A parameter determination method and device of a new energy grid-connected converter

Abstract

The application relates to the technical field of grid-connected protection of new energy stations, in particular to a parameter determination method and device for a new energy grid-connected converter, which fully considers the influence of the dynamic characteristics of a phase-locked loop on parameter identification, establishes an accurate parameter determination model, and then uses a particle swarm algorithm to perform optimization processing on the proportional-integral controller parameters of the parameter determination model to obtain target proportional-integral controller parameters, so that accurate identification of the parameters of the proportional-integral controller is realized.

Description

Technical Field

[0001] This application relates to the field of grid-connected protection technology for new energy power plants, and in particular to a method and device for determining parameters of a new energy grid-connected converter. Background Technology

[0002] With the high-proportion penetration of inverter-interfaced distributed generation (IIDG) into the distribution system, the fault characteristics of the distribution network will undergo fundamental changes. However, due to the "black box" nature of manufacturer control parameters, the accuracy of simulation model construction is affected, which in turn affects the realism of the assessed fault characteristics, resulting in significant differences between simulated waveforms and field recordings. As the interface for renewable energy to access the power system, the grid-connected converter is a key component determining the grid-connected operation characteristics of renewable energy. The accuracy of converter control parameters is particularly important in both its operational characteristic analysis and fault protection.

[0003] Existing parameter determination methods can only construct equivalent models of new energy converters that represent the stable operating state of the converter. They cannot accurately reflect the fault transient characteristics of new energy power sources, are not applicable to the analysis of electromagnetic transients, and result in low accuracy of the determined converter parameters. Summary of the Invention

[0004] To address the problems of existing technologies, this application provides a method and apparatus for determining parameters of a new energy grid-connected converter. The technical solution is as follows:

[0005] On the one hand, a method for determining the parameters of a renewable energy grid-connected converter is provided, applicable to a renewable energy grid-connected system, wherein the renewable energy grid-connected system includes a renewable energy power source, a converter, a transformer, and a public power grid system connected in sequence; the method includes:

[0006] An initial two-dimensional differential equation concerning the dq-axis current and time is constructed based on the preset command value of the dq-axis current, the equivalent energy storage value between the converter and the point of common coupling, the output angular frequency of the phase-locked loop, and the parameters of the proportional-integral controller. The dq-axis current components in the initial two-dimensional differential equation are mutually coupled. The converter includes a proportional-integral controller.

[0007] The parameters in the initial two-dimensional differential equation are reconstructed to obtain the target two-dimensional differential equation corresponding to two conjugate variables; the two variables are obtained by constructing complex numbers based on the dq-axis currents.

[0008] Based on the linear relationship between the two variables and the dq-axis current, the target two-dimensional differential equation is analyzed to obtain the parameter determination model; the parameter determination model characterizes the mapping relationship between the dq-axis current and the phase-locked loop output angular frequency and the proportional-integral controller parameters.

[0009] The particle swarm optimization algorithm is used to optimize the parameters of the proportional-integral controller in the parameter determination model to obtain the target proportional-integral controller parameters.

[0010] In one exemplary implementation, based on the linear relationship between the two variables and the dq-axis current, the target two-dimensional differential equation is analytically processed to obtain a parameter determination model, including:

[0011] Solve the target two-dimensional differential equation to obtain the analytical equation corresponding to any one of the two variables;

[0012] By performing complex reconstruction on the exponential terms in the analytical equation, the target analytical equation after complex reconstruction is obtained;

[0013] Based on the linear relationship between the two variables and the dq-axis current, the target analytical equation is analyzed to obtain the time-domain analytical equation of the dq-axis current.

[0014] The time-domain analytical equation of the dq-axis current is determined as a parameter determination model.

[0015] In one exemplary implementation, the parameter determination model is the following equation:

[0016]

[0017] Among them, i d (t) represents the d-axis current at time t; i q (t) represents the q-axis current at time t; and These are the commanded values ​​for the d-axis current and q-axis current after the fault, respectively; A1 = sqrt((C 1r ) 2 +(C 1i ) 2 );

[0018] A2 = sqrt((C 2r ) 2 +(C 2i ) 2 ); n1 = arctan(C 1i / C 1r ); n2 = arctan(C 2i / C 2r ); C 1r C 1i and C 2r C 2iThe parameters are obtained by substituting other parameter variables corresponding to the non-abrupt dq-axis current at the time of the fault into the parameter determination model; m1, m2, α and β are parameter variables determined based on the preset command value of dq-axis current, equivalent energy storage value, phase-locked loop output angular frequency and proportional-integral controller parameters.

[0019] In one exemplary embodiment, the particle swarm optimization algorithm is used to optimize the proportional-integral (PI) controller parameters of the parameter determination model to obtain the target PPI controller parameters, including:

[0020] In the event of a fault between the transformer and the public power grid system, the target transient electrical performance dataset corresponding to the fault transient process is determined. The target transient electrical performance dataset characterizes the phase-locked loop output angular frequency and dq-axis current measurement values ​​at multiple sampling times. The transformer is located between the converter and the public power grid system.

[0021] The calculated dq-axis current value corresponding to each sampling time in the target transient electrical performance dataset is determined based on the parameter determination model and the target transient electrical performance dataset.

