Method and system for controlling three-phase grid-connected inverter

Abstract

The invention relates to the technical field of grid-connected control, in particular to a three-phase grid-connected inverter control method and system, and the method comprises the steps: building a photovoltaic array and a three-phase grid-connected inverter mathematical model; the inverter is controlled by introducing a fractional order control theory and adopting fractional order integral sliding mode control; and inverter parameters are optimized by using a sparrow search neural network algorithm. The method has the advantages that fractional order control, integral control and sliding mode control are combined, electric power is extracted from the photovoltaic power generation system to the maximum extent, and therefore the electric energy conversion efficiency and the electric energy quality are improved. Furthermore, the fractional order sliding mode control method not only reduces power ripples and DC side voltage ripples, but also improves the robustness and stability of the photovoltaic power generation system.

Description

Technical Field

[0001] The present invention relates to the technical field of grid-connected control, and in particular to a control method and system for a three-phase grid-connected inverter. Background Art

[0002] As global energy demand surges, photovoltaic power generation systems, as a clean energy solution, are gradually becoming a strong candidate to replace traditional fossil fuels. Technological advances and cost reductions have made solar power generation more economical and feasible, and its positive impact on the environment has become increasingly prominent. However, the stability of the DC bus voltage and power in the photovoltaic power generation system is crucial to the safe operation of the entire system. At present, the ripple phenomenon in the power transmission process poses a challenge to the power quality and system stability. Although there have been studies on suppressing ripples through MPPT technology with third-order sliding mode control, adding large capacitors or passive filters, and various linear and nonlinear control strategies such as model predictive control, deadbeat control, and PI control technology, these methods are still insufficient in suppressing DC side voltage ripple, reducing costs, reducing volume, or improving system stability. Therefore, in-depth research and development of more effective ripple suppression technology for photovoltaic power generation systems is of great significance to ensure the power quality and system stability of the power grid. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a control method and system for a three-phase grid-connected inverter, which can solve the problems mentioned in the background technology.

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

[0007] In a first aspect, the present invention provides a control method for a three-phase grid-connected inverter, comprising establishing a photovoltaic array and a mathematical model of the three-phase grid-connected inverter;

[0008] The inverter is controlled by introducing fractional-order control theory and adopting fractional-order integral sliding mode control;

[0009] The inverter parameters are optimized using the sparrow search neural network algorithm.

[0010] As a preferred solution of the control method of the three-phase grid-connected inverter of the present invention, the photovoltaic array mathematical model is used to calculate the output characteristics of the photovoltaic array under the actual sunshine intensity and the actual battery temperature.

[0011] As a preferred solution of the control method of the three-phase grid-connected inverter of the present invention, the mathematical model of the three-phase grid-connected inverter includes the output voltage and current on the inverter side, the grid voltage, and the inductance and resistance on the inverter side.

[0012] As a preferred solution of the control method of the three-phase grid-connected inverter of the present invention, wherein: extending the fractional-order control theory to the traditional integer-order control method includes extending the integer-order derivative and integral concepts of calculus to fractional-order derivative and integral concepts.

[0013] As a preferred solution of the control method of the three-phase grid-connected inverter of the present invention, the fractional-order integral sliding mode control includes integral sliding mode control of the current inner loop and fractional-order sliding mode control of the voltage outer loop.

[0014] As a preferred solution of the control method of the three-phase grid-connected inverter of the present invention, the sparrow search neural network algorithm includes position update formulas of the discoverer, the joiner and the guard.

[0015] As a preferred solution of the control method of the three-phase grid-connected inverter of the present invention, wherein: optimizing the inverter parameters by using the sparrow search neural network algorithm includes:

[0016] Initialize the sparrow population and create individual sparrows;

[0017] Calculate the fitness values ​​of N individuals in the sparrow population;

[0018] Update the positions of the discoverer, joiner, and alerter in sequence;

[0019] Determine the position of the current iteration sparrow;

[0020] Output the optimized parameters.

[0021] In a second aspect, the present invention provides a control system for a three-phase grid-connected inverter, comprising: an establishment module for establishing a mathematical model of a photovoltaic array and a three-phase grid-connected inverter;

[0022] A control module, used for controlling the inverter by introducing fractional-order control theory and adopting fractional-order integral sliding mode control;

[0023] The optimization module is used to optimize the inverter parameters using the sparrow search neural network algorithm.

[0024] In a third aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0025] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when the computer program is executed by a processor.

