A grid-connected inverter parameter design method

Abstract

This invention discloses a parameter design method for a grid-connected inverter, relating to the field of inverter grid-connected system resonance analysis technology. The method involves: setting the parameter ranges for each part of the grid-connected inverter; setting the frequency variation range [m1, m2]; obtaining the frequency scanning curve between frequencies m1 and m2; obtaining the system resonance characteristic curve by changing the parameters; determining the optimization parameters; setting the characteristic harmonic frequency bands emitted by the inverter and the background grid; setting the optimization objective of the grid-connected inverter; initializing the particle swarm parameters and randomly initializing the position and velocity of each parameter particle of the grid-connected inverter; calculating the objective function value of the grid-connected inverter; exploring the individual optimal objective function value and the global optimal objective function value, and updating the individual optimal position and the global optimal position at any time; determining whether the iteration can end; outputting the optimal solution for each parameter and the optimal objective function value, and outputting the impedance-frequency characteristic curve of the inverter grid-connected system when using the optimal solution.

Description

Technical Field

[0001] This invention relates to the field of inverter grid-connected resonant characteristic analysis technology, and in particular to a method for designing grid-connected inverter parameters. Background Technology

[0002] With the rapid development of new energy power generation in my country, wind power and photovoltaic power generation have gradually become important components of current new energy power generation systems. New energy grid connection often uses inverters as the main interface. However, inverters based on pulse width modulation (PWM) technology output high-order harmonic components near the switching frequency. To suppress these high-order harmonic components at the inverter output, LC or LCL type filters are often required. LCL filters, with their high high-frequency attenuation characteristics, are widely used in new energy grid connection. However, the increasing scale of new energy power generation makes the system structure more complex. Multiple factors, such as LCL filters and changes in grid-side impedance, can interfere with each other, increasing the risk of resonance. Harmonic resonance not only affects the stability of the converter but also the power quality of the grid and even threatens the safe and stable operation of the entire system.

[0003] Therefore, to address the harmonic resonance problem caused by inverter grid connection to the system, it is necessary to study the design of LCL filter devices and inverter control parameters to avoid resonance. Summary of the Invention

[0004] The purpose of this invention is to provide a method for designing grid-connected inverter parameters. This method can not only reveal the resonance characteristics of the grid-connected inverter system, but also provide the optimal solution for the grid-connected inverter parameters through an optimization algorithm, thereby avoiding resonance from the source and solving the problem of high cost caused by setting up filters.

[0005] The objective of this invention is achieved through the following technical solution: a method for designing grid-connected inverter parameters, specifically:

[0006] (1) Based on the factors affecting the resonance of the inverter grid-connected system, establish a model of the inverter grid-connected system and make initial settings for the inverter grid-connected system parameters;

[0007] (2) Under the premise of ensuring the control effect of the grid-connected inverter and the filtering effect of the LCL filter, set the range of control parameters and LCL filter parameters in the grid-connected inverter;

[0008] (3) Set the frequency variation range of the inverter grid-connected system to [m] i [m1, m2], where m1 and m2 represent the minimum and maximum values ​​of the frequency variation range, respectively;

[0009] (4) At the common connection point between the grid-connected inverter and the grid, a frequency scan is performed with the frequency change range [m1, m2] and the frequency change step size Δm to obtain the impedance value of the inverter grid-connected system at each frequency.

[0010] (5) By changing the LCL filter parameters and inverter control parameters, the resonant frequency and impedance amplitude at the resonant point under different parameter values ​​can be obtained.

