Inverter control mode and parameter identification method, system, device and medium

Abstract

The present invention discloses a method, system, device and medium for identifying inverter control modes and parameters, and relates to the technical field of inverter parameter identification. The method includes: obtaining measurement data of a photovoltaic inverter in different control links; the control links include: a fault ride-through link, a fault ride-through recovery link, a current limiting link and a steady-state control link; according to the measurement data, identifying the control mode and parameters of the photovoltaic inverter in different control links to obtain identification results; the identification results are used to model and simulate the photovoltaic inverter to determine the operating characteristics of the photovoltaic inverter. The present invention can take into account the influence of different control modes on inverter parameter identification and controller characteristic analysis, effectively identify the control mode and related parameters of different control links of the photovoltaic inverter, and provide a model basis for photovoltaic inverter modeling and power system simulation analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of inverter parameter identification, and in particular to an inverter control mode and parameter identification method, system, device and medium. Background Art

[0002] In recent years, the proportion of renewable energy represented by photovoltaics in the power system has gradually increased. By the end of 2021, China's photovoltaic power generation installed capacity reached 306 million kilowatts, accounting for 13.8% of the country's total installed power generation capacity. Due to the current photovoltaic power stations' lack of support for the system and poor stability, they bring a series of challenges to the safety and stability of the new power system with new energy as the main body. Photovoltaic inverter is one of the core devices of photovoltaic power generation system. Clarifying the inverter control model and parameters is the basis for photovoltaic power station modeling analysis and power system simulation analysis. However, multiple control links of photovoltaic inverters can adopt multiple control modes. The control modes adopted by inverters of different manufacturers and models are different, which brings great troubles to the modeling of photovoltaic inverters and the analysis of photovoltaic inverter characteristics. To this end, it is urgent to study the control mode and parameter identification method of photovoltaic inverters to provide a reference for the modeling and simulation of new energy power systems.

[0003] Existing photovoltaic inverter parameter identification methods mostly focus on identifying the control parameters of photovoltaic inverters when the control mode is known. However, both the control mode and control parameters of the inverter will directly affect the characteristics of the photovoltaic inverter. Existing photovoltaic inverter parameter identification methods do not take into account the influence of the control mode, and ignore the important influence of different control modes on parameter identification and controller characteristic analysis. Summary of the invention

[0004] The purpose of the present invention is to provide a method, system, device and medium for identifying inverter control modes and parameters, so as to consider the influence of different control modes on inverter parameter identification and controller characteristic analysis, effectively identify the control modes and related parameters of different control links of photovoltaic inverters, and provide a model basis for photovoltaic inverter modeling and power system simulation analysis.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for identifying inverter control mode and parameters, comprising:

[0007] Obtain the measurement data of the photovoltaic inverter in different control links; the control links include: fault ride-through link, fault ride-through recovery link, current limiting link and steady-state control link; the measurement data of the fault ride-through link include: active power, reactive power, active current, reactive current and terminal voltage; the measurement data of the fault ride-through recovery link include: fault ride-through end time and active current recovery curve; the measurement data of the current limiting link include: reactive current and total current; the measurement data of the steady-state control link include: station active power command, station reactive power command, inverter output active power, inverter output reactive power, inverter terminal voltage, power factor reference value and reactive power reference value;

[0008] According to the measured data, the control modes and parameters of the photovoltaic inverter in different control links are identified to obtain identification results; the control modes of the fault ride-through link include: no additional control, specified power control, specified current control and control according to the current before ride-through; the control modes of the fault ride-through recovery link include: no additional control, fixed slope control and control according to the inertia constant; the control modes of the steady-state control link include: open-loop control, PI control and reactive / voltage coordinated control; the parameters of the current limiting link include: total current upper limit and reactive current upper limit; the identification results are used to model and simulate the photovoltaic inverter to determine the operating characteristics of the photovoltaic inverter.

[0009] Optionally, the identification result includes: a first identification result, a second identification result, a third identification result and a fourth identification result; according to the measurement data, the control modes and parameters of different control links are identified to obtain the identification results, which specifically include:

[0010] According to the measured data of the fault ride-through link, a control mode and parameters of the fault ride-through link are identified by using a least squares-based multivariate linear regression to obtain a first identification result;

[0011] Identify the control mode and parameters of the fault ride-through recovery link according to the measurement data of the fault ride-through recovery link to obtain a second identification result;

[0012] Identify the parameters of the current limiting link according to the measurement data of the current limiting link to obtain a third identification result;

[0013] According to the measured data of the steady-state control link, an adaptive particle swarm algorithm is used to identify the control mode and parameters of the steady-state control link to obtain a fourth identification result.

[0014] An inverter control mode and parameter identification system, comprising:

[0015] The measurement data acquisition module is used to obtain the measurement data of the photovoltaic inverter in different control links; the control links include: fault ride-through link, fault ride-through recovery link, current limiting link and steady-state control link; the measurement data of the fault ride-through link include: active power, reactive power, active current, reactive current and terminal voltage; the measurement data of the fault ride-through recovery link include: fault ride-through end time and active current recovery curve; the measurement data of the current limiting link include: reactive current and total current; the measurement data of the steady-state control link include: station active power command, station reactive power command, inverter output active power, inverter output reactive power, inverter terminal voltage, power factor reference value and reactive power reference value;

[0016] A control mode and parameter identification module is used to identify the control mode and parameters of the photovoltaic inverter in different control links according to the measured data to obtain an identification result; the control modes of the fault ride-through link include: no additional control, specified power control, specified current control and control according to the current before ride-through; the control modes of the fault ride-through recovery link include: no additional control, fixed slope control and control according to the inertia constant; the control modes of the steady-state control link include: open-loop control, PI control and reactive / voltage coordinated control; the parameters of the current limiting link include: total current upper limit and reactive current upper limit; the identification result is used to model and simulate the photovoltaic inverter to determine the operating characteristics of the photovoltaic inverter.

[0017] An electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above-mentioned inverter control mode and parameter identification method.

[0018] A computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the above-mentioned inverter control mode and parameter identification method.

[0019] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0020] The inverter control mode and parameter identification method provided by the present invention obtains measurement data of the photovoltaic inverter in different control links, and identifies the control mode and parameters of the photovoltaic inverter in different control links according to the measurement data to obtain identification results. It can take into account the influence of different control modes while identifying the inverter parameters, and determine the mode that the inverter adopts most among multiple control modes, and finally obtain the control mode and parameters of the inverter fault ride-through link, fault ride-through recovery link, current limiting link and steady-state control link, so as to provide a model basis for modeling and simulation analysis of photovoltaic inverters and power systems, and provide data support for operation characteristic analysis of photovoltaic inverters and power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 A flow chart of the inverter control mode and parameter identification method provided by the present invention;

[0023] Figure 2 The active power control block diagram of the photovoltaic inverter provided by the present invention;

[0024] Figure 3 The reactive power control block diagram of the photovoltaic inverter provided by the present invention;

[0025] Figure 4 A schematic diagram of the steady-state operation and voltage ride-through state switching provided by the present invention;

[0026] Figure 5 A flow chart of the low voltage ride through active control mode and parameter identification provided by the present invention;

[0027] Figure 6 A flow chart of the low voltage ride through reactive power control mode and parameter identification provided by the present invention;

[0028] Figure 7 A flow chart of the PSO algorithm including parameter boundary value adjustment provided by the present invention;

[0029] Figure 8 A flow chart of parameter identification of active control link in steady-state operation provided by the present invention;

[0030] Fig. 9 The present invention provides a flow chart for identifying parameters of a reactive power control link in steady-state operation. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] The purpose of the present invention is to provide a method, system, device and medium for identifying inverter control modes and parameters, so as to consider the influence of different control modes on inverter parameter identification and controller characteristic analysis, effectively identify the control modes and related parameters of different control links of photovoltaic inverters, and provide a model basis for photovoltaic inverter modeling and power system simulation analysis.