[0026] Compared with the prior art, the invention has the following beneficial effects: it combines fractional-order control, integral control and sliding mode control to extract power from the photovoltaic power generation system to the maximum extent, thereby improving the power conversion efficiency and power quality. Taking the two-stage three-phase grid-connected inverter photovoltaic power generation system as an example, the photovoltaic array and the three-phase grid-connected inverter are first mathematically modeled; then the fractional-order control is theoretically analyzed, and a fractional-order integral sliding mode control method is proposed, that is, the current inner loop adopts integral sliding mode control, and the voltage outer loop adopts fractional-order sliding mode control; finally, the sparrow search neural network algorithm is used to optimize the control parameters in the dual-loop control. The further fractional-order sliding mode control method not only reduces the power ripple and the DC side voltage ripple, but also improves the robustness and stability of the photovoltaic power generation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] 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 ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0028] Figure 1 This is the equivalent circuit diagram of the photovoltaic array.

[0029] Figure 2 This is the control structure diagram of a two-stage three-phase grid-connected inverter.

[0030] Figure 3 This is the overall block diagram of fractional-order integral sliding mode control for three-phase grid-connected inverter based on sparrow search.

[0031] Figure 4 This is a control structure diagram based on sparrow search.

[0032] Figure 5 Flowchart of the sparrow search algorithm.

[0033] Figure 6 A schematic diagram of the internal structure of a computer device. DETAILED DESCRIPTION

[0034] In order to make the above-mentioned objects, 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 accompanying drawings.

[0035] 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.

[0036] 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 does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0037] Example 1

[0038] Reference Figure 1 to Figure 5 , which is the first embodiment of the present invention, and provides a control method for a three-phase grid-connected inverter, comprising:

[0039] S1. Establish the mathematical model of photovoltaic array and three-phase grid-connected inverter.

[0040] Furthermore, the PV array mathematical model is used to estimate the PV array output characteristics under actual sunlight intensity and actual battery temperature.

[0041] It should be noted that the Figure 1 The photovoltaic array equivalent circuit established for this scheme, where I L represents photocurrent, I0 represents reverse saturation current, q represents electron charge, q=1.69×10 19 C, A represents the diode factor, K represents the Boltzmann constant, K = 1.38x10 -23 J / K, T is absolute temperature, R s Represents the series resistance, R sh Represents parallel resistance.

[0042] Furthermore, the mathematical model of the photovoltaic array is shown as follows:

[0043]

[0044] Simplifying the above mathematical model, we can get:

[0045]

[0046] According to formula (2)-(4) and i under standard conditions sc 、uoc 、i m 、u m , the i of the photovoltaic array under the actual sunshine intensity and actual battery temperature can be calculated s ' c 、u o ' c , i′ m , u′ m ,Right now:

[0047]

[0048] In the formula, S represents the actual sunshine intensity, and ΔS = S / S ref -1; T represents the actual battery temperature, and ΔT = TT ref ; N s and N p Respectively represent the number of PV modules in series and in parallel; correlation coefficients a = 0.0025, b = 0.5, c = 0.00288, e = 2.71828. Thus, the PU output characteristics of the PV array under different sunshine intensities can be obtained, and the PV power P can be obtained. pv size.

[0049] Furthermore, the mathematical model of the three-phase grid-connected inverter includes the inverter-side output voltage and current, the grid voltage, and the inverter-side inductance and resistance, wherein the mathematical model of the three-phase photovoltaic grid-connected inverter is the process of mathematically describing the electrical characteristics of the inverter and its interaction with the grid, and further, is the basis for understanding and analyzing how the inverter works in a photovoltaic power generation system.

[0050] It should be noted that this scheme adopts the Figure 2 The two-stage three-phase grid-connected inverter control structure shown mainly includes a photovoltaic array, a Boost converter, a three-phase inverter, an LCL filter, a grid voltage and other parts.

[0051] Furthermore, the three-phase voltage loop equation is established according to Kirchhoff's voltage law:

[0052]

[0053] Among them, u abc and i abc Respectively represent the output voltage and current of the inverter side, e abc Represents the grid voltage, L1 and R1 represent the inductance and resistance of the inverter side respectively. To simplify the model and facilitate inverter control, the loop equation in the dq rotating coordinate system is constructed as follows:

[0054]

[0055] Wherein, w represents the fundamental angular frequency.

[0056] It is further necessary to explain that after the mathematical model of the inverter is established, the power output of the inverter under different conditions is determined based on the power flow calculation based on this model, which includes calculating the actual active power (Pe) and reactive power (Qe), and ensuring that the power factor (PF) of the system is 1, which means that the system uses electricity efficiently without waste. The power flow calculation also involves the conservation of power on the DC side and the AC side to ensure the balance of energy.