[0022] The fitting similarity value is determined based on the measured dq-axis current values ​​and the corresponding calculated dq-axis current values ​​at each sampling time in the target transient electrical performance dataset; the fitting similarity value characterizes the degree of similarity between the measured dq-axis current values ​​and the corresponding calculated dq-axis current values ​​at each sampling time.

[0023] The parameters of the proportional-integral controller in the model are determined by iteratively updating the parameters using the particle swarm optimization algorithm until a preset number of iterations is reached. The convergence value of the fitting similarity value in the preset number of iterations is then determined as the target fitting similarity value.

[0024] The proportional-integral controller parameters corresponding to the target fitting similarity value are determined as the target proportional-integral controller parameters.

[0025] In one exemplary embodiment, during the k-th iteration, if the fitting similarity value corresponding to the k-th iteration is greater than or equal to the average fitting similarity value, then the weight coefficient in the particle swarm optimization algorithm is determined as the first preset weight coefficient; the average fitting similarity value is determined based on the fitting similarity value corresponding to each iteration in the k iterations and k; if the fitting similarity value corresponding to the k-th iteration is less than the average fitting similarity value, then the weight coefficient in the particle swarm optimization algorithm is determined as the second preset weight coefficient; k is a natural number.

[0026] The second preset weighting coefficient is determined based on the following formula:

[0027]

[0028] Where, ω minω represents the third preset weighting coefficient. max Z represents the first preset weighting coefficient; k Z represents the fitting similarity value corresponding to the k-th iteration; min Z represents the minimum fit similarity value in k iterations; avg This represents the average fitting similarity value; the first preset weight coefficient is greater than the third preset weight coefficient.

[0029] In one exemplary embodiment, the method for determining the phase-locked loop output angular frequency corresponding to the sampling time includes:

[0030] Acquire the voltage parameters, proportional-integral controller parameters, and power frequency angular frequency of the common connection point before and after the fault, corresponding to the sampling time.

[0031] The phase angle change value of the phase-locked loop output corresponding to the sampling time is determined based on the voltage parameters of the common connection point before and after the fault, the proportional-integral controller parameters, and the power frequency angular frequency.

[0032] The phase-locked loop output angular frequency corresponding to the sampling time is determined based on the phase-locked loop output phase angle change value, the power frequency angular frequency, and the voltage phase angle of the common connection point after the fault.

[0033] In one exemplary implementation, the fitting similarity value is calculated based on the following formula:

[0034]

[0035] Where N represents the number of particles; N is a natural number greater than or equal to 1; id_mea(k) and i q_ mea(k) represents the measured values ​​of the k-th d-axis current and q-axis current, respectively, and id_cal(k) and i q_ cal(k) are the calculated values ​​of the k-th d-axis current and q-axis current, respectively.

[0036] In one exemplary implementation, after the nth iteration, the updated position of the Kth particle can be calculated based on the following formula:

[0037]

[0038] The update rate of the Kth particle can be calculated based on the following formula:

[0039]

[0040] in, Let represent the updated position of the Kth particle after the nth iteration and the updated position of the Kth particle after the (n-1)th iteration, respectively. ω represents the update rate of the Kth particle after the nth iteration and the update rate of the Kth particle after the (n-1)th iteration, respectively; ω represents the weight coefficient; c1 represents the individual learning factor; c2 represents the collective learning factor; r1 and r2 are random numbers in the interval [0,1] to increase the randomness of the search; Let be the optimal position found by the k-th particle. This is the optimal position for group search.

[0041] In one exemplary implementation, the initial two-dimensional differential equation is expressed as follows:

[0042]

[0043] Where, Δω=ω0-ω PLL , representing the error value between the power frequency angular frequency and the actual angular frequency of the electrical coupling term; i d and i q These represent the d-axis current and the q-axis current, respectively; i d * and i q * These represent the commanded values ​​for the d-axis current and the q-axis current, respectively; L represents the equivalent inductance between the converter and the point of common coupling, and R represents the equivalent resistance between the converter and the point of common coupling; k ip k ii These are the proportional coefficient and integral coefficient in the parameters of the current inner loop proportional-integral controller, respectively.

[0044] In one exemplary implementation, the two conjugate variables can be represented as x1 and x2, respectively;

[0045] Where, x1=i d +ji q x2 = i d -ji q i d and i q These represent the d-axis current and the q-axis current, respectively.

[0046] In one exemplary implementation, the target two-dimensional differential equation can be expressed as follows:

[0047]

[0048] Where, m=(k ip +R) / L,n=Δω,l=k ii / L,p=k ii / L×i d * q = k ii / L×i q *All parameters are constants in the parameter determination model after construction.

[0049] On the other hand, a parameter determination device for a new energy grid-connected converter is provided, the device comprising:

[0050] The module is used to construct an initial two-dimensional differential equation about the dq-axis current and time based on the preset command value of the dq-axis current, the equivalent energy storage value between the converter and the point of common coupling, the output angular frequency of the phase-locked loop, and the parameters of the proportional-integral controller; the dq-axis current components in the initial two-dimensional differential equation are coupled to each other; the converter includes a proportional-integral controller;

[0051] The reconstruction module is used to reconstruct the parameters in the initial two-dimensional differential equation to obtain the target two-dimensional differential equation corresponding to two conjugate variables; the two variables are obtained by constructing complex numbers based on the dq-axis current;

[0052] The analytical module is used to perform analytical processing on the target two-dimensional differential equation based on the linear relationship between the two variables and the dq-axis current, and obtain the parameter determination model. The parameter determination model represents the mapping relationship between the dq-axis current and the phase-locked loop output angular frequency and the proportional-integral controller parameters.

