Parameter identification method and device for transient control model of photovoltaic inverter

Abstract

The invention provides a parameter identification method and device for a transient control model of a photovoltaic inverter, and relates to the technical field of control. The method comprises the following steps: constructing a transient control model of the photovoltaic inverter; performing preliminary identification on to-be-identified parameters of each to-be-identified module in the transient control model to obtain initial values of the to-be-identified parameters; for each to-be-identified module, determining a current adaptive value of the to-be-identified module; and under the condition that the current adaptive value of each to-be-identified module is smaller than the corresponding adaptive value threshold, taking the to-be-identified parameter reference value shown by the particle corresponding to the current adaptive value in each module as the to-be-identified parameter true value of each module. According to the scheme provided by the embodiment of the invention, the relevance and the synergistic effect of the parameters between the modules can be simulated for the modularized transient control model, the identification effect of the final parameter set is optimized, the accuracy is relatively high, and the method can adapt to parameter identification under complex working conditions.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of control, and particularly relates to a parameter identification method and device of a photovoltaic inverter transient control model. BACKGROUND

[0002] The parameter identification accuracy of a photovoltaic power generation system determines the reliability of a simulation model, and reflects whether the model can simulate the electrical characteristics of the control system under various transient faults of the power grid. Through parameter identification, the performance parameters of the photovoltaic inverter control system can be accurately understood, such as the maximum allowed access photovoltaic string power on the direct current input side, the rated direct current power, the maximum direct current voltage, and the maximum alternating current power, the maximum alternating current, the power factor and the like on the alternating current output side. These parameters are key indicators for evaluating system performance. Based on these parameters, the system can be optimized and designed to improve the power generation efficiency and stability of the system and reduce the operating cost of the system. In addition, accurate identification of system parameters provides more accurate and comprehensive data support for the power grid dispatching department. This helps the power grid dispatching department to better understand the operating state and power generation capacity of the photovoltaic power generation system, so as to develop reasonable dispatching strategies and ensure the stable operation of the power grid. The parameter identification method in the related art has low accuracy and cannot cope with complex working conditions. SUMMARY

[0003] To overcome the problems in the related art, the present disclosure provides a parameter identification method and device of a photovoltaic inverter transient control model.

[0004] According to a first aspect of an embodiment of the present disclosure, a parameter identification method of a photovoltaic inverter transient control model is provided, comprising: constructing a transient control model of a photovoltaic inverter; preliminarily identifying the to-be-identified parameters of each to-be-identified module in the transient control model to obtain initial values of the to-be-identified parameters; For each to-be-identified module, the to-be-identified parameters in the to-be-identified module are constructed as a plurality of particles, each particle is used to show a group of to-be-identified parameter reference values, and the initial values of at least part of the to-be-identified parameters are taken as the to-be-identified parameter reference values in each particle. Based on the to-be-identified parameter reference values shown by different particles, simulation electrical quantities output based on the to-be-identified module under a target working condition are determined in a simulation environment, and the deviation values between the simulation electrical quantities corresponding to each particle and the real electrical quantities collected in the real environment under the target working condition are taken as the fitness values of each particle, respectively. The smallest fitness value of the fitness values of the particles is taken as the current fitness value of the to-be-identified module. The target working condition is a calling working condition of the to-be-identified module. If the current fitness value of each module to be identified is less than the corresponding fitness threshold, the reference value of the parameter to be identified shown by the particle corresponding to the current fitness value in each module is taken as the true value of the parameter to be identified in each module; otherwise, based on the particle swarm optimization algorithm, the reference value of the parameter to be identified shown by each particle in each module to be identified is updated, and the current fitness value of each module to be identified is re-determined until the current fitness value of each module to be identified is less than the corresponding fitness threshold, and the reference value of the parameter to be identified shown by the particle corresponding to the current fitness value in each module is taken as the true value of the parameter to be identified in each module.

[0005] In some embodiments, before updating the reference values ​​of the identification parameters shown by each particle in each identification module based on the particle swarm optimization algorithm, and re-determining the current fitness value of each identification module, the method further includes: Determine whether the current iteration count is greater than a preset iteration count threshold, where the iteration count is the number of times the current fitness value of each module to be identified is redefined; If so, adjust the control method used by at least some of the modules to be identified to generate simulated electrical quantities, and reset the current iteration count to zero.

[0006] In some embodiments, updating the reference values ​​of the identification parameters shown by each particle in each identification module based on the particle swarm optimization algorithm includes: Based on the particle swarm optimization algorithm, update the inertia factor, velocity, and position of each particle; If the current iteration count is less than or equal to a preset iteration count threshold, update the historical best position of each particle. Based on the updated properties of each particle, the reference values ​​of the parameters to be identified shown by each particle are calculated.

[0007] In some embodiments, the transient control model includes: an active power control module under fault ride-through state, a reactive power control module under fault ride-through state, an active power control module under normal state, a reactive power control module under normal state, an inverter current control and limiting module, and a photovoltaic power generation module. The active power control module under fault ride-through condition is used to determine the reference active power or reference active current of the photovoltaic inverter based on the parameters of the photovoltaic inverter under fault ride-through condition. The reactive power control module under fault ride-through condition is used to determine the reference reactive power or reference reactive current of the photovoltaic inverter based on the parameters of the photovoltaic inverter under fault ride-through condition. The active power control module under normal conditions is used to determine the reference active power of the photovoltaic inverter based on the parameters of the photovoltaic inverter under normal conditions. The reactive power control module in the normal state is configured to determine reference reactive power of the photovoltaic inverter according to parameters of the photovoltaic inverter in the normal state. The inverter current control and limiting module is configured to determine an active current reference value according to the reference active power when the active power control module in the fault ride-through state is configured to determine the reference active power, and / or determine a reactive current reference value according to the reference reactive power when the reactive power control module in the fault ride-through state is configured to determine the reference reactive power. The photovoltaic power generation module is configured to determine target active power and target reactive power of the photovoltaic inverter according to the active current reference value and the reactive current reference value. In some embodiments, the to-be-identified modules include the active power control module in the fault ride-through state, the reactive power control module in the fault ride-through state, the active power control module in the normal state, and the reactive power control module in the normal state.