[0011] (6) Based on the obtained harmonic order and resonance amplitude, obtain the filter parameters and the resonance characteristic curve when the control parameters change;

[0012] (7) Optimize the parameters that have a significant impact on the resonant frequency or impedance amplitude based on the resonant characteristic curve;

[0013] (8) Based on the actual measured harmonic voltage and harmonic current values, determine the characteristic harmonic frequency bands of the grid-connected inverter and the background power grid;

[0014] (9) The particle swarm optimization algorithm is used to determine the optimization target of the grid-connected inverter;

[0015] (10) Set the constraints for the parameters selected in step (7);

[0016] (11) Initialize the position and velocity of each parameter particle of the grid-connected inverter, and initialize the particle swarm parameters of the particle swarm optimization algorithm;

[0017] (12) Calculate the objective function value of the grid-connected inverter;

[0018] (13) Compare the individual objective function value with the individual optimal objective function value and update the individual optimal position and the individual optimal objective function value;

[0019] (14) Determine whether the total number of particles has been reached. If yes, proceed to the next step. If no, return to step (12).

[0020] (15) Update the global optimal position and the global optimal objective function value;

[0021] (16) Determine whether the number of iterations initially set by the particle swarm algorithm has been reached. If yes, proceed to the next step; otherwise, return to step (12).

[0022] (17) Output the optimal solution and the optimal objective function value, and output the impedance frequency characteristic curve of the inverter grid-connected system when the optimal solution is used.

[0023] The establishment of the inverter grid-connected system model includes establishing a grid-connected inverter model and a grid equivalent model. Among them, the grid-connected inverter model based on the LCL filter device needs to consider the influencing factors such as the switching time of power electronic devices, harmonic output characteristics and inverter control strategy.

[0024] The set range of LCL filter parameters and grid-connected inverter control parameters includes the value range of converter-side inductor A [L... A1 L A2 ], where L A1 L A2 These represent the minimum and maximum values ​​of inductor A, respectively; the range of values ​​for the grid-side inductor B is [L]. B1 L B2 ], where L B1 L B2 These represent the minimum and maximum values ​​of the grid-side inductor B, respectively; the value range of the capacitor C in the filter branch is [C1, C2], where C1 and C2 represent the minimum and maximum values ​​of the filter capacitor C, respectively; the current controller uses a proportional resonant controller, and the proportional and resonant control parameters are set, with the proportional parameter ranging from [K...]. P1 K P2 ], where K P1 K P2 These represent the minimum and maximum values ​​within the range of the proportional parameter; the range of the resonant parameter [K]. R1 K R2 ], where K R1 K R2 These represent the minimum and maximum values ​​within the range of the resonant parameters, respectively.

[0025] The optimization objectives of the grid-connected inverter are twofold: first, to achieve harmonic mitigation by ensuring that the resonant point of the inverter's grid-connected system avoids the characteristic harmonic frequency bands generated by the inverter and the background power grid; and second, to minimize the cost of the LCL filter in the grid-connected inverter.

[0026] The constraint condition for the optimized parameters is that the value of inductor A is greater than the value of inductor B to ensure the filtering effect.

[0027] The position and velocity of each parameter particle in the grid-connected inverter are represented by X. id V idThe position represents the value of the grid-connected inverter optimization parameter, and the speed represents the change value of each optimization parameter of the grid-connected inverter. Here, d = 1, 2, ..., D represents the optimization parameter number; i = 1, 2, ..., N represents the particle number. The particle swarm parameters for initializing the particle swarm optimization algorithm include the total number of particles N, the total number of iterations M in the optimization process, the number of grid-connected inverter optimization parameters D, the weight coefficient ω in the particle swarm algorithm, and the learning coefficients c1 and c2.

[0028] The calculation of the objective function value of the grid-connected inverter includes the individual optimal objective function value f of the i-th particle. id and the population optimal objective function value f d Where d = 1, 2, ..., D; i = 1, 2, ..., N, that is, whether the frequency at which the inverter grid-connected system resonates deviates from the characteristic harmonic frequency band of the inverter and the background grid, and whether the cost of the LCL filter in the inverter has been minimized.

[0029] The individual's optimal position P id and the global optimal position P d Let represent the optimal solution of the i-th particle in the d-th parameter of the grid-connected inverter and the global optimal solution of the d-th parameter, respectively.