[0053] The processing module is used to optimize the proportional-integral controller parameters of the parameter determination model using the particle swarm optimization algorithm to obtain the target proportional-integral controller parameters.

[0054] On the other hand, an electronic device is provided, including a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the parameter determination method for a new energy grid-connected converter in any of the above aspects.

[0055] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction or at least one program is stored in the computer-readable storage medium, wherein the at least one instruction or the at least one program is loaded and executed by a processor to implement the parameter determination method for a new energy grid-connected converter as described above.

[0056] On the other hand, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. The processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the parameter determination method for a new energy grid-connected converter according to any of the above aspects.

[0057] This application fully considers the impact of phase-locked loop (PLL) dynamic characteristics on parameter identification and establishes an accurate parameter determination model, thereby improving the accuracy of parameter identification for grid-connected converters in energy systems. The specific construction process is as follows: Based on the preset command value of the dq-axis current, the equivalent energy storage value between the converter and the point of common coupling, the PLL output angular frequency, and the proportional-integral (PI) controller parameters, an initial two-dimensional differential equation regarding the dq-axis current and time is constructed; the dq-axis current components in the initial two-dimensional differential equation are mutually coupled; the converter includes a proportional-integral (PI) controller; the parameters in the initial two-dimensional differential equation are reconstructed to obtain a target two-dimensional differential equation corresponding to two conjugate variables; the two variables are based on the dq-axis current. The dq-axis current is constructed using complex numbers. Based on the linear relationship between the two variables and the dq-axis current, the target two-dimensional differential equation is analyzed to obtain the parameter determination model. The parameter determination model represents the mapping relationship between the dq-axis current, the phase-locked loop output angular frequency, and the proportional-integral controller parameters. Then, the proportional-integral controller parameters of the parameter determination model are optimized using the particle swarm optimization algorithm to obtain the target proportional-integral controller parameters, thereby achieving accurate identification of the proportional-integral controller parameters. These parameters are then substituted into the parameter determination model to obtain the target parameter determination model. Based on this accurate target parameter determination model, the research on grid fault characteristics and relay protection for new energy access is of great significance. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a schematic diagram of the structure of a new energy grid-connected system provided in an embodiment of this application;

[0060] Figure 2 This is a flowchart illustrating a method for determining parameters of a new energy grid-connected converter, as provided in an embodiment of this application.

[0061] Figure 3 This is a flowchart illustrating another method for determining parameters of a new energy grid-connected converter provided in an embodiment of this application;

[0062] Figure 4 This is a transfer function block diagram of a phase-locked loop provided in an embodiment of this application;

[0063] Figure 5 This is a voltage phase change diagram of a PCC point provided in an embodiment of this application;

[0064] Figure 6This is a curve showing the trajectory sensitivity of a proportional-integral controller parameter with respect to the d-axis current, as provided in an embodiment of this application.

[0065] Figure 7 This is a curve showing the trajectory sensitivity of a proportional-integral controller parameter with respect to the q-axis current, as provided in an embodiment of this application.

[0066] Figure 8 This is a time-frequency diagram of a phase-locked loop output provided in an embodiment of this application;

[0067] Figure 9 This is a current-time curve obtained based on a conventional and the parameter determination model of this application, as provided in an embodiment of this application;

[0068] Figure 10 This is a structural block diagram of a parameter determination device for a new energy grid-connected converter provided in an embodiment of this application. Detailed Implementation

[0069] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0070] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0071] It is understood that in the specific embodiments of this application, data such as user information are involved. When the above embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0072] In essence, phase-locked loops (PLLs) play a crucial role in renewable energy grid-connected systems. The dynamic process of the PLL after a fault significantly impacts the transient characteristics of the fault current. However, existing parameter identification methods do not consider the dynamic characteristics of the PLL, neglecting the nonlinear errors introduced by the PLL's dynamic response. This makes it impossible to establish a parameter determination model that accurately reflects the transient characteristics of the converter during faults. Consequently, the constructed equivalent model of the renewable energy converter can only represent the converter's stable operating state, failing to accurately reflect the transient characteristics of the renewable energy power supply during faults and making it unsuitable for electromagnetic transient analysis. This also results in low accuracy of the determined converter parameters. Therefore, this application fully considers the impact of the PLL's dynamic characteristics on parameter identification and establishes an accurate parameter determination model, thereby improving the accuracy of parameter identification for renewable energy grid-connected converters.

[0073] The symbols and terms used in this application specification and drawings will be explained below.

[0074] PCC stands for Common Connection Point.

[0075] PWM stands for Pulse Modulator.

[0076] PI stands for Proportional-Integral Controller.

[0077] T 3S / 2r The Park transformation is a commonly used coordinate transformation method for analyzing the operation of synchronous motors. The Park transformation projects the three-phase currents (a, b, c) of the stator onto the direct axis (d-axis), the quadrature axis (q-axis), and the zero axis (0-axis) perpendicular to the dq plane, which rotate with the rotor. This diagonalizes the stator inductance matrix and simplifies the analysis of synchronous motor operation. In other words, the Park transformation is used to transform the three-phase currents in the abc coordinate system to the dq coordinate system.