[0008] In some embodiments, the active power control module in the fault ride-through state includes an active power control module corresponding to a low penetration symmetric fault condition, an active power control module corresponding to a low penetration asymmetric fault condition, an active power control module corresponding to a high penetration symmetric fault condition, and an active power control module corresponding to a high penetration asymmetric fault condition. The reactive power control module in the fault ride-through state includes a reactive power control module corresponding to a low penetration symmetric fault condition, a reactive power control module corresponding to a low penetration asymmetric fault condition, a reactive power control module corresponding to a high penetration symmetric fault condition, and a reactive power control module corresponding to a high penetration asymmetric fault condition.

[0009] According to a second aspect of the embodiments of the present disclosure, a parameter identification device for a transient control model of a photovoltaic inverter is provided, including: A construction unit is configured to construct a transient control model of a photovoltaic inverter. A first identification unit is configured to preliminarily identify to-be-identified parameters of each to-be-identified module in the transient control model to obtain initial values of the to-be-identified parameters. The second identification unit is configured to, for each to-be-identified module, construct to-be-identified parameters in the to-be-identified module as a plurality of particles, each particle being used to show a group of to-be-identified parameter reference values; take initial values of at least part of the to-be-identified parameters as the to-be-identified parameter reference values in each particle; determine simulation electrical quantities output based on the to-be-identified module under a target working condition in a simulation environment based on the to-be-identified parameter reference values shown by different particles respectively, and take deviation values between the simulation electrical quantities corresponding to each particle and real electrical quantities collected under the target working condition in a real environment as fitness values of each particle respectively; take the smallest fitness value among the fitness values of the particles as a current fitness value of the to-be-identified module; wherein the target working condition is a calling working condition of the to-be-identified module. The judging unit is configured to, in a case where the current fitness value of each to-be-identified module is smaller than a corresponding fitness value threshold, take the to-be-identified parameter reference values shown by the particles corresponding to the current fitness value in each module as real values of the to-be-identified parameters of each module; otherwise, update the to-be-identified parameter reference values shown by the particles in each to-be-identified module based on a particle swarm optimization algorithm, and redetermine the current fitness values of each to-be-identified module until the current fitness value of each to-be-identified module is smaller than a corresponding fitness value threshold, and take the to-be-identified parameter reference values shown by the particles corresponding to the current fitness value in each module as real values of the to-be-identified parameters of each module.

[0010] In some embodiments, the judging unit is further configured to: determine whether the current iteration number is greater than a preset iteration number threshold, the iteration number being a number of times of redetermining the current fitness values of each to-be-identified module; if yes, adjust a control mode used by at least part of the to-be-identified modules to generate the simulation electrical quantities, and clear the current iteration number.

[0011] In some embodiments, the judging unit is further configured to: update inertia factors, speeds and positions of the particles based on the particle swarm optimization algorithm; in a case where the current iteration number is smaller than or equal to the preset iteration number threshold, update historical best positions of the particles; calculate the to-be-identified parameter reference values shown by the particles based on the updated properties of the particles.

[0012] In some embodiments, the transient control model comprises: an active power control module in a fault ride-through state, a reactive power control module in the fault ride-through state, an active power control module in a normal state, a reactive power control module in the normal state, an inverter current control and limiting module, and a photovoltaic power generation module. The active power control module in the fault ride-through state is configured to determine reference active power or reference active current of the photovoltaic inverter according to parameters of the photovoltaic inverter in the fault ride-through state. The reactive power control module in the fault ride-through state is configured to determine reference reactive power or reference reactive current of the photovoltaic inverter according to parameters of the photovoltaic inverter in the fault ride-through state. The active power control module in the normal state is configured to determine reference active power of the photovoltaic inverter according to parameters of the photovoltaic inverter in the normal state. The reactive power control module in the normal state is configured to determine reference reactive power of the photovoltaic inverter according to parameters of the photovoltaic inverter in the normal state. The inverter current control and limiting module is configured to determine active current reference according to reference active power when the active power control module in the fault ride-through state is configured to determine reference active power, and / or determine reactive current reference according to reference reactive power when the reactive power control module in the fault ride-through state is configured to determine reference reactive power. The photovoltaic power generation module is configured to determine target active power and target reactive power of the photovoltaic inverter according to the active current reference and the reactive current reference. The to-be-identified module at least includes the active power control module in the fault ride-through state, the reactive power control module in the fault ride-through state, the active power control module in the normal state, and the reactive power control module in the normal state.

[0013] In some embodiments, the active power control module in the fault ride-through state includes an active power control module corresponding to a low-penetration symmetric fault condition, an active power control module corresponding to a low-penetration asymmetric fault condition, an active power control module corresponding to a high-penetration symmetric fault condition, and an active power control module corresponding to a high-penetration asymmetric fault condition. The reactive power control module in the fault ride-through state includes a reactive power control module corresponding to a low-penetration symmetric fault condition, a reactive power control module corresponding to a low-penetration asymmetric fault condition, a reactive power control module corresponding to a high-penetration symmetric fault condition, and a reactive power control module corresponding to a high-penetration asymmetric fault condition.

[0014] The scheme provided by the embodiments of the present disclosure can simulate the correlation and synergistic effect of parameters between modules for a modular transient control model, optimize the identification effect of the final parameter set, has high accuracy, and can adapt to parameter identification under complex conditions. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 Fig. 1 shows a flow diagram of a parameter identification method of a transient control model of a photovoltaic inverter according to an embodiment of the present disclosure.

[0016] Figure 2 Fig. 2 shows an architecture diagram of a transient control model of a photovoltaic inverter according to an embodiment of the present disclosure.

[0017] Figure 3 Fig. 3 shows a time period division diagram of a fault ride-through process according to an embodiment of the present disclosure.

[0018] Figure 4 Fig. 4 shows a diagram of an active power control module in a fault ride-through state according to an embodiment of the present disclosure.

[0019] Figure 5 Fig. 5 shows a diagram of a reactive power control module in a fault ride-through state according to an embodiment of the present disclosure.

[0020] Figure 6 Fig. 6 shows a diagram of an active power control module in a normal state according to an embodiment of the present disclosure.

[0021] Figure 7 Fig. 7 shows a diagram of a reactive power control module in a normal state according to an embodiment of the present disclosure.

[0022] Figure 8 Fig. 8 shows a diagram of a division of transient and steady state intervals according to an embodiment of the present disclosure.

[0023] Figure 9 Fig. 9 shows a complete flow diagram of a parameter identification method according to an embodiment of the present disclosure.

[0024] Figure 10 Fig. 10 shows a principle diagram of a particle swarm optimization algorithm according to an embodiment of the present disclosure.

[0025] Figure 11 Fig. 11 shows a principle diagram of a parameter identification optimization strategy according to an embodiment of the present disclosure.