[0033] Specifically, most existing solutions only identify the control parameters of photovoltaic inverters, without considering the different control modes existing in their control links, that is, they assume that the data to be identified is obtained under a specific control mode, and it is difficult to obtain accurate identification results for data obtained under other different control modes. Due to the mismatch in control mode selection, the identified parameters may have large deviations when applied to photovoltaic inverter modeling and simulation analysis. While identifying the control parameters of photovoltaic inverters, the present invention identifies the control mode adopted by them, and realizes the integrated identification of control mode and control parameters. An integrated identification strategy of control mode and control parameters is proposed for the fault ride-through link, fault ride-through recovery link, current limiting link, and steady-state control link, which can provide photovoltaic inverter control mode and parameter support for photovoltaic inverter and power system modeling and simulation.

[0034] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] Embodiment 1

[0036] This embodiment provides a method for identifying inverter control mode and parameters, such as Figure 1 As shown, including:

[0037] Step S1: Acquire the measurement data of the photovoltaic inverter in different control links; the control links include: fault ride-through link, fault ride-through recovery link, current limiting link and steady-state control link; the measurement data of the fault ride-through link include: active power, reactive power, active current, reactive current and machine-end voltage; the measurement data of the fault ride-through recovery link include: fault ride-through end time and active current recovery curve; the measurement data of the current limiting link include: reactive current and total current; the measurement data of the steady-state control link include: site active power command, site reactive power command, inverter output active power, inverter output reactive power, inverter terminal voltage, power factor reference value and reactive power reference value.

[0038] Among them, the measurement data of the above-mentioned different control links are determined based on the electromechanical transient model of the photovoltaic inverter. The electromechanical transient model of the photovoltaic inverter includes: photovoltaic inverter steady-state control model, photovoltaic inverter voltage ride-through control (i.e., fault ride-through) model and current limiting model. The above models are described in detail below.

[0039] 1) Photovoltaic inverter steady-state control model

[0040] The steady-state power control model of the photovoltaic inverter includes active power control and reactive power control. Figure 2 As shown, the control modes include open-loop control (P_Flag=1) and PI control (P_Flag=2).

[0041] (1) Open-loop control: The required measurement values ​​are the station power command Pord and the inverter terminal voltage Vterm. Pord is delayed by T pord Then the power reference value Pref is obtained, and Vterm passes through a time constant of T rv The voltage calculation value is obtained after the first-order inertia link, and the power reference value is divided by the voltage calculation value to obtain the calculated current value. After a time constant of T ip After the first-order inertia link, the active current command Ipcmd' is obtained.

[0042] (2) PI control: The required measurement values ​​are the station power command Pord and the actual inverter output power Pe. The time constant of Pe is T rp After the first-order inertia link, it is compared with Pref and the difference is input into the PI control link, where K p_lp is the proportionality coefficient, K i_lp is the integral coefficient, and the output is the active current command Ipcmd'.

[0043] The inverter reactive power control link includes two parts: reactive power reference value selection and reactive current control mode selection. Its model structure and main control parameters are as follows: Figure 3shown.

[0044] The reactive power reference value selection includes three modes: constant power factor (Qref_flag=0), fixed reactive power (Qref_flag=1) and station reactive power instruction (Qref_flag=2).

[0045] (1) Constant power factor: Calculate the reactive power reference value Qref based on the power factor reference value PFref and the active power Pe_filt after filtering and delay.

[0046] (2) Fixed reactive power: Set the reactive power reference value Qref to the inverter output reactive power Qe_ref, which is obtained by power flow calculation.

[0047] (3) Station reactive power instruction: The station reactive power instruction Qord has a time constant of T qord After the first-order inertia link, the reactive power reference value Qref is obtained.

[0048] The reactive power control modes include open-loop control (Q_flag=1), reactive / voltage coordinated control (Q_flag=2), reactive PI control (Q_flag=3) and constant reactive current control (Q_flag=4).

[0049] (1) Open-loop control: Reactive power reference value Qref divided by Vterm_filt (Vterm after time constant T rv The voltage calculation value is obtained after the first-order inertia link of iq After the first-order inertia link, the active current command Iqcmd' is obtained.

[0050] (2) Reactive power / voltage coordinated control: After the voltage reference value is compared with the voltage measurement value Vterm_filt, it is input into the PI link, where K p_lv is the proportionality coefficient, K i_lv is the integral coefficient, and the output is the reactive current command Iqcmd'. The voltage reference value is the deviation between the power reference value Qref and the measured value Qe_filt through the PI link (K p_lqv is the proportionality coefficient, K i_lqv Qe_filt is the output value of the inverter output reactive power Qe after a time constant of T filt It is obtained after the first-order inertia link.

[0051] (3) Reactive PI control: After comparing Qe_filt with Pref, the difference is input into the PI control link (where K p_lq is the proportionality coefficient, K i_lq is the integral coefficient), and the reactive current command Iqcmd' is output.

[0052] (4) Constant reactive current control: The reactive current command Iqcmd' is set to Iq0, which is the initial reactive current obtained by the power flow calculation.

[0053] 2) PV inverter voltage ride-through control model

[0054] When the grid-connected voltage of the photovoltaic inverter drops to the low voltage crossing threshold V Lin Below, or raised to the high voltage crossing threshold V Hin When the inverter changes its control mode, its current command source is converted from the steady-state operation control link (output active current is Ipcmd', output reactive current is Iqcmd') to the voltage ride-through operation control link (output active current is Iphlvrt, output reactive current is Iqhlvrt). Figure 4 As shown in the figure, the output current is passed through the current limiting model (active current upper and lower limits Ipmax, Ipmin, reactive current upper and lower limits Iqmax, Iqmin, total current upper limit Imax) and the equivalent converter model (time constant is T g The first-order inertia link) is then injected into the grid.

[0055] The low voltage ride-through active power control modes include steady-state control (i.e. no additional control), specified power control, specified current control, and control according to the current before ride-through.

[0056] (1) Steady-state control: No current command switching is performed, and the active and reactive currents remain in normal operation. However, the integral link in the control link is frozen at this time.

[0057] (2) Specified power control: Specify the active power P during low voltage ride-through LVRT , which is divided by the terminal voltage Vt to obtain the active current command Ip LVRT , the calculation formula is:

[0058] P LVRT =K P-LVRT *P0+P set_LV

[0059] Ip LVRT =P LVRT / Vt

[0060] In the formula, K P_LVRT is the low wear active power coefficient, P set_LV is the low-pass active power setting value, and P0 is the initial active power.

