the inductance on the grid side , the evaluation value of the fourth target , vice versa ; calculate + + + and optimize it to be minimum, record the current optimal solution, and update the local optimal and global optimal position of the particle; When the preset number of iterations is reached, the optimization process is ended, and the final optimized photovoltaic parallel inverter parameter configuration is output; if not, the optimization loop is returned to continue execution.

[0031] It should be noted that in the embodiments of the present application, the preset number of iterations can be set according to experimental conditions, for example, when the preset number of iterations is 200, the optimization process is ended.

[0032] It should also be noted that in the embodiments of the present application, the multi-dimensional optimization target is constructed with the core of reducing the output harmonic content, and the dynamic adjustment time, the steady-state control accuracy and the filter economy are fused, wherein the size of the first threshold, the second threshold and the third threshold of the multi-dimensional optimization target can be set according to design requirements, 、 、 、 The size is determined according to the priority of the four targets, so that 、 、 、 The sum of is the lowest.

[0033] Further, it also includes: steady-state accuracy, dynamic adjustment time and total value are expressed as: Wherein, is the coordinate current converted from the three-phase current output by the inverter, is the command value of , is the coordinate current converted from the three-phase current output by the inverter, is the command value of , real is a function for solving the real part of a complex number, is the dominant pole of , is the inductance price coefficient, is the capacitance price coefficient.

[0034] It should be noted that, under the premise of ensuring the stability of the photovoltaic grid-connected inverter system, the present application can achieve the goal of suppressing harmonics first, while considering dynamic regulation performance, improving control accuracy and reducing filter cost, to ensure efficient, safe and economic operation of the photovoltaic grid-connected inverter connected to the power system.

[0035] Embodiment 2, refer to Figures 3-5 For an embodiment of the present application, a photovoltaic grid-connected inverter parameter multi-objective optimization design method is provided, and in order to verify the beneficial effects of the present application, scientific demonstration is carried out through test experiments.

[0036] In this embodiment, the photovoltaic grid-connected inverter is connected to a 380V grid, the inverter capacity is 100kVA, the DC side voltage is 700V, and the fundamental frequency is 50Hz. The size of the first threshold value, the second threshold value and the third threshold value are all set to 1, and are considered to be equally important. In this embodiment, the first threshold value is set to 0.3%, the second threshold value is set to 2%, the third threshold value is set to 35 milliseconds, the inductance price coefficient is set to 0.8, and the capacitance price coefficient is set to 0.2.

[0037] The improved particle swarm optimization algorithm is used to solve the multi-dimensional optimization target, and the optimal parameter configuration inverter side inductance is 0.9mH, the grid side inductance is 0.05mH, the filter capacitance C is 45μF, the feedback coefficient is 6.8, the proportional resonant controller proportional coefficient is 3.9, and the resonance coefficient is 1011.6.

[0038] The optimized photovoltaic grid-connected inverter is connected to the grid, and the inverter output current is set to jump from 100A to 150A at 0.5 seconds, and the inverter output waveform is as shown in Figure 3 , the harmonic spectrum of the a-phase output current when the inverter output current is 100A is as shown in Figure 4 , and the harmonic spectrum of the a-phase output current when the inverter output current is 150A is as shown in Figure 5 , it can be seen from Figure 3 that the dynamic regulation time is 22 milliseconds, which is less than 35 milliseconds, and the control error is 0.82%, which is less than 2%. From Figure 4 and Figure 5 , it can be seen that the content of each harmonic in the converter output current is less than 0.3%, which meets all the optimization targets.

[0039] The present application aims at the problems of insufficient coupling of controller and filter parameters, single optimization target and the like in existing designs, and builds a basic control framework of a photovoltaic grid-connected inverter and an output harmonic content and transfer function model; calculates the allowed value range of filter and controller parameters; establishes a multi-dimensional optimization target with reduction of output current harmonic content as the core, and fuses dynamic adjustment time, steady-state control accuracy and filter economy; then uses a particle swarm optimization algorithm to solve, and outputs optimal parameter configuration, which can realize multi-objective collaborative optimization, balance performance and economy, flexibly adapt to different engineering priority requirements, can be directly applied to engineering design, and improve the comprehensive operation performance of the photovoltaic grid-connected system.