[0026] Figure 12 Fig. 12 shows a structure diagram of a parameter identification device of a transient control model of a photovoltaic inverter according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] The exemplary embodiments will be described in detail herein below with reference to the drawings. The following description is related to the drawings when the drawings are referred to, and the same numerals in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all the embodiments consistent with the present disclosure. Rather, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0028] The method can be executed by an electronic device with computing capability.

[0029] Figure 1 A flowchart of a method for identifying parameters of a photovoltaic inverter transient control model is shown in the embodiments of the present disclosure. Figure 1 As shown in the figure, the method for identifying parameters of a photovoltaic inverter transient control model provided in the embodiments of the present disclosure includes the following steps.

[0030] S101, a transient control model of a photovoltaic inverter is constructed.

[0031] S102, the initial values of the to-be-identified parameters of each to-be-identified module in the transient control model are identified to obtain the initial values of the to-be-identified parameters.

[0032] S103, for each to-be-identified module, the to-be-identified parameters in the to-be-identified module are constructed as a plurality of particles, each particle being used to show a set of to-be-identified parameter reference values; the initial values of at least part of the to-be-identified parameters are taken as the to-be-identified parameter reference values in each particle; based on the to-be-identified parameter reference values shown by different particles, simulation electrical quantities output based on the to-be-identified module under a target working condition are determined in a simulation environment, and the deviation values between the simulation electrical quantities corresponding to each particle and the real electrical quantities collected under the target working condition in a real environment are respectively taken as the fitness values of each particle; the smallest fitness value among the fitness values of each particle is taken as the current fitness value of the to-be-identified module; wherein the target working condition is a calling working condition of the to-be-identified module.

[0033] S104, in the case where the current fitness values of each to-be-identified module are all less than the corresponding fitness value thresholds, the to-be-identified parameter reference values shown by the particles corresponding to the current fitness values in each module are taken as the real values of the to-be-identified parameters of each module; otherwise, based on a particle swarm optimization algorithm, the to-be-identified parameter reference values shown by each particle in each to-be-identified module are updated, and the current fitness values of each to-be-identified module are re-determined until the current fitness values of each to-be-identified module are all less than the corresponding fitness value thresholds, and the to-be-identified parameter reference values shown by the particles corresponding to the current fitness values in each module are taken as the real values of the to-be-identified parameters of each module.

[0034] In some embodiments, the transient control model includes: an active power control module in a fault ride-through state, a reactive power control module in a fault ride-through state, an active power control module in a normal state, a reactive power control module in a normal state, an inverter current control and limiting module, and a photovoltaic power generation module. an active power control module in the fault ride-through state, configured to determine a reference active power or a reference active current of the photovoltaic inverter according to parameters of the photovoltaic inverter in the fault ride-through state; an active power control module in the fault ride-through state, configured to determine a reference active power or a reference active current of the photovoltaic inverter according to parameters of the photovoltaic inverter in the fault ride-through state; an active power control module in the normal state, configured to determine a reference active power of the photovoltaic inverter according to parameters of the photovoltaic inverter in the normal state; an active power control module in the normal state, configured to determine a reference active power of the photovoltaic inverter according to parameters of the photovoltaic inverter in the normal state; an inverter current control and limiting module, configured to determine an active current reference value according to the reference active power when the active power control module in the fault ride-through state is configured to determine the reference active power, and / or determine a reactive current reference value according to the reference reactive power when the reactive power control module in the fault ride-through state is configured to determine the reference reactive power; a photovoltaic power generation module, configured to determine a target active power and a target reactive power of the photovoltaic inverter according to the active current reference value and the reactive current reference value; In the embodiment, the to-be-identified module includes at least the active power control module in the fault ride-through state, the reactive power control module in the fault ride-through state, the active power control module in the normal state, and the reactive power control module in the normal state.

[0035] For better understanding, first of all, the exemplary architecture of the transient control model in the embodiment will be introduced. Figure 2 It can be understood that the transient control model can be used to simulate the control strategy of the photovoltaic inverter in the transient process, especially the behavior during the fault ride-through.

[0036] Specifically, as shown in Figure 2 the transient control model in the embodiment includes a fault ride-through state judgment module (WEV), an active power control module in the normal state (WEU), a reactive power control module in the normal state (WEQ), an active power control module in the fault ride-through state (WLP), a reactive power control module in the fault ride-through state (WLQ), an inverter current control and limiting module (WES), a photovoltaic power generation module (PSL), and a frequency and inertia control module (WAF&WAI).

[0037] The fault ride-through state judgment module (WEV) can obtain the voltage of the power grid and determine whether the power grid is currently in a fault state according to the voltage of the power grid, so as to determine whether to use the control strategy in the normal state or the control strategy in the fault state.

[0038] The active power control module (WLP) in the fault ride-through state is used to execute the corresponding active power control strategy when the power grid is in the fault ride-through state.

[0039] The reactive power control module (WLQ) in the fault ride-through state is used to execute the corresponding reactive power control strategy when the power grid is in the fault ride-through state.

[0040] The active power control module (WEU) in the normal state is used to execute the corresponding active power control strategy when the power grid is in the normal state.

[0041] The reactive power control module (WEQ) in the normal state is used to execute the corresponding reactive power control strategy when the power grid is in the normal state.

[0042] The inverter current control and limiting module (WES) is used to calculate the corresponding active current and reactive current reference values according to the power reference values and and limit the current to ensure that the inverter operates within a safe range.

[0043] The photovoltaic power generation module (PSL) is used to simulate the output characteristics of the photovoltaic unit to generate target active power and target reactive power according to the input current reference values.

[0044] The frequency and inertia control module (WAF&WAI) is used to perform frequency regulation and inertia response, which can generate power adjustment signals and superimpose the power adjustment signals on the active power control module to support the frequency of the power grid.

[0045] Next, the active power control module in the fault ride-through state, the reactive power control module in the fault ride-through state, the active power control module in the normal state, and the reactive power control module in the normal state participating in identification will be described in detail.

[0046] Please refer to Figure 3 The fault ride-through process of the photovoltaic inverter can be divided into three periods, namely the fault ride-through period, the fault recovery starting point, and the fault recovery process. The three periods correspond to A, B, and C in Figure 3 respectively, and different control strategies can be designed according to their characteristics. Specifically, the active power control module in the fault ride-through state and the first reactive power control model can be designed in the following manner.