[0078] An adder is a device that generates the sum of numbers, and its purpose is to perform addition.

[0079] This refers to a transformer, which is capable of stepping up and stepping down voltage. Please refer to [link to relevant documentation]. Figure 1 The transformer can raise the voltage at bus C from 380V to 35KV.

[0080] SRF-PLL is short for Synchronous Reference Frame Phaselocked Loop.

[0081] Please see Figure 1 The diagram shown is a structural schematic of a new energy grid-connected system provided in an embodiment of this application. Figure 1The grid-connected new energy system shown can specifically be an inverter-interfaced renewable energy source (IIRE) transmission system and its control structure diagram. The short-circuit fault occurs... Figure 1 Between the A and B busbars, the new energy grid-connected system includes a new energy power source, a converter, a transformer, and a public power grid system connected in sequence. The common connection point is located between the converter and the transformer. In constructing the parameter determination model, this application fully considers the influence of the dynamic characteristics of the phase-locked loop on parameter identification, thereby constructing an accurate parameter determination model. Then, the particle swarm optimization algorithm is used to optimize the proportional-integral controller parameters of the parameter determination model to obtain the target proportional-integral controller parameters, thereby achieving accurate identification of the proportional-integral controller parameters. These parameters are then substituted into the parameter determination model to obtain the target parameter determination model.

[0082] Please see Figure 2 The diagram illustrates a flowchart of a parameter determination method for a new energy grid-connected converter according to an embodiment of this application. This specification provides the operational steps described in the embodiments or flowcharts, but based on conventional or non-inventive methods, more or fewer operational steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual system or product execution, the methods shown in the embodiments or drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment). Specifically, as shown... Figure 2 As shown, the method may include:

[0083] S201: Based on the preset command value of the dq-axis current, the equivalent energy storage value between the converter and the point of common coupling, the output angular frequency of the phase-locked loop, and the parameters of the proportional-integral controller, an initial two-dimensional differential equation about the dq-axis current and time is constructed; the dq-axis current components in the initial two-dimensional differential equation are mutually coupled; the converter includes a proportional-integral controller.

[0084] In this embodiment, the execution entity of the method can be a terminal or server communicatively connected to the new energy grid-connected system. This terminal can construct a parameter determination model and identify the parameters of the new energy grid-connected converter (such as proportional-integral controller parameters), thereby constructing an accurate parameter determination model. Optionally, the terminal can be an electronic terminal such as a tablet, laptop, or computer.

[0085] The principle for constructing the initial two-dimensional differential equations concerning the dq-axis current and time is as follows:

[0086] Since the DC voltage outer loop is typically disconnected after a fault occurs, the influence of the voltage outer loop can be ignored (see [link to relevant documentation]). Figure 1The control structure diagram only retains the inner current loop. The inner current loop tracks the command value of the dq axis current, and the converter enters the low voltage ride-through stage. According to the current Chinese national standard, this can be specifically expressed as the following formula:

[0087]

[0088] Among them, i d * and i q * These represent the commanded values ​​for the d-axis current and the q-axis current, respectively; I N BI represents the rated current; K is the reactive power support factor, which can be set to 1.5; N This indicates the maximum allowable current value of the converter during a fault, which can be set to 1.1 times the rated current; U represents the per-unit value of the grid voltage.

[0089] The converter port voltage equations in the dq coordinate system can be expressed as follows:

[0090]

[0091] Among them, u d and u q These represent the d-axis voltage and q-axis voltage at the converter port, respectively; L represents the equivalent inductance between the converter and the PCC point, and R represents the equivalent resistance between the converter and the PCC point.

[0092] The current inner-loop control response equation designed based on the power frequency steady-state electrical relationship in the inverter power supply control system can be expressed as follows:

[0093]

[0094] Where, k ip k ii These are the proportional and integral coefficients in the parameters of the current inner-loop proportional-integral controller; ω0 represents the power frequency angular frequency, which can be set to 100π; i d and i q Let represent the d-axis current and q-axis current, respectively. By combining equations (2) and (3), the converter port voltage u in equations (2) and (3) can be eliminated. By differentiating the combined differential equations, a second-order differential equation can be obtained:

[0095]

[0096] Where, Δω=ω0-ω PLL , which represents the error value between the power frequency angular frequency and the actual electrical coupling term angular frequency (i.e., the phase-locked loop output angular frequency).

[0097] S203: Reconstruct the parameters in the initial two-dimensional differential equation to obtain the target two-dimensional differential equation corresponding to two conjugate variables; the two variables are obtained by constructing complex numbers based on the dq-axis current.

[0098] Due to the dynamic process of the phase-locked loop during the fault transient, the dq-axis current components shown in equation (4) are mutually coupled, making it difficult to establish a parameter determination model, i.e., a transient current identification model. To address this problem, equations ① and ② of the above equation (4) can be reconstructed using complex numbers: ①+j②, ①-j②, and a new variable x1=i can be introduced. d +ji q with x2=i d -ji q Thus, we can obtain the following formula (5), which is the objective two-dimensional differential equation corresponding to the two conjugate variables x1 and x2.