[0057] Specifically, the power flow calculation

[0058] The actual active power P in the dq rotating coordinate system e and reactive power Q e Grams are expressed as:

[0059]

[0060] Set the q-axis current to 0 to keep the system power factor PF = 1. The above formula can be rewritten as:

[0061]

[0062] According to the power conservation principle of the inverter DC side and AC side, we can get:

[0063]

[0064] Among them, i c_dc Indicates the DC side capacitor current, i pv_dc Represents the output current of the Boost converter, i in_dc Indicates the inverter input current, P pv Represents photovoltaic power generation power.

[0065] S2. The inverter is controlled by introducing fractional-order control theory and adopting fractional-order integral sliding mode control.

[0066] Furthermore, extending the fractional-order control theory to the traditional integer-order control method includes extending the integer-order derivative and integral concepts of calculus to fractional-order derivative and integral concepts.

[0067] Preferably, fractional order control has the advantages of simple implementation and superior performance. In addition, using this control method in a control system will not only not increase the difficulty of experimental implementation, but also make it more suitable for improving the overall performance of the system.

[0068] Specifically, the expression of the fractional order equation is as follows:

[0069]

[0070] Where D represents the fractional-order operator, a represents the operation order, and a and t represent the minimum and maximum limits, respectively.

[0071] Furthermore, the fractional-order integral sliding mode control includes integral sliding mode control of a current inner loop and fractional-order sliding mode control of a voltage outer loop.

[0072] It should be noted that sliding mode control is a non-explicit control method, which is applied to inverter control due to its simple control and high robustness. In order to reduce the active power ripple and DC side voltage ripple, this scheme uses integral sliding mode control of the grid current inner loop to adjust the grid output power, and fractional order sliding mode control of the voltage outer loop to reduce the DC side voltage ripple.

[0073] S3. Use the sparrow search neural network algorithm to optimize the inverter parameters.

[0074] It should be noted that the Figure 3 The schematic diagram of the fractional-order integral sliding mode control structure of the three-phase grid-connected inverter based on the sparrow search neural network algorithm is shown in Figure 1. The voltage loop adopts fractional-order sliding mode control, the current loop adopts integral sliding mode control, and the sliding mode control parameters are obtained by the sparrow search neural network algorithm (see Figure 1). Figure 4 shown).

[0075] Furthermore, the integral sliding mode control is used to adjust the grid output power, and the state equation of the inverter is:

[0076]

[0077] Where x = [i d i q ] T represents the state vector, u=[u d u q ] T Represents a vector instruction, G = 1 / L1[e d e q ] T represents the perturbation vector. B=1 / L.

[0078] According to the above formula, the expression of grid current error can be obtained as follows:

[0079] e r_dq =i dq -i d ' q (13)

[0080] The integral sliding surface of the current inner loop and its derivative expression are:

[0081]

[0082] Here, m1 and m2 represent positive constants.

[0083] Finally, the integral sliding mode control expression of the two-stage three-phase inverter command is obtained as follows:

[0084]

[0085] In the formula, k d1,2 and k q1,2 Represents the proportional gain of the equivalent instruction term.

[0086] The present invention selects the DC side voltage reference value u′ dc and the actual value u dc The error between is used to control the DC side voltage and to derive the DC side capacitor current i c_dc Expression, the error variable z1 can be expressed as:

[0087] z1=u′ dc -u dc (16)

[0088] The fractional-order sliding surface of the voltage outer loop can be expressed as:

[0089]

[0090] in,

[0091] Get the DC side capacitor current i c_dc The expression is:

[0092]

[0093] Combined with formula (10), the d-axis grid current reference value i is obtained d The expression of ′ is:

[0094]

[0095] Furthermore, the sparrow search neural network algorithm includes position update formulas for the discoverer, joiner, and sentinel.

[0096] It should be noted that the location of the discoverer is updated as follows:

[0097]

[0098] Where, X i,j Indicates the individual position of the sparrow, i indicates the current iteration number, iter maxrepresents the maximum number of iterations, s represents a random number in [0,1], R1 represents the warning value, R1∈[0,1], ST represents the safety value, ST∈[0.5,1], K represents a random number that obeys the normal distribution, and H represents a 1×d matrix in which each element is 1.

[0099] The position update of the participant is as follows:

[0100]

[0101] Where, X p and X worst Represent the current optimal position and the worst position respectively. + Represents a 1×d matrix, each element of which has a random amplitude of 1 or -1.

[0102] The position of the sentinel is updated as follows:

[0103]

[0104] In the formula, k represents a random number that conforms to the standard normal distribution, X best Indicates the current global best position. i represents the current sparrow fitness value, and f w and f g The tables represent the current best and worst fitness values, P represents a uniform random number between [-1,1], and e represents a smaller number.