[0061] (3) Specified current control: Specify the active current command Ip during low voltage ride-through LVRT , the calculation formula is:

[0062] IpLVRT =K 1-Ip-LV *Vt+K 2-Ip-LV *Ip0+Ip set_LV

[0063] In the formula, K 1_Ip_LV and K 2_Ip_LV All are low-power active current calculation coefficients, Ip set_LV is the low-throw active current setting value, and Ip0 is the initial active current.

[0064] (4) Control according to the current before the ride-through: During the low voltage ride-through period, the current before the ride-through is maintained. This control method can be regarded as the K in the specified current control of (3). 1_Ip_LV Take 0, K 2_Ip_LV Take 1, IP set_LV Special case when taking 0:

[0065] Ip LVRT =Ip0

[0066] Low voltage ride-through reactive control includes three control modes: steady-state control (i.e. no additional control), specified power, and specified current.

[0067] (1) Steady-state control: When the current command is in normal operating state, the reactive current command after the integral link is frozen.

[0068] (2) Specified power control: Specify the reactive power Q during low voltage ride-through LVRT , divided by the terminal voltage Vt to obtain the reactive current command Iq LVRT , the calculation formula is:

[0069] Q LVRT =K Q-LVRT *Q0+Q set_LV

[0070] Iq LVRT =Q LVRT / Vt

[0071] In the formula, K Q_LVRT is the low wear reactive power coefficient, Q set_LV is the low-penetration reactive power setting value, Q0 is the initial reactive power.

[0072] (3) Specified current control: Introducing low voltage ride-through threshold V Lin , at this time the current command is:

[0073] Iq LVRT =K 1-Iq-LV *(V Lin -Vt)+K 2-Iq-LV *Iq0+Iq set_LV

[0074] In the formula, K 1_Iq_LV and K 2_Iq_LV All are low-power reactive current calculation coefficients, Iq set_LV is the low-pass reactive current setting value, and Iq0 is the initial reactive current.

[0075] The active and reactive power control methods of HVRT are the same as those of LVRT. In the specified current control part of the HVRT reactive power control method, the reactive current command is:

[0076] Iq HVRT =K 1-Iq-HV *(V Hin -Vt)+K 2-Iq-HV *Iq0+Iq set_HV

[0077] In the formula, K 1_Iq_HV and K 2_Iq_HV All are high-throughput reactive current calculation coefficients, V Hin is the high voltage crossing threshold, Iq set_HV Iq0 is the high-voltage reactive current setting value, and Iq0 is the initial reactive current.

[0078] 3) Current Limitation Model

[0079] To prevent overcurrent of the photovoltaic inverter, the output current of the steady-state operation and voltage ride-through links must be input into the current limiting link to limit the active single current, reactive current, and total current that exceed the limit. In normal operation, the active current output is prioritized, and in voltage ride-through, the reactive current output is prioritized. The important parameters of the current limiting link include the total current upper limit I max and reactive current upper limit Iq max , considering the current limitation, the active current during voltage ride through is:

[0080]

[0081] Iqcmd=min(Iq max ,Iqhlvrt)

[0082] Where Iq is the reactive current output by the photovoltaic inverter, Iphlvrt and Iqhlvrt are Figure 4 Output value of the medium voltage ride-through link.

[0083] Step S2: According to the measured data, the control modes and parameters of the photovoltaic inverter in different control links are identified to obtain identification results; the control modes of the fault ride-through link include: no additional control, specified power control, specified current control and control according to the current before ride-through; the control modes of the fault ride-through recovery link include: no additional control, fixed slope control and control according to inertia constant; the control modes of the steady-state control link include: open-loop control, PI control and reactive / voltage coordinated control; the parameters of the current limiting link include: total current upper limit and reactive current upper limit; the identification results are used to model and simulate the photovoltaic inverter to determine the operating characteristics of the photovoltaic inverter.

[0084] Furthermore, the identification result includes: a first identification result, a second identification result, a third identification result and a fourth identification result; step S2 specifically includes:

[0085] Step S21: According to the measurement data of the fault ride-through link, a multivariate linear regression based on least squares is used to identify the control mode and parameters of the fault ride-through link to obtain a first identification result.

[0086] Step S21 specifically includes:

[0087] Step S211: determining an available data group according to the measurement data of the fault ride-through link and the parameters of the current limiting link; the available data group is obtained by removing the current limiting data from the measurement data of the fault ride-through link.

[0088] Step S212: According to the available data set and the specified power calculation formula, use least squares-based multivariate linear regression to identify parameters under the specified power control mode, and determine the difference between the power calculation value and the power measurement value according to the parameters under the specified power control mode as the first difference.

[0089] Step S213: according to the available data set and the specified current calculation formula, use the least squares-based multivariate linear regression to identify the parameters under the specified current control mode, and determine the difference between the current calculation value and the current measurement value according to the parameters under the specified current control mode as the second difference.

[0090] Step S214: determine whether the first difference and the second difference are both greater than a set error threshold, and obtain a first determination result.

[0091] Step S215: If the first judgment result is yes, determine that the control mode in the first identification result is no additional control.

[0092] Step S216: If the first judgment result is no, determine whether the first difference is smaller than the second difference to obtain a second judgment result.

[0093] Step S217: If the second judgment result is yes, determine that the control mode in the first identification result is the specified power control, and the parameters in the first identification result are parameters in the specified power control mode.

[0094] Step S218: If the second judgment result is no, determine that the control mode in the first identification result is specified current control or current control before crossing, and the parameters in the first identification result are parameters in the specified current control mode or parameters in the current control mode before crossing.

[0095] The fault ride-through link includes: a fault ride-through active control link and a fault ride-through reactive control link.

[0096] When in the fault ride-through reactive control link: if the second judgment result is no, then determine that the control mode of the first identification result is the specified current control, and the parameters of the first identification result are the parameters under the specified current control mode.

[0097] When in the fault ride-through reactive control link: if the second judgment result is no, then determine whether the parameters in the specified current control mode meet the setting conditions to obtain a third judgment result; the parameters in the specified current control mode include: the first fault ride-through current calculation coefficient, the second fault ride-through current calculation coefficient and the fault ride-through current setting value; the setting condition is: the difference between the first fault ride-through current calculation coefficient and 0 is less than the first setting value, the difference between the second fault ride-through current calculation coefficient and 1 is less than the second setting value, and the difference between the fault ride-through current setting value and 0 is less than the third setting value; if the third judgment result is yes, then determine that the control mode in the first identification result is controlled by the current before ride-through, and the parameters in the first identification result are the parameters in the current control mode before ride-through; if the third judgment result is no, then determine that the control mode in the first identification result is the specified current control, and the parameters in the first identification result are the parameters in the specified current control mode. As a specific implementation, the first setting value, the second setting value and the third setting value are all set to 1e-5.