[0099]

[0100] Where, m=(k ip +R) / L,n=Δω,l=k ii / L,p=k ii / L×i d * q = k ii / L×i q * All parameters are constants in the parameter determination model after construction.

[0101] S205: Based on the linear relationship between the two variables and the dq-axis current, the target two-dimensional differential equation is analyzed to obtain the parameter determination model; the parameter determination model characterizes the mapping relationship between the dq-axis current and the phase-locked loop output angular frequency and the proportional-integral controller parameters.

[0102] In one exemplary implementation, please refer to Figure 3 The specific implementation of step S205 may include:

[0103] S2051: Solve the target two-dimensional differential equation to obtain the analytical equation corresponding to any one of the two variables.

[0104] If Δω = constant, then formula (5) is a two-dimensional differential equation with constant coefficients. According to the general solution method for differential equations, the time-domain expressions of x1 and x2 can be obtained. Since variables x1 and x2 are conjugates, the current i can be obtained by solving any one term of x1 and x2. d and i q Analytical Expression. Based on the solution method for two-dimensional differential equations with constant coefficients, the analytical expression for x1 can be expressed as follows:

[0105]

[0106] Where C1 and C2 are undetermined complex coefficients.

[0107] S2053: Reconstruct the exponential terms in the analytical equation using complex numbers to obtain the target analytical equation after complex reconstruction.

[0108] In this embodiment, since C1 and C2 in the above formula (6) are complex coefficients to be determined, they can be obtained by substituting the initial current value of the converter and its derivative value; C1 and C2 can be transformed into exponential terms, so the exponential terms in formula (6) are C1, C2 ... The exponential term on the right side of formula (6) can be represented by the exponents exp[(α+jm1)t] and exp[(β+jm2t)t].

[0109] S2055: Based on the linear relationship between the two variables and the dq-axis current, the target analytical equation is analyzed to obtain the time-domain analytical equation of the dq-axis current.

[0110] S2057: The time-domain analytical equation of the dq-axis current is determined as a parameter determination model.

[0111] Combine [x1 x2] T and[i d i q ] T The linear relationship between them can be used to obtain i d (t) and i q The time-domain analytical expression of (t), which is also the parameter-determined model, is specifically represented by the following equation:

[0112]

[0113] Among them, i d (t) represents the d-axis current at time t; i q (t) represents the q-axis current at time t; and These are the commanded values ​​for the d-axis current and q-axis current after the fault, respectively; A1 = sqrt((C 1r ) 2 +(C 1i ) 2 );

[0114] A2 = sqrt((C 2r ) 2 +(C 2i ) 2 ); n1 = arctan(C 1i / C 1r ); n2 = arctan(C 2i / C2r ); C 1r C 1i and C 2r C 2i The parameters are obtained by substituting other parameter variables corresponding to the non-abrupt dq-axis current at the time of the fault into the parameter determination model; m1, m2, α and β are parameter variables determined based on the preset command value of dq-axis current, equivalent energy storage value, phase-locked loop output angular frequency and proportional-integral controller parameters. The specific solution process for these four parameter variables is described above.

[0115] In this embodiment, the preset command value for the dq-axis current specifically includes the d-axis current after a fault. and the commanded value of the q-axis current Command value i for d-axis current d * and the command value i of the q-axis current q * The equivalent energy storage value between the converter and the point of common coupling can specifically include the equivalent inductance L between the converter and the PCC point and the equivalent resistance R between the converter and the PCC point.

[0116] To further improve the accuracy of the constructed parameter determination model, a differential approximation approach can be used to calculate the transient current (i.e., the dq-axis current) corresponding to multiple sampling times during the fault transient process in practical applications. This is based on the phase-locked loop output angular frequency ω. PLL The constant change causes Δω to change continuously. Therefore, differential equation (5) is a variable coefficient differential equation, which is difficult to solve. The transient process of a converter fault is usually within tens of milliseconds, so the time period of the fault transient process [t1,t2] can be used to calculate the transient process time. n According to the sampling frequency f, it is divided into (t) n -t1)f segment, i.e. [t1,t2],[t2,t3],…,[t n-1 ,t n ], where t1 is the time when the fault occurs. Using the differential approximation approach, the angular frequency of the phase-locked loop output is assumed to remain constant during this time interval; for example, ω is approximated as... PLL (t n-1 ) represents the interval [t n-1 ,t n The angular frequency of ] is substituted into the formula to calculate t. n-1 The transient current at any given moment can be used to more accurately reflect the transient current change pattern when there is a nonlinear dynamic error between the angular frequency of the phase-locked loop output and the power frequency.

[0117] S207: The particle swarm optimization algorithm is used to optimize the proportional-integral controller parameters of the parameter determination model to obtain the target proportional-integral controller parameters.