[0047] Figure 4 A schematic diagram of the active power control module in the fault ride-through state in the embodiment of the present disclosure is shown. InFigure 4 FLG1 corresponds to the control mode during fault ride-through: 0 for no control; 1 for the control mode based on specified active power; 2 for the control mode based on specified active current; 3 for the control mode based on initial (before fault) active current. FLG2 corresponds to the control mode at the start of fault recovery: 0 for no control; 1 for the control mode based on specified active current; 2 for the control mode based on active current during fault; 3 for the control mode based on active power during fault. FLG3 corresponds to the control mode during fault recovery process: 0 for no control; 1 for the control mode based on specified recovery slope; 2 for the control mode based on inertial curve.

[0048] Specifically, the active power control module in the fault ride-through state can be constructed to include three stages of fault ride-through period, start of fault recovery, and fault recovery process.

[0049] When the photovoltaic inverter is in the fault ride-through period, the active power control module in the fault ride-through state is any one of the following: corresponding to the control mode based on specified active power, for generating a reference active power; wherein, is the reference active power, is an active power correction coefficient, is an initial active power, is a specified active power.

[0050] corresponding to the control mode based on specified active current, for generating a reference active current; wherein, is the reference active current, is a voltage correction coefficient, is an end voltage amplitude, is a current correction coefficient, is an initial active current, is a specified active current.

[0051] corresponding to the control mode based on initial active current, for generating a reference active current; wherein, is the reference active current, is an initial active current.

[0052] When the photovoltaic inverter is in the start of fault recovery, the active power control module in the fault ride-through state is any one of the following: corresponding to the control mode based on specified active current, for generating a reference active current; wherein, is a reference active current, is a current correction coefficient, is an initial active current, is a specified active current.

[0053] corresponding to a control mode based on the active current during the fault, for generating a reference active current; wherein, is a reference active current, is an active current during the fault.

[0054] corresponding to a control mode based on the active power during the fault, for generating a reference active power; wherein, is a reference active power, is an active power during the fault.

[0055] In the case that the photovoltaic inverter is in a fault recovery process, the active power control module in the fault ride-through state is any one of: corresponding to a control mode based on a specified recovery slope, for generating a reference active power; wherein, is a reference active power, is a coefficient, is an initial active power, is an active power during the fault.

[0056] corresponding to a control mode based on an inertial curve, for generating a reference active power; wherein, is a reference active power, is an initial active power, is an inertial time constant, is a complex variable.

[0057] Figure 5 A schematic diagram of a reactive power control module in the fault ride-through state in the embodiments of the present disclosure is shown. In Figure 5 , FLG1 corresponds to a control mode during the fault ride-through: when 0, no control; when 1, a control mode based on a specified reactive power is adopted; when 2, a control mode based on a specified reactive current is adopted. FLG2 corresponds to a control mode at the start of fault recovery: when 0, no control; when 1, a control mode based on a specified reactive current is adopted; when 2, a control mode based on an initial (before the fault occurs) reactive current is adopted; when 3, a control mode based on an active power during the fault is adopted. FLG3 corresponds to a control mode during the fault recovery process: when 0, no control; when 1, a control mode based on an inertial curve is adopted.

[0058] Specifically, the reactive power control module in the fault ride-through state can be constructed to include three stages of a fault ride-through period, a fault recovery start point, and a fault recovery process.

[0059] In a case where the photovoltaic inverter is in the fault ride-through period, the reactive power control module in the fault ride-through state is any one of: corresponding to the control mode based on the specified reactive power, for generating a reference reactive power; wherein, is the reference reactive power, is a reactive power correction coefficient, is an initial reactive power, is the specified reactive power.

[0060] corresponding to the control mode based on the specified reactive current, for generating a reference reactive current; wherein, is the reference reactive current, is a voltage correction coefficient, is a specified voltage, is an end voltage amplitude, is a current correction coefficient, is an initial reactive current, is the specified reactive current.

[0061] In a case where the photovoltaic inverter is in the fault recovery start point, the reactive power control module in the fault ride-through state is any one of: corresponding to the control mode based on the specified reactive current, for generating a reference reactive current; wherein, is the reference reactive current, is a power correction coefficient, is an initial reactive current, is the specified reactive current.

[0062] corresponding to the control mode based on the initial reactive current, for generating a reference reactive current; wherein, is the reference reactive current, is the initial reactive current.

[0063] corresponding to the control mode based on the reactive power during the fault, for generating a reference reactive current; wherein, is the reference reactive power, is the reactive power during the fault.

[0064] In a case where the photovoltaic inverter is in the fault recovery process, the reactive power control module in the fault ride-through state is any one of: corresponding to the inertia curve-based control mode, for generating a reference reactive power; wherein, is the reference reactive power, is the initial reactive power, is an inertia time constant, is a complex variable.

[0065] Exemplarily, Figure 6 a schematic diagram of the active power control module in the normal state in the embodiment of the present disclosure is shown. As Figure 6 shown, the active power control module in the normal state can be constructed in the following ways: ; wherein, is the reference active power, is the actual maximum power point active power, is a complex variable, is a time constant.

[0066] In addition, in Figure 6 , is the maximum power limit, is the minimum power limit, is the maximum change rate, is the minimum change rate.

[0067] By approximating the maximum power point active power as the active power command in the normal operating state, sampling the data with a time delay element, and taking numerical amplitude limiting and change rate limiting for control, the active power control model can be simplified to the greatest extent.

[0068] Exemplarily, Figure 7 a schematic diagram of the reactive power control module in the normal state in the embodiment of the present disclosure is shown. As Figure 7 shown, the reactive power control module in the normal state can be constructed in any of the following ways: corresponding to the constant power control mode; wherein, is the reference reactive power, is the reactive power control command.

[0069] corresponding to the constant voltage control mode; wherein, is the reference reactive power, is a proportional element coefficient, is a voltage measurement time constant, is a reference voltage, is a complex variable, is a target control voltage, is a reactive current, is a line impedance, is an integral coefficient, is a time constant.

[0070] corresponding to a constant power factor control mode; wherein, is a reference reactive power, is a target active power, is a reference power factor, is a complex variable, is a time constant.

[0071] In addition, in Figure 7 , is a maximum limit of reactive power, is a minimum limit of reactive power.

[0072] In this way, a perfect transient control model can be established, which provides a basis for accurately fitting the photovoltaic control process based on the identified parameters.

[0073] It can be understood that the to-be-identified parameters can include the coefficients and time constants involved in each of the above to-be-identified models.