[0098] Among them, both multivariate linear regression and binary linear regression can use the least squares method to estimate model parameters, and the calculation method is as follows:

[0099] Assume that z is the same as x1,x2,…x n If there is a linear relationship between them, we can get the linear equation:

[0100] z=b0+b1x1+b2x2+…+b n x n

[0101] For k sets of input data:

[0102] Z=[ z (1)z(2)···z(k)] T

[0103]

[0104] θ=[b0 b1…b n ] T

[0105] The above formula can be rewritten into matrix form:

[0106] Z=Xθ

[0107] Assume the parameter estimates are: The estimated output is Then the sum of squares of the differences between the measured value and the estimated value can be expressed as:

[0108]

[0109] when When the minimum is taken, the parameter estimate is considered Closest to the true value θ, according to the extreme value theorem:

[0110]

[0111] After sorting, we can get the solution:

[0112]

[0113] The following is a detailed discussion of the above identification method based on the control mode of the PV inverter voltage ride-through, taking its low voltage ride-through active control mode as an example. The parameters to be identified are shown in Table 1.

[0114] Table 1 Summary of low voltage ride through active power control modes and parameters to be identified

[0115]

[0116]

[0117] Since there are multiple control modes and parameters, while using the least squares-based multivariate linear regression to identify the control parameters, the identification errors of different control modes are calculated to determine the mode and setting parameters used by the model. The identification strategy is as follows: Figure 5 shown.

[0118] (1) Data input: test data of different voltage drops from 0.05 to 0.8 pu and different initial active powers.

[0119] (2) Data processing: Read the rated capacity S n, sampling interval T s , enter the low voltage crossing threshold V Lin .

[0120] (3) Obtain the steady-state values ​​of active current, terminal voltage, and active power before and during the low-voltage run-through period, identify the parameters of the current limiting link, and confirm the available data group.

[0121] (4) According to the specified power calculation formula, multivariate linear regression is performed by the least squares method to identify K p_LVRT , P set_LV , using the identified parameters, the power calculation value P is calculated according to the specified power control mode cal , calculate its difference with the power measurement value P mea The difference e1.

[0122]

[0123]

[0124] Wherein, P0i,i=1,2,…,n is the active power before the i-th group of data enters the low voltage ride-through.

[0125] (5) According to the specified current calculation formula, the same method is used to identify K 1_Ip_LV , K 2_Ip_LV 、Ip set_LV , using the identified parameters, the current calculated value I is calculated according to the specified current control mode cal , calculate its value with the current measurement value I mea The difference e2.

[0126]

[0127]

[0128] Where V t i,i=1,2,…,n is the terminal voltage during the low voltage ride through period of the i-th group of data, and Ip0i,i=1,2,…,n is the active current before the i-th group of data enters the low voltage ride through period.

[0129] (6) The error threshold is selected as 0.1. If e1>0.1 and e2>0.1, it is considered that both control methods are not applicable, and the control method is judged as no additional control (when using simulation data, the error threshold can be selected as 1e-5).

[0130] (7) If e1<0.1 and e1<0.1 are satisfied, the parameter group with the smaller error value is considered to be the control mode and the corresponding parameters.

[0131] (8) If the current control is specified in step (7), it is necessary to determine whether the current value satisfies the active current before entering the crossing. If it satisfies K 1_Ip_LV ≈0, K 2_Ip_LV ≈1, Ip set_LV ≈0, the control mode is the current control before crossing. If the above conditions are not met, the control mode is the conventional specified current control.

[0132] The reactive power control mode and parameter identification method are similar to those of the active power control mode, except that it does not include the identification process of the current control mode before crossing in step (8). The identification process is as follows: Figure 6 As shown in FIG. 1 , the active and reactive control modes and parameter identification methods of high voltage ride through are the same as those of low voltage ride through, so they are not described in detail here.

[0133] Step S22: According to the measured data of the fault ride-through recovery link, identify the control mode and parameters of the fault ride-through recovery link to obtain a second identification result. Among them, the voltage ride-through recovery control mode (i.e., the fault ride-through recovery control mode) includes no additional control, fixed slope, and inertia constant. Reactive current recovery usually selects no additional control, which will not be described here.

[0134] Step S22 specifically includes:

[0135] Step S221: Taking the end time of the fault crossing as the starting point, all data points within the range from the first set time to the second set time on the active current recovery curve are determined as the target data group, and the data points at the first set time on the active current recovery curve are connected with the data points at the second set time to obtain the target straight line.

[0136] Step S222: Calculate the absolute value of the data error between the data points in the target data set and the corresponding data points on the target straight line.

[0137] Step S223: For any target data set, if the absolute value of the data error of more than 95% of the data points is less than or equal to 10% of the corresponding data points on the target straight line, it is determined that the target data set satisfies the fixed slope recovery.

[0138] Step S224: Determine whether more than 90% of the target data groups satisfy the fixed slope recovery condition, and obtain a fourth determination result.

[0139] Step S225: If the fourth judgment result is yes, determine that the control mode in the second identification result is constant slope control, and determine that the parameter in the second identification result is the average value of the slopes of the target straight lines corresponding to all target data groups that meet the fixed slope recovery.

[0140] Step S226: If the fourth judgment result is no, a least squares-based polynomial fitting is used to fit the data points in the target data set to obtain a fitting polynomial corresponding to the target data set.

[0141] Step S227: Calculate the inertial time constant of the data points in the target data set according to the fitting polynomial.

[0142] Step S228: For any target data set, if more than 95% of the inertia time constants are distributed within the range of ±10% of the average value of the inertia time constants, the target time constant corresponding to the target data set is determined to be the average value of the inertia time constants of all data points in the target data set.

[0143] Step S229: Determine whether the difference between the maximum value and the minimum value of all target time constants is less than or equal to 10% of the average value of all target time constants, and obtain a fifth determination result.

[0144] Step S2210: If the fifth judgment result is yes, determine that the control mode in the second identification result is control according to the inertia constant, and determine that the parameter in the second identification result is an average value of all target time constants.

[0145] Step S2211: If the fifth judgment result is no, determining that the control mode in the second identification result is no additional control.

[0146] As a specific implementation method, the active current recovery control mode and parameter identification strategy are as follows:

[0147] (1) Taking the end of fault ride-through as the starting point, use straight line L to connect the two data points 20ms and 120ms after the end of the fault on the active current recovery curve to obtain the slope d of straight line L. L .

[0148] (2) Calculate the Ip(i) of each data point within 20ms to 120ms after the fault ends and the corresponding time value Ip on the straight line L L The absolute value of the error Δ(i) of (i).

[0149] (3) If more than 95% of the data error absolute values ​​Δ(i) do not exceed 10% of the corresponding data point Ip(i), then the data set is determined to meet the fixed slope recovery requirements.

[0150] (4) For all n data sets, if more than 90% of the data sets meet the fixed slope recovery requirement, it is determined that the fault recovery control mode at this time is fixed slope control, and the fixed slope parameter is the d of each data set that meets the fixed slope recovery requirement. L average value.

[0151] (5) If the constant slope control is not satisfied, a polynomial fitting based on least squares is used to obtain a smooth curve. The data from 20 ms to 120 ms after the fault ends is fitted to obtain a fifth-order polynomial.

[0152] (6) Calculate the inertial time constant T of each data point using the discretized data in the fifth-order polynomial. If 95% of the T distribution is within ±10% of the average value, the target time constant of the data set can be determined as the average value T of T. mean .

[0153] (7) If all target time constants T mean If the difference between the maximum and minimum values ​​of does not exceed 10% of the average value, it is determined that the control mode at this time is controlled by the inertia curve (that is, controlled by the inertia constant), and the inertia time constant is the target time constant T of each group. mean Otherwise, it is judged as no additional control.