[0118] In one exemplary embodiment, step S207 may include: in the event of a fault between the transformer and the public power grid system, determining a target transient electrical performance dataset corresponding to the fault transient process; the target transient electrical performance dataset characterizes the phase-locked loop output angular frequency and dq-axis current measurement values ​​corresponding to multiple sampling times; determining the dq-axis current calculation value corresponding to each sampling time in the target transient electrical performance dataset based on the parameter determination model and the target transient electrical performance dataset; determining a fitting similarity value based on the dq-axis current measurement value and the corresponding dq-axis current calculation value at each sampling time in the target transient electrical performance dataset; the fitting similarity value characterizes the degree of similarity between the dq-axis current measurement value and the corresponding dq-axis current calculation value at each sampling time; iteratively updating the proportional-integral controller parameters in the parameter determination model using a particle swarm optimization algorithm until a preset number of iterations is reached, and determining the convergence value of the fitting similarity value in the preset number of iterations as the target fitting similarity value; and determining the proportional-integral controller parameters corresponding to the target fitting similarity value as the target proportional-integral controller parameters.

[0119] In this embodiment, the target transient electrical performance dataset may specifically include a phase-locked loop output angular frequency dataset and a dq-axis current measurement set; continuing the above example, the time period [t1, t2] of the fault transient process is... n According to the sampling frequency f, it is divided into (t) n -t1)f segment, i.e. [t1, t2], [t2, t3], ..., [t n-1 , t n ], where t1 is the time when the fault occurs, and the phase-locked loop output angular frequency dataset is represented as {ω PLL (t1), ..., ωPLL(t) n-1 )}, where ω PLL (t1) represents the angular frequency in the interval [t1, t2]; ω PLL (tn-1) is the angular frequency of the interval [tn-1, tn]; the set of dq-axis current measurements is represented as {idq(1), ..., idq(n-1)}, where idq(1) represents the dq-axis current of the interval [t1, t2], i dq (n-1) represents the interval [t] n-1 , t n The dq axis current.

[0120] In the specific optimization process of the particle swarm optimization algorithm, the particle swarm can be initialized first. Let's assume the position parameter of the k-th particle is X. k =(x k1 ,x k2 ), where x k1 =k ip xk2 =k ii The velocity of the k-th particle is V. k =(v k1 ,v k2 Each particle is assigned a random initial position and velocity. Continuing with the example above, k∈n-1, meaning each particle corresponds to a sampling time.

[0121] For each sampling time, the randomly given particle position parameters and the aforementioned phase-locked loop output angular frequency ω are used... PLL Substituting into the above formula (7), we can obtain the calculated value of the d-axis current i. d_cal and the calculated value of q-axis current i q_cal In each generation of evolution, the fitting similarity value Z for each particle is calculated. The smaller Z is, the higher the fit between the calculated and measured values, indicating that the obtained k is... ii and k ip To get closer to the true value, specifically, Z can be represented based on the following formula;

[0122]

[0123] Where N represents the number of particles, N can specifically be equal to the above [t] n-1 , t n In the symbol ], n-1; N is a natural number greater than or equal to 1; i d_mea (k) and i q_mea (k) represents the measured values ​​of the k-th d-axis current and q-axis current, respectively, i d_cal (k) and i q_cal (k) represents the calculated values ​​of the k-th d-axis current and q-axis current, respectively.

[0124] Next, the parameter k to be identified is determined based on the fitted similarity value Z. ip ,k ii The position and velocity of the particles can be updated by iterative optimization according to the following formulas (9) and (10).

[0125]

[0126]

[0127] in, Let represent the updated position of the Kth particle after the nth iteration and the updated position of the Kth particle after the (n-1)th iteration, respectively. ω represents the update rate of the Kth particle after the nth iteration and the update rate of the Kth particle after the (n-1)th iteration, respectively; c1 represents the weight coefficient; c2 represents the individual learning factor and c2 represents the collective learning factor. In this embodiment, the empirical values ​​c1 = c2 = 0.6 can be taken; r1 and r2 are random numbers in the interval [0,1] to increase the randomness of the search. This represents the optimal position (individual optimal position) found by the k-th particle. The optimal position for swarm search (swarm optimal position) can be the optimal position of a single particle in each position update, that is, the position parameter corresponding to the minimum value of Z in each position update; similarly, the swarm optimal position can be the optimal position of all particles in each position update, that is, the position parameter of the particle corresponding to the minimum value of Z in each position update of all particles.

[0128] To avoid the particle swarm optimization algorithm getting trapped in local optima, in one exemplary implementation, during the k-th iteration, if the fitting similarity value corresponding to the k-th iteration is greater than or equal to the average fitting similarity value, then the weight coefficients in the particle swarm optimization algorithm are determined as the first preset weight coefficients; the average fitting similarity value is determined based on the fitting similarity values ​​corresponding to each iteration in the k iterations and k; if the fitting similarity value corresponding to the k-th iteration is less than the average fitting similarity value, then the weight coefficients in the particle swarm optimization algorithm are determined as the second preset weight coefficients; k is a natural number.

[0129] The second preset weighting coefficient is determined based on the following formula:

[0130]

[0131] Where, ω min ω represents the third preset weighting coefficient. max Z represents the first preset weighting coefficient; k Z represents the fitting similarity value corresponding to the k-th iteration; min Z represents the minimum fit similarity value in k iterations; avg This represents the average fitting similarity value; the first preset weighting coefficient is greater than the third preset weighting coefficient. Optional, the average fitting similarity value Z... avg The calculation method can be the arithmetic mean, which is the sum of the fitting similarity values ​​corresponding to each of the k iterations, divided by the number of iterations k. Alternatively, a weighted average can be used. No specific method is required.