[0074] In some embodiments, when performing S102 above, the to-be-identified parameters can be preliminarily identified by a least square method. Specifically, small perturbation, large perturbation and power control tests can be designed according to the control logic of each to-be-identified module to test the parameters, for preliminary identification of the parameters. Then, based on the least square estimation method of the parameter test input and output, the to-be-identified parameter vector is converged to a unique parameter set through multiple iterations, and the initial values of the final to-be-identified parameters are obtained.

[0075] In some embodiments, when performing S103 above, the current fitness value of the to-be-identified module can be determined in the following manner.

[0076] First, the fitness value of the particle can be represented by the deviation value between the simulation electrical quantity output by the to-be-identified module when the to-be-identified parameter reference value of the to-be-identified parameter is shown by the to-be-identified parameter and the real electrical quantity collected under the same working condition in the real environment. That is, the fitness value of the particle can be evaluated by calculating the deviation between the simulation electrical quantity and the real electrical quantity. The electrical quantities involved can include: voltage U s active current I p reactive current I q active power P reactive power Q .

[0077] Exemplarily, the deviation of each period transient and steady state interval under the target working condition can be calculated, wherein the average deviation is only calculated in the transient interval of each period, the average deviation and the maximum deviation are respectively calculated in the steady state interval, and finally the weighted average total deviation under the target working condition is taken as the fitness value of the particle.

[0078] Specifically, the calculation method of the deviation is as follows: (1) Average deviation of steady (transient) state interval F A(B) : (1) In the formula: K M,start , K M,end respectively represent the serial numbers of the first and last data measured in the real environment in the corresponding interval; K S,start , K S,end respectively represent the serial numbers of the first and last data obtained in the simulation environment in the corresponding interval; X M and X S respectively represent the unit values of the test and simulation data of the electrical quantity.

[0079] (2) Maximum deviation of steady state interval F C : (2) (3) Weighted average total error F G : (3) In the formula: F A , F B , F C respectively represent the average deviation of the test and simulation data of the A, B and C periods.

[0080] Exemplarily, please refer to Figure 8 , the determination method of the transient and steady state interval is as follows: Since the accuracy requirements of the transient and steady state interval are different, the electrical quantity data sequence can be divided into three periods of A (before disturbance), B (during disturbance) and C (after disturbance); according to the response characteristics of active power and reactive power, the B and C periods are divided into transient interval and steady state interval, wherein the B period is divided into B1 (transient) and B2 (steady) intervals, and the C period is divided into C1 (transient) and C2 (steady) intervals.

[0081] (1) A, B, C period determination Based on the test voltage data, the test and simulation data sequences are divided into three periods: a) A - before disturbance; b) B - during disturbance; c) C - after disturbance.

[0082] Each period is divided into transient and steady state intervals according to the characteristics of active and reactive power test data during voltage sag, as shown in Figure 8 The start and end time of A, B, C period determination method is as follows: a) 2s before voltage sag as A period start; b) 20ms before voltage sag to 0.9p.u. as A period end, B period start; c) 20ms before disturbance clearance start as B period end, C period start; d) 2s after disturbance clearance, photovoltaic power generation unit / system active power starts to stabilize output as C period end.

[0083] (2) Transient and steady state interval determination a) A period A period is the time interval before disturbance, which is all steady state interval, as shown in A interval Figure 8 of b) B period B period is divided into transient and steady state intervals. Voltage transient sag, power regulation stage is transient interval as Figure 8 B1 period in Figure 8 ; voltage sag stable operation is steady state interval, as shown in B2 period

[0084] c) C period C period is divided into transient and steady state intervals. Voltage transient recovery, power regulation stage is transient interval, as Figure 8 C1 period in Figure 8 ; recovery stable operation stage is steady state interval, as shown in C2 period (3) B, C period transient interval and steady state interval determination The B and C periods are divided into transient intervals and steady-state intervals according to the response characteristics of the current, active power and reactive power. The transient interval is the fluctuation interval of the current, active power and reactive power caused by the voltage instantaneous large fluctuation. The steady-state interval is the interval of normal operation and stable operation after voltage fluctuation. The transient start time is the end time of the previous steady state, and the transient end time is the start time of the next steady state. For the transient interval caused by voltage fluctuation, the last 20 ms in which the power and current fluctuations enter the ±10% range of the average value of the interval is the end of the transient process.

[0085] Please refer to Figure 9 , Figure 9 The complete flowchart of the parameter identification method in the embodiment of the present disclosure is shown. As shown in Figure 9 Before updating the reference value of the to-be-identified parameter shown by each particle in each to-be-identified module and re-determining the current fitness value of each to-be-identified module based on the particle swarm optimization algorithm, the method further comprises: determining whether the current iteration number is greater than a preset iteration number threshold, the iteration number being the number of times of re-determining the current fitness value of each to-be-identified module; If yes, adjusting the control mode of at least part of the to-be-identified modules for generating the simulation electrical quantity, and clearing the current iteration number.

[0086] Please continue to refer to Figure 9 updating the reference value of the to-be-identified parameter shown by each particle in each to-be-identified module based on the particle swarm optimization algorithm, comprising: updating the inertia factor, speed and position of each particle based on the particle swarm optimization algorithm; in the case where the current iteration number is less than or equal to the preset iteration number threshold, updating the historical best position of each particle; calculating the reference value of the to-be-identified parameter shown by each particle based on the updated properties of each particle.

[0087] Next, the particle swarm optimization algorithm in the embodiment of the present disclosure will be described in detail. In fact, the embodiment of the present disclosure combines the advantages of the cell genetic algorithm (CGA) and the particle swarm optimization (PSO) algorithm. By introducing cell structure and particle swarm intelligence, more efficient global search and local optimization are achieved in the parameter identification process. In CGA PSO, the population is divided into multiple cells, and the particles in each cell perform local search through the PSO algorithm, while the cells exchange information and cooperate through genetic operations. This hybrid strategy enables CGA PSO to converge to the optimal solution faster while maintaining population diversity.

[0088] PSO (Particle Swarm Optimization) is inspired by flocks of birds foraging for food. Each bird is unaware of the location of food in a wide area, but can perceive its own distance from the food. Therefore, the most efficient foraging strategy is to concentrate exploration in the area where the birds closest to the food are located. Each bird can share information, thus working collaboratively to evaluate and transmit location information of the optimal foraging location. Ultimately, the flock will converge near the food source, achieving a collective discovery of the optimal solution. Based on this concept, PSO views the parameter optimization problem as a process of particle swarm optimization finding the optimal set of parameter solutions: each particle obtains a fitness value according to the optimization objective function. F Based on this value, the particle adjusts its speed, determines its direction of movement and step size in space, and the best-performing particle shares its position with other particles, allowing other particles to track its position. The particle swarm then performs further optimization in the solution space.