[0154] Step S23: identifying the parameters of the current limiting link according to the measurement data of the current limiting link to obtain a third identification result.

[0155] Step S23 specifically includes:

[0156] Step S231: convert the measured data of the current limiting link into per-unit values ​​to obtain a test data set.

[0157] Step S232: Determine whether there is a group in the test data group whose maximum reactive current is greater than 1.05 pu, and obtain a sixth determination result.

[0158] Step S233: If the sixth judgment result is yes, all groups whose maximum reactive current is within the range of the maximum value of the maximum reactive current and 0.01pu below the maximum value of the maximum reactive current are determined as the first current limiting groups, and the upper limit of the reactive current in the third identification result is determined as the average value of the maximum reactive current of all first current limiting groups.

[0159] Step S234: If the sixth judgment result is no, the upper limit of reactive current in the third identification result is determined to be 1.1 pu.

[0160] Step S235: Determine whether there is a group in the test data group whose maximum total current is greater than 1.1 pu, and obtain a seventh determination result.

[0161] Step S236: If the seventh judgment result is yes, all groups whose maximum total current is within the range of the maximum value of the maximum total current and 0.01pu below the maximum value of the maximum total current are determined as second current limiting groups, and the total current upper limit in the third identification result is determined as the average value of the maximum total current of all second current limiting groups.

[0162] Step S237: If the seventh determination result is no, the total current upper limit in the third identification result is determined to be 1.2 pu.

[0163] As a specific implementation method, based on the current limiting model and considering typical parameters of the current limiting link, the following method is used to identify the parameters of the current limiting link:

[0164] (1) Test data of input power factor 0.8-1 and voltage drop 0.05-0.8pu.

[0165] (2) If there is a group with a maximum reactive current value exceeding 1.05pu, the maximum value and the group within 0.01pu below it are determined to be current limited. This part of the data group is removed in further parameter identification, and the current limit value is the data group average. Otherwise, Iq is determined max =1.1.

[0166] (3) If there is a group with a maximum total current value exceeding 1.1 pu, the maximum value and the group within 0.01 pu below it are determined to be current limited. This part of the data group is removed in further parameter identification, and the current limit value is the data group average. Otherwise, it is determined that I max =1.2.

[0167] Step S24: According to the measurement data of the steady-state control link, an adaptive particle swarm algorithm is used to identify the control mode and parameters of the steady-state control link to obtain a fourth identification result.

[0168] Step S24 specifically includes:

[0169] Step S241: dividing the measured data of the steady-state control link into a calculation data group and a verification data group according to a set ratio.

[0170] Step S242: According to the calculation data group, the adaptive particle swarm algorithm is used to identify the parameters of each control mode of the steady-state control link. Among them, for any control mode, when the adaptive particle swarm algorithm is used to identify its parameters: first, the adaptive particle swarm algorithm is used to identify all parameters in the control mode; secondly, several calculation data groups with smaller objective function fitness values ​​are selected to obtain the average values ​​of each parameter respectively, and the values ​​of the control link parameters are determined; then the adaptive particle swarm algorithm is used to identify the measurement value time constant in the control mode; finally, several calculation data groups with smaller objective function fitness values ​​are selected to obtain the average values ​​of each measurement value time constant, and the values ​​of each measurement value time constant are determined. The parameters in each control mode of the steady-state control link include control link parameters and measurement value time constants.

[0171] Step S243: Calculate the error value under each control mode according to the verification data set and the parameters under each control mode.

[0172] Step S244: comparing the error values ​​under the control modes, and determining the control mode in the fourth identification result as the control mode with the smallest error value, and determining the parameters in the fourth identification result as the parameters under the control mode with the smallest error value.

[0173] Compared with the voltage ride-through control link which uses steady-state values ​​to identify control modes and parameters, the steady-state control link under normal operation has more control modes and parameters, and needs to use the dynamic process of the inverter for identification. In order to find a set of parameters with the smallest deviation between the response curve and the curve to be identified among multiple control modes and parameters, the particle swarm algorithm is used as the optimization tool.

[0174] As a basic intelligent optimization algorithm, the particle swarm algorithm has derived many improved methods, such as changing the inertia factor and learning factor, changing the speed dimension, optimizing the mutation strategy, etc. The present invention adds a feedback link for adjusting the parameter optimization range on the basis of the adaptive particle swarm algorithm, that is, when the parameter in the optimal solution contains a boundary value, the parameter boundary value is expanded and the solution is performed again to ensure that for equipment with atypical parameters, the identification strategy proposed in the present invention can still find its optimal solution under different control models. For the case where the parameter x is greater than 0, the method for adjusting the parameter boundary value is:

[0175]

[0176] In the formula, x maxi 、x mini are the upper and lower limits of the optimization range of the original parameter x, respectively. best is the PSO optimization result, x maxi+1 、x mini+1 are the upper and lower limits of the optimization range of the updated parameter x respectively. The particle swarm algorithm used in the present invention is as follows Figure 7 As shown in the figure, in the particle swarm algorithm used, each particle contains all the parameters to be identified, the change of particle position means the change of parameter value, the particle speed means the change of parameter value, the optimal solution is the optimal set of parameters obtained by identification, that is, the particle moves in a high-dimensional space composed of various parameters to be identified, and the main process of the identification method is the same as the conventional particle swarm algorithm.

[0177] The parameter identification of the steady-state active control mode and the steady-state reactive control mode is discussed in detail below.

[0178] 1) Steady-state active power control mode and parameter identification

[0179] The steady-state active power control model of photovoltaic inverter is as follows: Figure 2shown and includes Figure 4 In the “equivalent converter link”, T pord is the delay time of the station command, that is, the time interval between the command change time and the active current change time, which can be identified by changing the control command. The control link adopts two methods: open-loop control and PI control. The parameters to be identified are shown in Table 2.

[0180] Table 2 Summary of control modes and parameters to be identified in active control link

[0181]

[0182] Adaptive particle swarm algorithm is used to identify the active control mode and parameters. Multiple sets of data are used to simultaneously identify multiple parameters of the two control modes. The control link parameters are identified first, and then the measurement value time constant is identified. The identification process is as follows: Figure 8 As shown, the following steps are included.

[0183] (1) In order to effectively identify different control modes, more than three sets of simulation or measured data are required.

[0184] (2) Three sets of data are input into the two control models in sequence, and the PSO algorithm proposed in the present invention is used to identify all parameters. For each control mode, the objective function fitness value (the deviation between the identification result and the input data) of the three sets of data is obtained.

[0185] (3) Select the two sets of parameter identification results with the smaller target fitness function value among the three sets of data, calculate the average value and confirm K p , K i , T ip parameter.

[0186] (4) Further identification of T rp , T g , T rv For each control mode, obtain the objective function fitness value of three sets of data, select the two smaller sets of parameter identification results, calculate the average value and confirm T rp , T g , T rv parameter.

[0187] (5) After obtaining the parameters of the two control modes, use the remaining data sets to verify the two control modes and compare the waveform errors between the two modes. Confirm that the control mode with the smaller error is the control mode set for the inverter, and determine the parameter values ​​based on the identification results of steps (3) and (4).