[0040] Embodiment 3 is an embodiment of the present application, which provides a photovoltaic grid-connected inverter parameter multi-objective optimization design system, comprising: A framework building module is configured to build a control framework of the photovoltaic grid-connected inverter system, and the control framework comprises a filter and a controller. A model building module is configured to build a harmonic content model for representing the output current harmonic content of the system and a transfer function model for representing the dynamic response characteristics of the system based on the control framework. A parameter feasible region determination module is configured to preliminarily determine the feasible region of the filter parameters according to the electrical indicators, and determine the final feasible region of the filter and controller parameters based on the transfer function model. An optimization module is configured to build a multi-dimensional optimization target of the system, and solve the multi-dimensional optimization target function to obtain the optimal parameter configuration of the system.

[0041] The photovoltaic grid-connected inverter parameter multi-objective optimization design system provided in the embodiment can realize collaborative optimization of harmonic suppression, dynamic adjustment time, steady-state control accuracy and filter cost, and improve the comprehensive operation performance of the photovoltaic grid-connected system.

[0042] The embodiment further provides an electronic device suitable for the case of the user course recommendation method based on large model generation, comprising: A memory and a processor; the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions to implement the user course recommendation method based on large model generation proposed in the above embodiment.

[0043] The embodiment further provides a storage medium having a computer program stored thereon, and the program is executed by the processor to implement the user course recommendation method based on large model generation proposed in the above embodiment.

[0044] The storage medium proposed in this embodiment belongs to the same inventive concept as the user course recommendation method based on large model generation proposed in the above embodiment, and the technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0045] From the above description about the embodiments, those skilled in the art can clearly understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, such as object-oriented programming languages Java and interpreted scripting language JavaScript.

[0046] The present application is described with reference to flowcharts and / or block diagrams according to the methods, devices (systems), and computer program products of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more blocks.

[0047] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more blocks.

[0048] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a process for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more blocks.

[0049] While the preferred embodiments of the application have been described, additional variations and modifications can be made to the embodiments by those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that additions and modifications are included within the scope of the application, which is defined by the following claims.

[0050] Obviously, various modifications and changes can be made to the present application by those skilled in the art without departing from the spirit and scope of the application. Accordingly, it is intended that all such modifications and changes be included within the scope of the application as defined by the following claims.

[0051] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not to limit the present application. Although the present application is explained in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and all the modifications and replacements should be included in the scope of the claims of the present application.

Claims

1. A multi-objective optimization design method for photovoltaic grid-connected inverter parameters, characterized in that, include: A control framework for a photovoltaic grid-connected inverter system is constructed, the control framework including filters and controllers; Based on the aforementioned control framework, a harmonic content model is established to characterize the harmonic content of the system output current, and a transfer function model is established to characterize the dynamic response characteristics of the system. The feasible region of the filter parameters is initially determined based on the electrical indicators, and the final feasible region of the filter and controller parameters is determined based on the transfer function model. Establish a multi-dimensional optimization objective for the system, and solve the multi-dimensional optimization objective function to obtain the optimal parameter configuration of the system.

2. The multi-objective optimization design method for photovoltaic grid-connected inverter parameters as described in claim 1, characterized in that, The control framework for constructing the photovoltaic grid-connected inverter system includes: the filter is an LCL type filter, and the LCL filter includes an inverter-side inductor. , grid-side inductor and filter capacitor C; The controller is a proportional resonant controller, and the input signal of the proportional resonant controller includes the grid-side inductance. The deviation between the current command value and the measured value in the αβ coordinate system, and the deviation after feedback coefficient. The current feedback signal of the adjusted filter capacitor C in the αβ coordinate system.

3. The multi-objective optimization design method for photovoltaic grid-connected inverter parameters as described in claim 2, characterized in that, Based on the aforementioned control framework, a harmonic content model for characterizing the harmonic content of the system output current and a transfer function model for characterizing the dynamic response characteristics of the system are established, including: incorporating the inverter-side inductance in the system... , grid-side inductor Filter capacitor C, proportional coefficient of proportional resonant controller resonance coefficient The parameters marked as to be optimized are used to establish a mathematical model of harmonic content and transfer function, which is expressed as follows: in, For the transfer function model, To output harmonic content, This is the inverter's DC voltage. For a Bessel function of the first kind, and For integers with different parity, In order to adjust the system, This refers to the output current amplitude of the inverter. The carrier angular frequency, The fundamental angular frequency, , The imaginary unit, The harmonic angular frequency, For proportional gain, For controller delay, This is the equivalent element for pulse width modulation. For resonant gain, For bandwidth.