[0030] Compared with the prior art, the beneficial effects of the present invention are:

[0031] I. This invention can reveal the resonant characteristics of inverter grid-connected systems.

[0032] Second, this invention can change the controller parameters to avoid system resonance based on the grid connection status of the inverter (number of inverters connected to the grid, power supply operation mode, etc.).

[0033] Third, this invention can realize the design of parameters of each part of the grid-connected inverter. Under the premise of meeting the filtering requirements and providing power to the power system with good waveform control, it can not only avoid system resonance, but also minimize the cost of the inverter.

[0034] Fourth, this invention can avoid resonance from the source without the need for additional filtering design. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the grid-connected inverter parameter design method described in Embodiment 1 of the present invention. Detailed Implementation

[0036] To better understand the inventive concept of this invention, its working principle is briefly described below: The goal is to achieve a low-cost in-phase compensation device that avoids the characteristic harmonic frequency bands emitted by the inverter and the background power grid at the inverter-grid-connected system's resonant point, while meeting filtering requirements and ensuring normal power supply to the grid. The proposed particle swarm resonance characteristic optimization algorithm reveals the resonant characteristics of the inverter-grid-connected system and provides the optimal solution for the grid-connected inverter parameters, thus avoiding resonance at its source and solving the problem of high costs associated with setting up filters. The invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0037] Example 1 Figure 1 As shown:

[0038] A method for designing parameters of a grid-connected inverter includes the following steps:

[0039] (1) Based on the factors affecting the resonance of the inverter grid-connected system, establish a model of the inverter grid-connected system and make initial settings for the inverter grid-connected system parameters, including establishing a grid-connected inverter model and a grid equivalent model. Among them, the grid-connected inverter model based on LCL filter device needs to consider the influencing factors such as the switching time of power electronic devices, harmonic output characteristics and inverter control strategy.

[0040] (2) Under the premise of ensuring the control effect of the grid-connected inverter and the filtering effect of the LCL filter, set the range of control parameters and LCL filter parameters in the grid-connected inverter, including the value range of the inverter-side inductor A [L A1 L A2 ], where L A1 L A2 These represent the minimum and maximum values ​​of inductor A, respectively; the range of values ​​for the grid-side inductor B is [L]. B1 L B2 ], where L B1 LB2 represents the minimum and maximum values ​​of the grid-side inductor B, respectively; the value range of capacitor C in the filter branch is [C1, C2], where C1 and C2 represent the minimum and maximum values ​​of the filter capacitor C, respectively; L A1 L B 1. The value of C1 is determined by the filtering effect, L A2 L B2 The value of C2 is determined by the power factor of the inverter grid-connected system; the current controller adopts a proportional resonant controller, and the proportional and resonant control parameters are set, with the proportional parameter range [K]. P1 K P2 ], where K P1 K P2 These represent the minimum and maximum values ​​within the range of the proportional parameter; the range of the resonant parameter [K].R1 K R2 ], where K R1 K R2 These represent the minimum and maximum values ​​within the range of the resonant parameter, respectively; K P1 K P2 K R1 K R2 The value is determined by the actual control effect of the controller.

[0041] (3) Set the frequency variation range of the inverter grid-connected system to [m1, m2], where m1 and m2 represent the minimum and maximum values ​​of the frequency variation range, respectively;

[0042] (4) At the common connection point between the grid-connected inverter and the grid, a frequency scan is performed with the frequency change range [m1, m2] and the frequency change step size Δm to obtain the impedance value of the inverter grid-connected system at each frequency.

[0043] (5) By changing the LCL filter parameters and inverter control parameters, the resonant frequency and impedance amplitude at the resonant point under different parameter values ​​can be obtained.