[0132] Generally, the larger the weight coefficient, the stronger the global optimization ability and the weaker the local optimization ability; conversely, the smaller the weight coefficient, the weaker the global optimization ability and the stronger the local optimization ability. However, the dynamic weight coefficients used in this application can achieve more accurate optimization results than fixed values.

[0133] The particle swarm optimization algorithm described above can be used to analyze k. ii and k ip The optimization process continues until a preset number of iterations is reached. Specifically, Z can be plotted graphically for each iteration. The convergence value of Z (usually the minimum value) is used as the target fitting similarity value, and then the k corresponding to the convergence value of Z is... ii and k ip As the target proportional-integral controller parameter.

[0134] In this embodiment, a direct measurement method can be used to directly obtain the phase-locked loop (PLL) output angular frequency corresponding to the sampling time. However, sometimes it cannot be directly measured. Therefore, in another exemplary embodiment, the PLL output angular frequency corresponding to the sampling time can be determined by the following steps: obtaining the voltage parameters, proportional-integral (PI) controller parameters, and power frequency of the common connection point before and after the fault corresponding to the sampling time; determining the PLL output phase angle change value corresponding to the sampling time based on the voltage parameters, PI controller parameters, and power frequency of the common connection point before and after the fault corresponding to the sampling time; and determining the PLL output angular frequency corresponding to the sampling time based on the PLL output phase angle change value, the power frequency, and the voltage phase angle of the common connection point after the fault.

[0135] Specifically, the principle of calculating the output angular frequency of the phase-locked loop in this application is as follows:

[0136] Please see Figure 4 The diagram shows the transfer function block diagram of the phase-locked loop (PLL); the input voltage e at point PCC of the PLL. abc The three-phase AC voltage is converted into DC (dq-axis component) by the Park transform, and the phase-locked loop dynamically adjusts the phase angle θ of the Park transform PLL output through PI control. PLL Make e q =0 to achieve voltage phase tracking, and its transfer function is:

[0137]

[0138] Where s is the Lagrange operator.

[0139] like Figure 5 The diagram shows the voltage phase change at point PCC, where θ0 is the output phase angle of point PCC after the fault, and E is the voltage vector before the fault. pcc This represents the voltage vector after the fault. Because the phase information output by the phase-locked loop (PLL) undergoes dynamic changes, the original dq axis rotates with the dynamic adjustment of the PLL's PI controller, eventually stabilizing at d1q1. Assume the PLL output phase angle is close to the true phase of the voltage after the fault, i.e., θ0-θ... PLL ≈0 yields:

[0140] e q =E pcc sin(θ0-θ PLL )≈E pcc (θ0-θ PLL (12)

[0141] Combining equations (11) and (12), we can obtain the expression for the output phase angle of the phase-locked loop in the complex frequency domain, which can be expressed as the following formula:

[0142]

[0143] After a fault occurs, the change in the actual phase angle of the system is simulated by introducing a delay term for the phase angle drop. It is assumed that the actual phase of the system decreases exponentially from a to b, i.e., θ0(t) = b + (ba)exp(-At), where A is the delay factor for the voltage phase change. Substituting the Laplace transform of θ0(t) into equation (13), and then performing an inverse Laplace transform, the output phase angle of the phase-locked loop is obtained as θ. PLL (t)=ω0t+b+Δθ PLL (t), which can be specifically expressed as the following formula:

[0144]

[0145]

[0146] Where a is the voltage phase angle of PCC point before the fault occurs, b is the voltage phase angle of PCC point after the fault occurs, and c = ba.

[0147] In this embodiment, the voltage parameters of the common connection point before and after the fault may specifically include a, b, and E as mentioned above, E pcc And A.

[0148] Phase-locked loop output angular frequency ω PLL (t) can be derived from the phase angle θ PLL (t) is obtained by differentiating with respect to time t. As can be seen from equation (14), after a fault occurs, the dynamic response of the phase-locked loop is affected by the phase change of the PCC point before and after the fault, as well as the control parameters of the phase-locked loop.

[0149] Please refer to Table 1, which shows the parameter determination results for the traditional parameter determination model, and Table 2 shows the parameter determination results for the parameter determination model of this application. Given k... ip and k ii The actual values ​​are 0.3 and 6, respectively. The traditional parameter determination model and the parameter determination model of this application are used to determine k. ip The identification errors obtained were 14.23% and 4.83%, respectively, determined using the traditional parameter determination model and the parameter determination model of this application, respectively. iiThe identification errors were determined to be 62.17% and 3.67%, respectively. A comparison shows that the method proposed in this application results in a higher proportionality coefficient k. ip The identification error decreased from 14.23% to 4.83%, and the integral coefficient k ii The identification error was reduced from 62.17% to 3.67%, improving the accuracy of parameter identification.

[0150] Table 1

[0151]

[0152]

[0153] Table 2

[0154] parameter Truth value Identification results Identification error <![CDATA[k ip ]]> 0.3 0.2855 4.83％ <![CDATA[k ii ]]> 6 6.2203 3.67％

[0155] Please see Figure 6 and Figure 7 The figures shown are curves illustrating the trajectory sensitivity of the proportional-integral controller parameters with respect to the dq-axis current. At the instant the fault occurs, the proportional coefficient k... ip The reaction is strong, and the integral coefficient k ii The reaction is relatively small, indicating that the dq-axis currents are proportional to the proportionality coefficient k. ip The sensitivity is high, therefore the proportionality coefficient k ip The identification accuracy is significantly better than that of the integral coefficient k. ii The identification method proposed in this paper improves the low sensitivity coefficient k. ii The accuracy of identification.