[0089] In the initialization phase of PSO, a certain number of randomly distributed particles are generated, representing initial candidate solutions to the problem. Each particle is a potential representative, and its fitness value defines its quality in the space. Subsequently, the optimal solution is gradually approximated through iteration. During the iteration process, each particle identifies its best position (denoted as P). best ) and the optimal location found by all particles in the swarm (denoted as G) best Each particle updates its velocity and position using this information. Thus, PSO can simulate swarm intelligence, utilizing information sharing between individual particles within the swarm to find the optimal solution in a complex search space. The PSO optimization process is as follows: Figure 10 As shown.

[0090] For example, suppose Z i = ( Z i1 , Z i2 , ..., Z ij ),for j The 3rd dimension vector graph i Each particle, by calculating its current fitness value F Pbest Assess the positional differences between different particles; v i = ( v i1 , v i2 , ..., v ij ) is a particle i of j 3D velocity vector, P besti =(P i1 P i2 , ..., P ij P represents the local optimum of the particle found.Gi =(P G1 P G2 , ..., P Gj The local optimum is the global optimum found by the swarm search. After n iterations, the fitness values ​​of the local optimum and the global optimum are compared. F in and F Pbest By continuously solving the problem alternately, after the nth iteration, the particle... i The speed and position are (4) In the formula, c 1 and c 2 represents the particle's learning factor, primarily reflecting the particle's ability to learn from its optimal position. For the first i The position of each particle in the search space, and rand() is a random number used to increase the randomness of the search.

[0091] Considering the broad optimization space, and to avoid getting trapped in local minima during the solution process, particles need to possess both individual and collective cognition. Therefore, a dynamic adjustment of inertia weights is introduced to balance global and local searches. The inertia weights decrease linearly with the number of iterations. This strategy encourages broader exploration in the early stages of the algorithm, gradually guiding the particles closer to the optimal solution as iterations progress. The improved PSO particle... i The rate and position algorithm is (5) In the formula ω As an inertia factor, its role is to balance the system's search space and search speed. On the one hand, a smaller... ω It allows for searching within a specified area, improving search efficiency; on the other hand, it can also handle larger... ω This can increase the search space and avoid getting stuck in local optima and exiting the iteration. Therefore, ω The value is obtained using an iterative method, and the iterative process is as follows: (6) In the formula, ω max , ω min They are respectively ω The upper and lower limits; k This represents the number of iterations. k max This represents the maximum number of iterations.

[0092] The power control model (WLP & WLQ) in the fault ride-through state established in the embodiments of the present disclosure has multiple fitting methods to choose from, and the simulation effect of the model is achieved through the cooperation of each module. Therefore, the CGAPSO algorithm is introduced, and the information strategy sharing between each optimization group is added to the traditional PSO algorithm, the information is exchanged between each group, and the fitting method of the model is determined together.

[0093] Please refer to Figure 11 and combine Figure 9 The CGAPSO algorithm is applied to the embodiments of the present disclosure, and the to-be-identified module in the transient analysis model can be regarded as a cell, and the to-be-identified parameter set in each module can be regarded as a particle. The test results under different working conditions can be regarded as the optimization reference materials, and the test results under each working condition can be regarded as the constraint conditions of the parameter set. The control parameters in each module share information and cooperate with each other, and then the optimization direction and speed of each parameter are determined. The fitness function of the particle is established according to the evaluation system of the parameter model, and multiple iterations are performed to determine the optimal parameter set. In addition, the preliminary identification result of the to-be-identified parameter can be used as part of the initial value of the particle, which can further accelerate the convergence rate of the algorithm and prevent the particle swarm from falling into local optimum.

[0094] The power control module (WEQ, WEU, WLP, WLQ) in the transient control model is regarded as a "cell" in the CGAPSO algorithm, that is, an identification group, and the parameters in each module are regarded as a particle group. The parameter set obtained by preliminary identification is used as the initial value of the particle group optimization. Each iteration brings the iteration result into the format model and performs transient simulation test under various working conditions. The average weighted error is calculated according to the parameter test result and the actual test result, which is used as the fitness value of the particle position at this time. The speed and direction of the next iteration of the particle are determined according to the information exchange with other models, and the inertia factor is updated. According to the same optimization process, the iteration is continuously performed until the fitness value of the particle is less than the set value, and the iteration is ended.

[0095] Among them, the information sharing and cooperation between modules can be understood as a voting mechanism. For the parameter identification result of any iteration round, each model votes according to its current fitness value, and the models that meet the fitness value threshold condition vote "1" and the models that do not meet the fitness value threshold condition vote "0". If the voting result of a round is "1", the parameter set meets the requirements (the deviation meets the requirements), and if the voting result of a round is "0", the iteration continues. If the iteration round exceeds the required round, it is considered that the fitting method of the model is no longer suitable for the actual working condition, and the fitting method is switched (which can be regarded as the decision of the cells).

[0096] Based on the same inventive concept, this disclosure also provides a parameter identification device for a photovoltaic inverter transient control model, as shown in the following embodiment. Since the principle of this photovoltaic inverter transient control model parameter identification device embodiment in solving the problem is the same as that described above... Figure 1 The method embodiments shown are similar, therefore the implementation of the parameter identification device embodiment for the photovoltaic inverter transient control model can be found above. Figure 1 The implementation of the method embodiments shown will not be repeated here.