[0188] In this embodiment, as shown in steps (2)-(4), when determining the parameters under a control mode, the PSO algorithm is used twice for identification, wherein the parameters identified for the first time are key parameters with high sensitivity, while the parameters identified for the second time are non-key parameters with low sensitivity. By preferentially identifying the parameters with high sensitivity, the accuracy of the identification result can be improved. In this embodiment, the control mode and parameter identification results of the active control link are shown in Table 3.

[0189] Table 3 Active control link control mode and parameter identification results

[0190] Parameter name Setting Value Adaptive PSO error Standard PSO error Control Mode PI control PI control / PI control / <![CDATA[K p_lp ]]> 0.2 0.2002 0.1％ 0.2011 0.55％ <![CDATA[K i_lp ]]> 3.5 3.5000 0 3.5034 0.97％ <![CDATA[T rp ]]> 0.01 0.0098 2％ 0.0073 27％ <![CDATA[T g ]]> 0.01 0.0101 1％ 0.0132 32％

[0191] 2) Steady-state reactive power control mode and parameter identification

[0192] The reactive power control model of photovoltaic inverter is as follows: Figure 3 shown and includes Figure 4 In the “equivalent converter link”, T qord is the delay time of the station command, that is, the time interval between the command change time and the active current change time, which can be identified by changing the control command. The reactive power control link of the converter includes reactive power reference value selection, reactive current control mode selection, and reactive power / voltage coordinated control mode selection. The parameters to be identified are shown in Table 4.

[0193] Table 4 Summary of control modes and parameters to be identified in reactive power control link

[0194]

[0195]

[0196] The overall identification process of reactive power control link is as follows: Fig. 9 As shown, the following steps are included.

[0197] (1) In order to effectively identify different control modes, it is necessary to obtain more than three sets of simulation or measured data.

[0198] (2) Three sets of data are input into the three control models in sequence, and the PSO algorithm proposed in the present invention is used to identify all parameters. For each control mode, the objective function fitness value (the deviation between the identification result and the input data) of the three sets of data is obtained.

[0199] (3) Select two sets of parameter identification results with smaller target fitness function values ​​among the three sets of data, calculate the average value, and then confirm each proportional integral parameter and T iq .

[0200] (4) Further identification of T fltr , T g , T rvFor each control mode, obtain the objective function fitness value of three sets of data, select the two smaller sets of parameter identification results, calculate the average value and confirm T fltr , T g , T rv parameter.

[0201] (5) After obtaining the parameters of the three control modes, use the remaining data sets to verify the three control modes and compare the waveform errors between the two. Confirm that the control mode with the smaller error is the control mode set for the inverter, and determine the parameter values ​​based on the identification results of steps (3) and (4).

[0202] Embodiment 2

[0203] In order to execute the method corresponding to the above-mentioned embodiment 1 to achieve the corresponding functions and technical effects, a system for identifying inverter control modes and parameters is provided below, including: a measurement data acquisition module and a control mode and parameter identification module. The measurement data acquisition module is used to acquire the measurement data of the photovoltaic inverter in different control links; the control mode and parameter identification module is used to identify the control mode and parameters of the photovoltaic inverter in different control links according to the measurement data to obtain the identification results; the identification results are used to model and simulate the photovoltaic inverter to determine the operating characteristics of the photovoltaic inverter.

[0204] The control link includes: fault ride-through link, fault ride-through recovery link, current limiting link and steady-state control link; the measurement data of the fault ride-through link includes: active power, reactive power, active current, reactive current and terminal voltage; the measurement data of the fault ride-through recovery link includes: fault ride-through end time and active current recovery curve; the measurement data of the current limiting link includes: reactive current and total current; the measurement data of the steady-state control link includes: station active power instruction, station reactive power instruction, inverter output active power, inverter output reactive power, inverter terminal voltage, power factor reference value and reactive power reference value. The control modes of the fault ride-through link include: no additional control, specified power control, specified current control and control according to the current before ride-through; the control modes of the fault ride-through recovery link include: no additional control, fixed slope control and control according to inertia constant; the control modes of the steady-state control link include: open-loop control, PI control and reactive / voltage coordinated control; the parameters of the current limiting link include: total current upper limit and reactive current upper limit.

[0205] Embodiment 3

[0206] An embodiment of the present invention further provides an electronic device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to run the computer program to enable the electronic device to execute the inverter control mode and parameter identification method in embodiment 1. The electronic device may be a server.

[0207] In addition, the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the inverter control mode and parameter identification method in the first embodiment.

[0208] In summary, in response to the problems existing in the prior art, the present invention proposes a method, system, device and medium for identifying the control mode and parameters of an inverter. First, the main control modes and important parameters of different links such as the steady-state operation link and the fault ride-through link are determined, and then the control mode and parameters of the fault ride-through link are identified by using the measured values ​​of power, voltage, current, etc., and the multivariate linear regression based on least squares is adopted. The control mode and parameters of the steady-state control link are identified by using an adaptive particle swarm algorithm. While identifying the inverter parameters, the influence of different control modes is taken into account, and the mode commonly used by the inverter is determined in multiple control modes, and finally the model and parameters of the inverter steady-state control and voltage ride-through links are obtained.