[0105] Furthermore, the inverter parameters are optimized using the sparrow search neural network algorithm, including:

[0106] Initialize the sparrow population and create sparrow individuals, where the sparrow individuals represent the sparrow individuals of possible solutions;

[0107] Calculate the fitness values ​​of N individuals in the sparrow population;

[0108] Update the positions of the discoverer, joiner, and alerter in sequence;

[0109] It should be noted that the first A sparrows are selected as discoverers according to the fitness value, the sparrow individuals except the discoverers among the N sparrow individuals are selected as joiners, and B sparrow individuals are selected as guards. Further, the values ​​of A and B are a ratio of the population size N.

[0110] Determine the position of the current iteration sparrow;

[0111] Output the optimized parameters.

[0112] It should be noted that before outputting the optimized parameters, it is necessary to check whether the number of iterations exceeds the preset maximum number of iterations. Only when the maximum number of iterations is reached, the optimized controller parameters are output.

[0113] In summary, the beneficial effect of the control method of a three-phase grid-connected inverter of the present invention is that it combines fractional-order control, integral control and sliding mode control, extracts electricity from the photovoltaic power generation system to the maximum extent, thereby improving the power conversion efficiency and power quality, and taking a two-stage three-phase grid-connected inverter photovoltaic power generation system as an example, firstly, the photovoltaic array and the three-phase grid-connected inverter are mathematically modeled; then, the fractional-order control is theoretically analyzed, and a fractional-order integral sliding mode control method is proposed, that is, the current inner loop adopts integral sliding mode control, and the voltage outer loop adopts fractional-order sliding mode control; finally, the sparrow search neural network algorithm is used to optimize the control parameters in the dual-loop control. The further fractional-order sliding mode control method not only reduces the power ripple and the DC side voltage ripple, but also improves the robustness and stability of the photovoltaic power generation system.

[0114] Example 2

[0115] This embodiment provides a control system for a three-phase grid-connected inverter, which includes a building module for building a mathematical model of a photovoltaic array and a three-phase grid-connected inverter;

[0116] A control module, used for controlling the inverter by introducing fractional-order control theory and adopting fractional-order integral sliding mode control;

[0117] The optimization module is used to optimize the inverter parameters using the sparrow search neural network algorithm.

[0118] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0119] Example 3

[0120] This embodiment provides a computer device, which may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a control method for a three-phase grid-connected inverter is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0121] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the following is achieved: establishing a mathematical model of a photovoltaic array and a three-phase grid-connected inverter; controlling the inverter by introducing fractional-order control theory and adopting fractional-order integral sliding mode control; and optimizing the inverter parameters using a sparrow search neural network algorithm.

[0122] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. 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 control method for a three-phase grid-connected inverter, characterized in that: include, Establish mathematical models of photovoltaic arrays and three-phase grid-connected inverters; The inverter is controlled by introducing fractional-order control theory and adopting fractional-order integral sliding mode control; The inverter parameters are optimized using the sparrow search neural network algorithm.

2. The control method of the three-phase grid-connected inverter according to claim 1, characterized in that: The photovoltaic array mathematical model is used to calculate the photovoltaic array output characteristics under actual sunlight intensity and actual battery temperature.

3. The control method of the three-phase grid-connected inverter according to claim 2, characterized in that: The mathematical model of the three-phase grid-connected inverter includes the output voltage and current of the inverter side, the grid voltage, and the inductance and resistance of the inverter side.

4. The control method of the three-phase grid-connected inverter according to claim 3, characterized in that: Extending the fractional-order control theory to the traditional integer-order control method includes extending the integer-order derivative and integral concepts of calculus to fractional-order derivative and integral concepts.

5. The control method of the three-phase grid-connected inverter according to claim 4, characterized in that: The fractional-order integral sliding mode control includes integral sliding mode control of a current inner loop and fractional-order sliding mode control of a voltage outer loop.

6. The control method of the three-phase grid-connected inverter according to any one of claims 1 to 5, characterized in that: The sparrow search neural network algorithm includes position update formulas for discoverers, joiners and guards.

7. The control method of the three-phase grid-connected inverter according to claim 6, characterized in that: The optimization of inverter parameters using the sparrow search neural network algorithm includes: Initialize the sparrow population and create individual sparrows; Calculate the fitness values ​​of N individuals in the sparrow population; Update the positions of the discoverer, joiner, and alerter in sequence; Determine the position of the current iteration sparrow; Output the optimized parameters.

8. A control system for a three-phase grid-connected inverter, characterized in that: include: Establishing modules for establishing mathematical models of photovoltaic arrays and three-phase grid-connected inverters; A control module, used for controlling the inverter by introducing fractional-order control theory and adopting fractional-order integral sliding mode control; The optimization module is used to optimize the inverter parameters using the sparrow search neural network algorithm.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. 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 according to any one of claims 1 to 7 are implemented.

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