[0097] Figure 12 This diagram illustrates the structure of a parameter identification device for a transient control model of a photovoltaic inverter, as shown in an embodiment of this disclosure. Figure 12 As shown, the parameter identification device 1200 for the transient control model of the photovoltaic inverter includes: Building unit 1201 is used to build a transient control model for a photovoltaic inverter; The first identification unit 1202 is used to perform preliminary identification of the parameters to be identified in each module to be identified in the transient control model, and obtain the initial values ​​of the parameters to be identified. The second identification unit 1203 is used to construct multiple particles for the parameters to be identified in each module to be identified, with each particle representing a set of reference values ​​for the parameters to be identified; to use the initial values ​​of at least some of the parameters to be identified as reference values ​​for the parameters to be identified in each particle; to determine the simulated electrical quantity output by the module to be identified under the target operating condition in the simulation environment based on the reference values ​​of the parameters to be identified shown by different particles, and to use the deviation between the simulated electrical quantity corresponding to each particle and the real electrical quantity collected under the target operating condition in the real environment as the fitness value of each particle; and to use the minimum fitness value among the fitness values ​​of each particle as the current fitness value of the module to be identified; wherein, the target operating condition is the calling operating condition of the module to be identified. The judgment unit 1204 is used to, when the current fitness value of each module to be identified is less than the corresponding fitness value threshold, take the reference value of the parameter to be identified shown by the particle corresponding to the current fitness value in each module as the true value of the parameter to be identified in each module; otherwise, based on the particle swarm optimization algorithm, update the reference value of the parameter to be identified shown by each particle in each module to be identified, and redetermine the current fitness value of each module to be identified, until the current fitness value of each module to be identified is less than the corresponding fitness value threshold, and take the reference value of the parameter to be identified shown by the particle corresponding to the current fitness value in each module as the true value of the parameter to be identified in each module.

[0098] In some embodiments, the determining unit 1204 is further configured to: Determine whether the current iteration count is greater than the preset iteration count threshold. The iteration count is the number of times the current fitness value of each module to be identified is redefined. If yes, adjust the control mode of at least part of the to-be-identified module for generating the simulation electrical quantity, and clear the current iteration number.

[0099] In some embodiments, the determining unit 1204 is further configured to: update the inertia factor, the speed, and the position of each particle based on a particle swarm optimization algorithm; in a case where the current iteration number is less than or equal to a preset iteration number threshold, update the historical best position of each particle; calculate the to-be-identified parameter reference value shown by each particle based on the updated attributes of each particle.

[0100] In some embodiments, the transient control model comprises: an active power control module in a fault ride-through state, a reactive power control module in the fault ride-through state, an active power control module in a normal state, a reactive power control module in the normal state, an inverter current control and limiting module, and a photovoltaic power generation module. The active power control module in the fault ride-through state is configured to determine a reference active power or a reference active current of the photovoltaic inverter according to parameters of the photovoltaic inverter in the fault ride-through state. The reactive power control module in the fault ride-through state is configured to determine a reference reactive power or a reference reactive current of the photovoltaic inverter according to parameters of the photovoltaic inverter in the fault ride-through state. The active power control module in the normal state is configured to determine a reference active power of the photovoltaic inverter according to parameters of the photovoltaic inverter in the normal state. The reactive power control module in the normal state is configured to determine a reference reactive power of the photovoltaic inverter according to parameters of the photovoltaic inverter in the normal state. The inverter current control and limiting module is configured to determine an active current reference value according to the reference active power in a case where the active power control module in the fault ride-through state is configured to determine the reference active power, and / or determine a reactive current reference value according to the reference reactive power in a case where the reactive power control module in the fault ride-through state is configured to determine the reference reactive power. The photovoltaic power generation module is configured to determine a target active power and a target reactive power of the photovoltaic inverter according to the active current reference value and the reactive current reference value. In some embodiments, the to-be-identified module comprises at least: the active power control module in the fault ride-through state, the reactive power control module in the fault ride-through state, the active power control module in the normal state, and the reactive power control module in the normal state.

[0101] In some embodiments, the active power control module in the fault ride-through state includes: an active power control module corresponding to a low penetration symmetric fault condition, an active power control module corresponding to a low penetration asymmetric fault condition, an active power control module corresponding to a high penetration symmetric fault condition, and an active power control module corresponding to a high penetration asymmetric fault condition; The reactive power control module in the fault ride-through state includes: a reactive power control module corresponding to a low penetration symmetric fault condition, a reactive power control module corresponding to a low penetration asymmetric fault condition, a reactive power control module corresponding to a high penetration symmetric fault condition, and a reactive power control module corresponding to a high penetration asymmetric fault condition.

[0102] Other embodiments of the disclosure will be apparent to those of ordinary skill in the art from a consideration of the specification and practice of the disclosure as disclosed herein. It is intended that the disclosure encompass any and all variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such modifications and features that are apparent to those skilled in the art or desired by those of ordinary skill in the art. The specification and examples given are intended as illustrative only and are not intended to limit the true scope and spirit of the disclosure.

Claims

1. A parameter identification method of a photovoltaic inverter transient control model, characterized in that, The method comprises the following steps: constructing a transient control model of a photovoltaic inverter; preliminarily identifying to-be-identified parameters in each to-be-identified module in the transient control model to obtain initial values of the to-be-identified parameters; for each to-be-identified module, constructing the to-be-identified parameters in the to-be-identified module into a plurality of particles, each particle being used to show a set of to-be-identified parameter reference values; and taking the initial values of at least part of the to-be-identified parameters as the to-be-identified parameter reference values in each particle; based on the to-be-identified parameter reference values shown by different particles respectively, determining simulation electrical quantities output based on the to-be-identified module under a target working condition in a simulation environment, and taking the deviation values between the simulation electrical quantities corresponding to each particle and real electrical quantities collected under the target working condition in a real environment as the fitness values of each particle respectively; taking the smallest fitness value among the fitness values of all the particles as the current fitness value of the to-be-identified module; wherein the target working condition is a calling working condition of the to-be-identified module; in the case that the current fitness value of each to-be-identified module is smaller than a corresponding fitness value threshold, taking the to-be-identified parameter reference values shown by the particles corresponding to the current fitness values in all the modules as the real values of the to-be-identified parameters of all the modules; otherwise, based on a particle swarm optimization algorithm, updating the to-be-identified parameter reference values shown by all the particles in each to-be-identified module, and re-determining the current fitness values of all the to-be-identified modules until the current fitness value of each to-be-identified module is smaller than a corresponding fitness value threshold, and taking the to-be-identified parameter reference values shown by the particles corresponding to the current fitness values in all the modules as the real values of the to-be-identified parameters of all the modules.

2. The method of claim 1, wherein, Before the step of updating the to-be-identified parameter reference values shown by all the particles in each to-be-identified module based on the particle swarm optimization algorithm and re-determining the current fitness values of all the to-be-identified modules, the method further comprises the following steps: judging whether a current iteration number is greater than a preset iteration number threshold, the iteration number being the number of times of re-determining the current fitness values of all the to-be-identified modules; if yes, adjusting a control mode used for generating simulation electrical quantities of at least part of the to-be-identified modules, and clearing the current iteration number.