[0209] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0210] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for identifying inverter control mode and parameters, characterized in that: include: Obtain measurement data of photovoltaic inverters in different control links; The control link includes: a fault ride-through link, a fault ride-through recovery link, a current limiting link and a steady-state control link; the measurement data of the fault ride-through link includes: active power, reactive power, active current, reactive current and terminal voltage; the measurement data of the fault ride-through recovery link includes: the end time of the fault ride-through and the active current recovery curve; the measurement data of the current limiting link includes: reactive current and total current; the measurement data of the steady-state control link includes: station active power command, station reactive power command, inverter output active power, inverter output reactive power, inverter terminal voltage, power factor reference value and reactive power reference value; According to the measured data, the control modes and parameters of the photovoltaic inverter in different control links are identified to obtain identification results; the control modes of the fault ride-through link include: no additional control, specified power control, specified current control and control according to the current before ride-through; the control modes of the fault ride-through recovery link include: no additional control, fixed slope control and control according to inertia constant; the control modes of the steady-state control link include: open-loop control, PI control and reactive power / voltage coordinated control; the parameters of the current limiting link include: total current upper limit and reactive current upper limit; the identification results are used to model and simulate the photovoltaic inverter to determine the operating characteristics of the photovoltaic inverter; The identification results include: a first identification result, a second identification result, a third identification result and a fourth identification result; according to the measurement data, the control modes and parameters of different control links are identified to obtain the identification results, which specifically include: According to the measured data of the fault ride-through link, a control mode and parameters of the fault ride-through link are identified by using a least squares-based multivariate linear regression to obtain a first identification result; Identify the control mode and parameters of the fault ride-through recovery link according to the measurement data of the fault ride-through recovery link to obtain a second identification result; Identify the parameters of the current limiting link according to the measurement data of the current limiting link to obtain a third identification result; According to the measured data of the steady-state control link, an adaptive particle swarm algorithm is used to identify the control mode and parameters of the steady-state control link to obtain a fourth identification result; According to the measured data of the fault ride-through link, the control mode and parameters of the fault ride-through link are identified by using multivariate linear regression based on least squares to obtain a first identification result, which specifically includes: Determine an available data group according to the measurement data of the fault ride-through link and the parameters of the current limiting link; the available data group is obtained by removing the current limiting data from the measurement data of the fault ride-through link; According to the available data set and the specified power calculation formula, a multivariate linear regression based on least squares is used to identify the parameters under the specified power control mode, and a difference between the power calculation value and the power measurement value is determined according to the parameters under the specified power control mode as a first difference; According to the available data set and the specified current calculation formula, a multivariate linear regression based on least squares is used to identify the parameters under the specified current control mode, and a difference between the current calculation value and the current measurement value is determined according to the parameters under the specified current control mode as a second difference; Determine whether the first difference and the second difference are both greater than a set error threshold, and obtain a first determination result; If the first judgment result is yes, determining that the control mode in the first identification result is no additional control; If the first judgment result is no, determining whether the first difference is less than the second difference to obtain a second judgment result; If the second judgment result is yes, determining that the control mode in the first identification result is the specified power control, and the parameters in the first identification result are parameters in the specified power control mode; If the second judgment result is no, determining that the control mode in the first identification result is the specified current control or the current control before crossing, and the parameters in the first identification result are the parameters in the specified current control mode or the parameters in the current control mode before crossing; The fault ride-through link includes: a fault ride-through active control link and a fault ride-through reactive control link; When in the fault ride-through reactive control link: if the second judgment result is no, determining that the control mode of the first identification result is the specified current control, and the parameters of the first identification result are the parameters under the specified current control mode; When in the fault-through reactive control link: if the second judgment result is no, then determine whether the parameters in the specified current control mode meet the set conditions to obtain a third judgment result; the parameters in the specified current control mode include: a first fault-through current calculation coefficient, a second fault-through current calculation coefficient and a fault-through current setting value; the setting condition is: the difference between the first fault-through current calculation coefficient and 0 is less than the first setting value, the difference between the second fault-through current calculation coefficient and 1 is less than the second setting value, and the difference between the fault-through current setting value and 0 is less than the third setting value; if the third judgment result is yes, then determine that the control mode in the first identification result is controlled by the current before crossing, and the parameters in the first identification result are the parameters in the current control mode before crossing; if the third judgment result is no, then determine that the control mode in the first identification result is the specified current control, and the parameters in the first identification result are the parameters in the specified current control mode; According to the measurement data of the fault ride-through recovery link, the control mode and parameters of the fault ride-through recovery link are identified to obtain a second identification result, which specifically includes: Taking the fault ride-through end time as the starting point, all data points within the range from the first set time to the second set time on the active current recovery curve are determined as the target data group, and the data points at the first set time and the data points at the second set time on the active current recovery curve are connected to obtain a target straight line; Calculate the absolute value of the data error between the data points in the target data set and the corresponding data points on the target straight line; For any target data set, if the absolute value of the data error of more than 95% of the data points is less than or equal to 10% of the corresponding data points on the target straight line, it is determined that the target data set meets the fixed slope recovery requirement; Determine whether more than 90% of the target data groups satisfy the fixed slope recovery condition, and obtain a fourth determination result; If the fourth judgment result is yes, then determining the control mode in the second identification result to be constant slope control, and determining the parameter in the second identification result to be the average value of the slopes of the target straight lines corresponding to all target data groups that satisfy the fixed slope recovery; If the fourth judgment result is no, fitting the data points in the target data set by using least squares-based polynomial fitting to obtain a fitting polynomial corresponding to the target data set; Calculating the inertial time constant of the data points in the target data set according to the fitting polynomial; For any target data set, if more than 95% of the inertia time constants are distributed within the range of ±10% of the average value of the inertia time constant, then the target time constant corresponding to the target data set is determined to be the average value of the inertia time constants of all data points in the target data set; Determine whether the difference between the maximum value and the minimum value of all target time constants is less than or equal to 10% of the average value of all target time constants, to obtain a fifth determination result; If the fifth judgment result is yes, determining that the control mode in the second identification result is control according to the inertia constant, and determining that the parameter in the second identification result is an average value of all target time constants; If the fifth judgment result is no, determining that the control mode in the second identification result is no additional control; According to the measured data of the current limiting link, the parameters of the current limiting link are identified to obtain a third identification result, which specifically includes: Converting the measured data of the current limiting link into per-unit values ​​to obtain a test data set; Determine whether there is a group with a maximum reactive current greater than 1.05 pu in the test data group, and obtain a sixth determination result; If the sixth judgment result is yes, all groups whose maximum reactive current is within the range of the maximum value of the maximum reactive current and 0.01 pu below the maximum value of the maximum reactive current are determined as the first current limiting group, and the upper limit of the reactive current in the third identification result is determined as the average value of the maximum reactive current of all the first current limiting groups; If the sixth judgment result is no, the reactive current upper limit in the third identification result is determined to be 1.1 pu; Determine whether there is a group with a maximum total current greater than 1.1 pu in the test data group, and obtain a seventh determination result; If the seventh judgment result is yes, all groups whose maximum total current is within the range of the maximum value of the maximum total current and 0.01 pu below the maximum value of the maximum total current are determined as the second current limiting group, and the total current upper limit in the third identification result is determined as the average value of the maximum total current of all the second current limiting groups; If the seventh judgment result is no, the total current upper limit in the third identification result is determined to be 1.2 pu; According to the measured data of the steady-state control link, the control mode and parameters of the steady-state control link are identified by using an adaptive particle swarm algorithm to obtain a fourth identification result, which specifically includes: Dividing the measured data of the steady-state control link into a calculation data group and a verification data group according to a set ratio; According to the calculation data group, an adaptive particle swarm algorithm is used to identify parameters of each control mode of the steady-state control link; Calculating the error value under each control mode according to the verification data group and the parameters under each control mode; The error values ​​under the control modes are compared, and the control mode in the fourth identification result is determined as the control mode with the smallest error value, and the parameters in the fourth identification result are determined as the parameters under the control mode with the smallest error value.