[0044] (6) Based on the obtained harmonic order and resonance amplitude, obtain the filter parameters and the resonance characteristic curve when the control parameters change;

[0045] (7) Optimize the parameters that have a significant impact on the resonant frequency or impedance amplitude based on the resonant characteristic curve;

[0046] (8) Based on the actual measured harmonic voltage and harmonic current values, determine the characteristic harmonic frequency bands of the grid-connected inverter and the background power grid;

[0047] (9) The particle swarm optimization algorithm is used to determine the optimization objectives of the grid-connected inverter. The optimization objectives are: firstly, to avoid the characteristic harmonic frequency band generated by the inverter and the background power grid at the resonant point of the inverter grid-connected system to achieve the purpose of harmonic control; and secondly, to minimize the cost of the LCL filter in the grid-connected inverter.

[0048] (10) Set constraints on the parameters selected in step (7), including ensuring that the value of inductor A is greater than the value of inductor B to ensure the filtering effect;

[0049] (11) Initialize the position and velocity of each parameter particle of the grid-connected inverter, and initialize the particle swarm optimization algorithm's particle swarm parameters, including the position and velocity of each parameter particle of the grid-connected inverter, denoted as X. id V id Where position represents the value of the grid-connected inverter's optimization parameters, and speed represents the change in the value of each optimization parameter of the grid-connected inverter. The speed update formula is: The position update formula is Where d = 1, 2, ... D represents the optimization parameter number; i = 1, 2, ..., N represents the particle number; m = 1, 2, ..., M represents the iteration number; ω is the weight coefficient in the particle swarm optimization algorithm; c1 and c2 are the learning coefficients; rand1() and rand2() are random numbers in the interval [0, 1], respectively.

[0050] (12) Calculate the objective function value of the grid-connected inverter, including the individual optimal objective function value f of the i-th particle. id and the population optimal objective function value f d Where d = 1, 2, ... D; i = 1, 2, ..., N, that is, whether the frequency at which the inverter grid-connected system resonates deviates from the characteristic harmonic frequency band emitted by the inverter and the background grid, and whether the cost of the LCL filter in the inverter has been minimized;

[0051] (13) Compare the individual objective function value with the individual optimal objective function value and update the individual optimal position P. id and the individual optimal objective function value f id ;

[0052] (14) Determine whether the total number of particles has been reached. If yes, proceed to the next step. If no, return to step (12).

[0053] (15) Update the global optimal position and the global optimal objective function value;

[0054] (16) Determine whether the number of iterations initially set by the particle swarm algorithm has been reached. If yes, proceed to the next step; otherwise, return to step (12).

[0055] (17) Output the optimal solution P d and the optimal objective function value f d It also outputs the impedance-frequency characteristic curve of the inverter grid-connected system when the optimal solution is adopted.

Claims

1. A method for designing parameters of a grid-connected inverter, specifically including the following steps: (1) Based on the factors affecting the resonance of the inverter grid-connected system, establish a model of the inverter grid-connected system and make initial settings for the inverter grid-connected system parameters; (2) On the premise of ensuring the control effect of the grid-connected inverter and the filtering effect of the LCL filter, set the range of control parameters and LCL filter parameters in the grid-connected inverter; (3) Set the frequency variation range of the inverter grid-connected system to [ m 1, m 2], where, m 1 and m 2 represents the minimum and maximum values ​​of the frequency variation range, respectively; (4) At the point of common connection between the grid-connected inverter and the power grid, the frequency varies [ m 1, m 2] for range, Frequency scanning is performed with a frequency variation step size to obtain the impedance value of the inverter grid-connected system at each frequency; (5) By changing the LCL filter parameters and inverter control parameters, the resonant frequency and impedance amplitude at the resonant point under different parameter values ​​can be obtained; (6) Based on the obtained harmonic order and resonance amplitude, obtain the filter parameters and the resonance characteristic curve when the control parameters change; (7) Optimize the parameters that have a significant impact on the resonant frequency or impedance amplitude based on the resonant characteristic curve; (8) Based on the actual measured harmonic voltage and harmonic current values, determine the characteristic harmonic frequency bands of the grid-connected inverter and the background power grid; (9) The particle swarm optimization algorithm is used to determine the optimization objective of the grid-connected inverter; (10) Set the constraints for the parameters selected in step (7); (11) Initialize the position and velocity of each parameter particle of the grid-connected inverter, and initialize the particle swarm parameters of the particle swarm optimization algorithm; (12) Calculate the objective function value of the grid-connected inverter; (13) Compare the individual objective function value with the individual optimal objective function value and update the individual optimal position and the individual optimal objective function value; (14) Determine whether the total number of particles has been reached. If yes, proceed to the next step. If no, return to step (12). (15) Update the global optimal position and the global optimal objective function value; (16) Determine whether the number of iterations initially set by the particle swarm algorithm has been reached. If yes, proceed to the next step; otherwise, return to step (12). (17) Output the optimal solution and the optimal objective function value, and output the impedance frequency characteristic curve of the inverter grid-connected system when the optimal solution is adopted.