3. The method of claim 2, wherein, The step of updating the to-be-identified parameter reference values shown by all the particles in each to-be-identified module based on the particle swarm optimization algorithm comprises the following steps: based on the particle swarm optimization algorithm, updating inertia factors, speeds and positions of all the particles; in the case that the current iteration number is smaller than or equal to the preset iteration number threshold, updating historical best positions of all the particles; based on the updated properties of all the particles, calculating the to-be-identified parameter reference values shown by all the particles.

4. The method of claim 1, wherein, The transient control model comprises an active power control module in a fault ride-through state, a reactive power control module in the fault ride-through state, an active power control module in a normal state, a reactive power control module in the normal state, an inverter current control and limiting module and a photovoltaic power generation module; the active power control module in the fault ride-through state is configured to determine a reference active power or a reference active current of the photovoltaic inverter according to parameters of the photovoltaic inverter in the fault ride-through state. The reactive power control module in the fault ride-through state is configured to determine reference reactive power or reference reactive current of the photovoltaic inverter according to parameters of the photovoltaic inverter in the fault ride-through state; The active power control module in the normal state is configured to determine reference active power of the photovoltaic inverter according to parameters of the photovoltaic inverter in the normal state; The reactive power control module in the normal state is configured to determine reference reactive power of the photovoltaic inverter according to parameters of the photovoltaic inverter in the normal state; The inverter current control and limiting module is configured to determine active current reference according to reference active power when the active power control module in the fault ride-through state is configured to determine reference active power, and / or determine reactive current reference according to reference reactive power when the reactive power control module in the fault ride-through state is configured to determine reference reactive power; The photovoltaic power generation module is configured to determine target active power and target reactive power of the photovoltaic inverter according to the active current reference and the reactive current reference. The to-be-identified modules at least include the active power control module in the fault ride-through state, the reactive power control module in the fault ride-through state, the active power control module in the normal state, and the reactive power control module in the normal state.

5. The method of claim 4, wherein, The active power control module in the fault ride-through state includes an active power control module corresponding to a low-penetration symmetric fault condition, an active power control module corresponding to a low-penetration asymmetric fault condition, an active power control module corresponding to a high-penetration symmetric fault condition, and an active power control module corresponding to a high-penetration asymmetric fault condition. The reactive power control module in the fault ride-through state includes a reactive power control module corresponding to a low-penetration symmetric fault condition, a reactive power control module corresponding to a low-penetration asymmetric fault condition, a reactive power control module corresponding to a high-penetration symmetric fault condition, and a reactive power control module corresponding to a high-penetration asymmetric fault condition.

6. A device for parameter identification of a photovoltaic inverter transient control model, characterized in that The method comprises: The construction unit is configured to construct a transient control model of the photovoltaic inverter; The first identification unit is configured to preliminarily identify to-be-identified parameters of each to-be-identified module in the transient control model to obtain initial values of the to-be-identified parameters; The second identification unit is configured to, for each to-be-identified module, construct to-be-identified parameters in the to-be-identified module into a plurality of particles, each particle being used to show a set of to-be-identified parameter reference values, and use initial values of at least part of the to-be-identified parameters as the to-be-identified parameter reference values in each particle; Based on the to-be-identified parameter reference values shown by different particles, simulation electrical quantities output based on the to-be-identified module in a target condition are determined in a simulation environment, and a deviation value between the simulation electrical quantity corresponding to each particle and a real electrical quantity collected in the target condition in a real environment is taken as an adaptation value of each particle; the smallest adaptation value among the adaptation values of the particles is taken as a current adaptation value of the to-be-identified module; wherein the target condition is a calling condition of the to-be-identified module; and The judgment unit is further configured to:

7. The apparatus of claim 6, wherein, determine whether the current iteration number is greater than a preset iteration number threshold, the iteration number being the number of times of redetermination of the current fitness value of each to-be-identified module; if yes, adjust the control mode of at least part of the to-be-identified modules for generating the simulation electrical quantity, and clear the current iteration number. The judgment unit is further configured to:

8. The apparatus of claim 7, wherein, update the inertia factor, the speed and the position of each particle based on the particle swarm optimization algorithm; update the historical best position of each particle in the case that the current iteration number is less than or equal to the preset iteration number threshold; calculate the to-be-identified parameter reference value shown by each particle based on the updated attributes of each particle. The transient control model comprises: an active power control module in a fault ride-through state, a reactive power control module in the fault ride-through state, an active power control module in a normal state, a reactive power control module in the normal state, an inverter current control and limiting module, and a photovoltaic power generation module; 9. The apparatus of claim 6, wherein, The active power control module in the fault ride-through state is configured to determine reference active power or reference active current of the photovoltaic inverter according to parameters of the photovoltaic inverter in the fault ride-through state; The reactive power control module in the fault ride-through state is configured to determine reference reactive power or reference reactive current of the photovoltaic inverter according to parameters of the photovoltaic inverter in the fault ride-through state; The active power control module in the normal state is configured to determine reference active power of the photovoltaic inverter according to parameters of the photovoltaic inverter in the normal state; The reactive power control module in the normal state is configured to determine reference reactive power of the photovoltaic inverter according to parameters of the photovoltaic inverter in the normal state; The inverter current control and limiting module is configured to determine active current reference according to the reference active power in the case that the active power control module in the fault ride-through state is configured to determine the reference active power, and / or determine reactive current reference according to the reference reactive power in the case that the reactive power control module in the fault ride-through state is configured to determine the reference reactive power; The photovoltaic power generation module is configured to determine target active power and target reactive power of the photovoltaic inverter according to the active current reference and the reactive current reference. ​ The to-be-identified module at least includes: an active power control module in a fault ride-through state, a reactive power control module in the fault ride-through state, an active power control module in a normal state, and a reactive power control module in the normal state.

10. The apparatus of claim 9, wherein, The active power control module in the fault ride-through state includes: an active power control module corresponding to a low-penetration symmetric fault condition, an active power control module corresponding to a low-penetration asymmetric fault condition, an active power control module corresponding to a high-penetration symmetric fault condition, and an active power control module corresponding to a high-penetration asymmetric fault condition. The reactive power control module in the fault ride-through state includes: a reactive power control module corresponding to the low-penetration symmetric fault condition, a reactive power control module corresponding to the low-penetration asymmetric fault condition, a reactive power control module corresponding to the high-penetration symmetric fault condition, and a reactive power control module corresponding to the high-penetration asymmetric fault condition.

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