2. An inverter control mode and parameter identification system, characterized in that: include: A measurement data acquisition module is used to obtain measurement data of the photovoltaic inverter in different control links; The control link includes: a fault ride-through link, a fault ride-through recovery link, a current limiting link and a steady-state control link; the measurement data of the fault ride-through link includes: active power, reactive power, active current, reactive current and terminal voltage; the measurement data of the fault ride-through recovery link includes: the end time of the fault ride-through and the active current recovery curve; the measurement data of the current limiting link includes: reactive current and total current; the measurement data of the steady-state control link includes: station active power command, station reactive power command, inverter output active power, inverter output reactive power, inverter terminal voltage, power factor reference value and reactive power reference value; A control mode and parameter identification module is used to identify the control mode and parameters of the photovoltaic inverter in different control links according to the measured data to obtain an identification result; the control modes of the fault ride-through link include: no additional control, specified power control, specified current control and control according to the current before ride-through; the control modes of the fault ride-through recovery link include: no additional control, fixed slope control and control according to the inertia constant; the control modes of the steady-state control link include: open-loop control, PI control and reactive / voltage coordinated control; the parameters of the current limiting link include: total current upper limit and reactive current upper limit; the identification result is used to model and simulate the photovoltaic inverter to determine the operating characteristics of the photovoltaic inverter; The identification results include: a first identification result, a second identification result, a third identification result and a fourth identification result; according to the measurement data, the control modes and parameters of different control links are identified to obtain the identification results, which specifically include: According to the measurement data of the fault ride-through link, a control mode and parameters of the fault ride-through link are identified by using a multivariate linear regression based on least squares to obtain a first identification result; Identify the control mode and parameters of the fault ride-through recovery link according to the measurement data of the fault ride-through recovery link to obtain a second identification result; Identify the parameters of the current limiting link according to the measurement data of the current limiting link to obtain a third identification result; According to the measured data of the steady-state control link, an adaptive particle swarm algorithm is used to identify the control mode and parameters of the steady-state control link to obtain a fourth identification result; According to the measured data of the fault ride-through link, the control mode and parameters of the fault ride-through link are identified by using multivariate linear regression based on least squares to obtain a first identification result, which specifically includes: Determine an available data group according to the measurement data of the fault ride-through link and the parameters of the current limiting link; the available data group is obtained by removing the current limiting data from the measurement data of the fault ride-through link; According to the available data set and the specified power calculation formula, a multivariate linear regression based on least squares is used to identify the parameters under the specified power control mode, and a difference between the power calculation value and the power measurement value is determined according to the parameters under the specified power control mode as a first difference; According to the available data set and the specified current calculation formula, a multivariate linear regression based on least squares is used to identify the parameters under the specified current control mode, and a difference between the current calculation value and the current measurement value is determined according to the parameters under the specified current control mode as a second difference; Determine whether the first difference and the second difference are both greater than a set error threshold, and obtain a first determination result; If the first judgment result is yes, determining that the control mode in the first identification result is no additional control; If the first judgment result is no, determining whether the first difference is less than the second difference to obtain a second judgment result; If the second judgment result is yes, determining that the control mode in the first identification result is the specified power control, and the parameters in the first identification result are parameters in the specified power control mode; If the second judgment result is no, determining that the control mode in the first identification result is the specified current control or the current control before crossing, and the parameters in the first identification result are the parameters in the specified current control mode or the parameters in the current control mode before crossing; The fault ride-through link includes: a fault ride-through active control link and a fault ride-through reactive control link; When in the fault ride-through reactive control link: if the second judgment result is no, determining that the control mode of the first identification result is the specified current control, and the parameters of the first identification result are the parameters under the specified current control mode; When in the fault-through reactive control link: if the second judgment result is no, then determine whether the parameters in the specified current control mode meet the set conditions to obtain a third judgment result; the parameters in the specified current control mode include: a first fault-through current calculation coefficient, a second fault-through current calculation coefficient and a fault-through current setting value; the setting condition is: the difference between the first fault-through current calculation coefficient and 0 is less than the first setting value, the difference between the second fault-through current calculation coefficient and 1 is less than the second setting value, and the difference between the fault-through current setting value and 0 is less than the third setting value; if the third judgment result is yes, then determine that the control mode in the first identification result is controlled by the current before crossing, and the parameters in the first identification result are the parameters in the current control mode before crossing; if the third judgment result is no, then determine that the control mode in the first identification result is the specified current control, and the parameters in the first identification result are the parameters in the specified current control mode; According to the measurement data of the fault ride-through recovery link, the control mode and parameters of the fault ride-through recovery link are identified to obtain a second identification result, which specifically includes: Taking the fault ride-through end time as the starting point, all data points within the range from the first set time to the second set time on the active current recovery curve are determined as the target data group, and the data points at the first set time and the data points at the second set time on the active current recovery curve are connected to obtain a target straight line; Calculate the absolute value of the data error between the data points in the target data set and the corresponding data points on the target straight line; For any target data set, if the absolute value of the data error of more than 95% of the data points is less than or equal to 10% of the corresponding data points on the target straight line, it is determined that the target data set meets the fixed slope recovery requirement; Determine whether more than 90% of the target data groups satisfy the fixed slope recovery condition, and obtain a fourth determination result; If the fourth judgment result is yes, then determining the control mode in the second identification result to be constant slope control, and determining the parameter in the second identification result to be the average value of the slopes of the target straight lines corresponding to all target data groups that satisfy the fixed slope recovery; If the fourth judgment result is no, fitting the data points in the target data set by using least squares-based polynomial fitting to obtain a fitting polynomial corresponding to the target data set; Calculating the inertial time constant of the data points in the target data set according to the fitting polynomial; For any target data set, if more than 95% of the inertia time constants are distributed within the range of ±10% of the average value of the inertia time constant, then the target time constant corresponding to the target data set is determined to be the average value of the inertia time constants of all data points in the target data set; Determine whether the difference between the maximum value and the minimum value of all target time constants is less than or equal to 10% of the average value of all target time constants, to obtain a fifth determination result; If the fifth judgment result is yes, determining that the control mode in the second identification result is control according to the inertia constant, and determining that the parameter in the second identification result is an average value of all target time constants; If the fifth judgment result is no, determining that the control mode in the second identification result is no additional control; According to the measured data of the current limiting link, the parameters of the current limiting link are identified to obtain a third identification result, which specifically includes: Converting the measured data of the current limiting link into per-unit values ​​to obtain a test data set; Determine whether there is a group with a maximum reactive current greater than 1.05 pu in the test data group, and obtain a sixth determination result; If the sixth judgment result is yes, all groups whose maximum reactive current is within the range of the maximum value of the maximum reactive current and 0.01 pu below the maximum value of the maximum reactive current are determined as the first current limiting group, and the upper limit of the reactive current in the third identification result is determined as the average value of the maximum reactive current of all the first current limiting groups; If the sixth judgment result is no, the reactive current upper limit in the third identification result is determined to be 1.1 pu; Determine whether there is a group with a maximum total current greater than 1.1 pu in the test data group, and obtain a seventh determination result; If the seventh judgment result is yes, all groups whose maximum total current is within the range of the maximum value of the maximum total current and 0.01 pu below the maximum value of the maximum total current are determined as the second current limiting group, and the total current upper limit in the third identification result is determined as the average value of the maximum total current of all the second current limiting groups; If the seventh judgment result is no, the total current upper limit in the third identification result is determined to be 1.2 pu; According to the measured data of the steady-state control link, the control mode and parameters of the steady-state control link are identified by using an adaptive particle swarm algorithm to obtain a fourth identification result, which specifically includes: Dividing the measured data of the steady-state control link into a calculation data group and a verification data group according to a set ratio; According to the calculation data group, an adaptive particle swarm algorithm is used to identify parameters of each control mode of the steady-state control link; Calculating the error value under each control mode according to the verification data group and the parameters under each control mode; The error values ​​under the control modes are compared, and the control mode in the fourth identification result is determined as the control mode with the smallest error value, and the parameters in the fourth identification result are determined as the parameters under the control mode with the smallest error value.

3. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the inverter control mode and parameter identification method as claimed in claim 1.

4. A computer-readable storage medium, characterized in that: It stores a computer program, and when the computer program is executed by a processor, the method for identifying the inverter control mode and parameters as claimed in claim 1 is implemented.

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