2. The method of claim 1, wherein: The establishment of the inverter grid-connected system model includes establishing a grid-connected inverter model and a grid equivalent model. Among them, the grid-connected inverter model based on the LCL filter device needs to consider the influencing factors such as the switching time of power electronic devices, harmonic output characteristics and inverter control strategy.

3. The method of claim 1, wherein: The range of control parameters and LCL filter parameters in the grid-connected inverter, as described above, includes the range of values ​​for the inverter-side inductor A. L A1 , L A2 ],in L A1 , L A2 These represent the minimum and maximum values ​​of inductor A, respectively; the range of values ​​for the grid-side inductor B... L B1 , L B2 ],in L B1 ， L B2 These represent the minimum and maximum values ​​of the inductor B on the grid side, respectively; the capacitor in the filter branch... C The range of values ​​for is [ C 1, C 2], of which C 1 ，C 2 represents the filter capacitors respectively. C The minimum and maximum values ​​within the range of values; the current controller uses a proportional resonant controller, and the proportional and resonant control parameters are set, with the proportional parameter range being [...]. K P1 , K P2 ],in K P1 , K P2 These represent the minimum and maximum values ​​within the range of the proportional parameter; the range of the resonant parameter [ K R1 , K R2 ],in K R1 , K R2 These represent the minimum and maximum values ​​within the range of the resonant parameters, respectively.

4. The method of claim 1, wherein: The optimization objectives of the grid-connected inverter are twofold: first, to achieve harmonic mitigation by ensuring that the resonant point of the inverter's grid-connected system avoids the characteristic harmonic frequency bands generated by the inverter and the background power grid; and second, to minimize the cost of the LCL filter in the grid-connected inverter.

5. The grid-connected inverter parameter design method according to claim 3, characterized in that: The constraint for optimizing the parameters is that the value of inductor A is greater than the value of inductor B to ensure the filtering effect.

6. The method of claim 1, wherein: The position and velocity of each parameter particle in the grid-connected inverter are respectively expressed as: , Where position represents the value of the grid-connected inverter's optimization parameters, and speed represents the change in the value of each optimization parameter of the grid-connected inverter. d =1, 2,… D Indicates the optimization parameter number; i =1, 2,…, N The particle number is indicated; the particle swarm optimization algorithm's initialization parameters include the total number of particles in the swarm. Total number of iterations during the optimization process Number of optimization parameters for grid-connected inverters Weight coefficients in particle swarm optimization algorithm Learning coefficient , .

7. The method of claim 1, wherein: The calculation of the objective function value of the grid-connected inverter includes the first... i The individual optimal objective function value of each particle and the group optimal objective function value ,in d =1, 2,… D ; i =1, 2,…, N This refers to whether the frequency at which the inverter resonates with the grid deviates from the characteristic harmonic frequency bands emitted by the inverter and the background grid, and whether the cost of the LCL filter in the inverter has been minimized.

8. The grid-connected inverter parameter design method according to claim 1, characterized in that: The individual's optimal position and global optimal position These represent the first and second generations of the grid-connected inverter. d The parameter is the first one i The optimal solution for the nth particle and the nth d The global optimal solution for each parameter.

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