4. The multi-objective optimization design method for photovoltaic grid-connected inverter parameters as described in claim 3, characterized in that, Electrical parameters include ripple current, inductive reactive power, and capacitive reactive power. Based on these electrical parameters, the feasible region of the filter parameters is initially determined. Then, based on the transfer function model, the final feasible region of the filter and controller parameters is determined, including: calculating the inverter-side inductance based on the ripple current and inductive reactive power. and grid-side inductor The feasible region is calculated based on the capacitive reactive power, and the feasible region of the filter capacitor C is determined. Calculate the inverter-side inductance by ensuring the poles are located in the left half-plane. , grid-side inductor Filter capacitor C, feedback coefficient Proportional coefficient of proportional resonant controller resonance coefficient The feasible domain.

5. The multi-objective optimization design method for photovoltaic grid-connected inverter parameters as described in claim 4, characterized in that, The system's multi-dimensional optimization objectives include: the first objective, the second objective, the third objective, and the fourth objective. The first objective is to reduce the output harmonic content and constrain the output harmonic content within a first threshold. The second objective is steady-state control accuracy, which is constrained within a second threshold. The third objective is a dynamic response, which constrains the dynamic adjustment time within a third threshold. The fourth objective is to reduce the economic cost of filters by minimizing the total value of LCL filters based on the price coefficients of inductors and capacitors.

6. The multi-objective optimization design method for photovoltaic grid-connected inverter parameters as described in claim 4, characterized in that, Solving the multi-dimensional optimization objective function yields the optimal system parameter configuration, including: If the current output harmonic content is within the first threshold, then the evaluation value of the first target is... If the current output harmonic content is not within the first threshold, then the evaluation value of the first target is... ; If the current steady-state accuracy is within the second threshold, then the evaluation value of the second objective is... If the current steady-state accuracy is not within the second threshold, then the evaluation value of the second objective is... ; If the current dynamic adjustment time is within the third threshold, then the evaluation value of the third objective is... If the current dynamic adjustment time is not within the third threshold, then the evaluation value of the third objective is... ; The price coefficients of the preset inductors and capacitors are considered when the total value of the LCL filter is... Lowest and inverter-side inductance Greater than the grid-side inductance The value of the fourth objective is then determined by the given value. ,on the contrary ; The evaluation values ​​of the four objectives are summed to obtain a comprehensive evaluation value, and the final optimized system parameter configuration is output through an optimization algorithm.

7. The multi-objective optimization design method for photovoltaic grid-connected inverter parameters as described in claim 4, characterized in that, Also includes: The steady-state accuracy, dynamic adjustment time, and total value are expressed as follows: in, The three-phase current output from the inverter is obtained by conversion Coordinate current, for The instruction value, The three-phase current output from the inverter is obtained by conversion Coordinate current, for The instruction value is `real`, where `real` is the function for solving the real part of a complex number. for The dominant pole, This is the inductor price coefficient. This is the capacitor price coefficient.

8. A multi-objective optimization design system for photovoltaic grid-connected inverter parameters, employing the multi-objective optimization design method for photovoltaic grid-connected inverter parameters as described in any one of claims 1 to 7, characterized in that, include: A framework construction module is used to build the control framework of a photovoltaic grid-connected inverter system, the control framework including filters and controllers; The model building module is used to build a harmonic content model to characterize the harmonic content of the system output current and a transfer function model to characterize the dynamic response characteristics of the system based on the control framework. The parameter feasible region determination module is used to initially determine the feasible region of filter parameters based on electrical indicators, and to determine the final feasible region of filter and controller parameters based on the transfer function model. The optimization module is used to establish multi-dimensional optimization objectives for the system and solve the multi-dimensional optimization objective function to obtain the optimal parameter configuration of the system.

9. A computer device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the multi-objective optimization design method for photovoltaic grid-connected inverter parameters as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program, characterized in that, when the computer program is executed by a processor, it implements the steps of a multi-objective optimization design method for photovoltaic grid-connected inverter parameters as described in any one of claims 1 to 7.

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