[0156] Figure 8 The image shows the time-frequency diagram of the phase-locked loop output. Figure 8 It can be seen that after a fault occurs, the phase angle and angular frequency output by the phase-locked loop undergo a transient transition process.

[0157] Substituting the identification results obtained in Table 1 into the traditional parameter determination model, and the identification results obtained in Table 2 into the parameter determination model proposed in this application, a comparison chart of the calculated and simulated d-axis current values ​​can be obtained (e.g., Figure 9 ).from Figure 9 It can be seen that the fitted current of the traditional parameter determination model has a large error compared with the simulation results. This is because the parameter determination model of the traditional method ignores the nonlinear error of the phase-locked loop (PLL) and cannot describe the underdamped oscillations caused by the dynamic process of the PLL. The fitted current of the parameter determination model proposed in this application shows a high degree of agreement with the simulation results, further demonstrating the effectiveness of the parameter determination method proposed in this application.

[0158] Table 3

[0159]

[0160] Table 3 shows the identification error of the parameter determination method provided in this application under different noise intensities. As can be seen from Table 3, the method proposed in this application has good identification accuracy under different noise intensities, with a proportionality coefficient k... ip The identification error is less than 6%, and the integral coefficient k ii The identification error is less than 4%. This shows that the proposed method has a high noise tolerance.

[0161] However, it should be noted that the above-mentioned parameter identification model is only a preferred embodiment of the present invention and should not be construed as a limitation on the scope of protection of the present invention. Any minor changes and modifications made to the present invention without departing from the concept of the present invention shall fall within the scope of protection of the present invention.

[0162] Corresponding to the parameter determination methods for new energy grid-connected converters provided in the above embodiments, this application also provides a parameter determination device for new energy grid-connected converters. Since the parameter determination device for new energy grid-connected converters provided in this application corresponds to the parameter determination methods for new energy grid-connected converters provided in the above embodiments, the implementation methods of the aforementioned parameter determination methods for new energy grid-connected converters are also applicable to the parameter determination device for new energy grid-connected converters provided in this embodiment, and will not be described in detail in this embodiment.

[0163] Please see Figure 10 The diagram shows a structural schematic of a parameter determination device for a new energy grid-connected converter provided in an embodiment of this application. This device has the function of implementing the parameter determination method for the new energy grid-connected converter described in the above method embodiment. This function can be implemented by hardware or by hardware executing corresponding software. Figure 10 As shown, the parameter determination device 1000 for the new energy grid-connected converter includes:

[0164] Module 1001 is used to construct an initial two-dimensional differential equation about the dq-axis current and time based on the preset command value of the dq-axis current, the equivalent energy storage value between the converter and the point of common coupling, the phase-locked loop output angular frequency, and the proportional-integral controller parameters; the dq-axis current components in the initial two-dimensional differential equation are coupled to each other; the converter includes a proportional-integral controller;

[0165] The reconstruction module 1003 is used to reconstruct the parameters in the initial two-dimensional differential equation to obtain the target two-dimensional differential equation corresponding to two conjugate variables; the two variables are obtained by constructing complex numbers based on the dq-axis current;

[0166] The analytical module 1005 is used to perform analytical processing on the target two-dimensional differential equation based on the linear relationship between the two variables and the dq-axis current, and obtain the parameter determination model. The parameter determination model characterizes the mapping relationship between the dq-axis current and the phase-locked loop output angular frequency and the proportional-integral controller parameters.

[0167] The processing module 1007 is used to optimize the proportional-integral controller parameters of the parameter determination model using the particle swarm optimization algorithm to obtain the target proportional-integral controller parameters.

[0168] In one exemplary embodiment, the analytical module 1005 is used to solve the target two-dimensional differential equation to obtain the analytical equation corresponding to any one of the two variables.

[0169] By performing complex reconstruction on the exponential terms in the analytical equation, the target analytical equation after complex reconstruction is obtained;

[0170] Based on the linear relationship between the two variables and the dq-axis current, the target analytical equation is analyzed to obtain the time-domain analytical equation of the dq-axis current.

[0171] The time-domain analytical equation of the dq-axis current is determined as a parameter determination model.

[0172] In one exemplary implementation, the parameter determination model is the following equation:

[0173]

[0174] Among them, i d (t) represents the d-axis current at time t; i q (t) represents the q-axis current at time t; and These are the commanded values ​​for the d-axis current and q-axis current after the fault, respectively; A1 = sqrt((C 1r ) 2 +(C 1i ) 2 );

[0175] A2 = sqrt((C 2r ) 2 +(C 2i ) 2 ); n1 = arctan(C 1i / C 1r ); n2 = arctan(C 2i / C 2r ); C 1r C 1i and C 2r C 2iThe parameters are obtained by substituting other parameter variables corresponding to the non-abrupt dq-axis current at the time of the fault into the parameter determination model; m1, m2, α and β are parameter variables determined based on the preset command value of dq-axis current, equivalent energy storage value, phase-locked loop output angular frequency and proportional-integral controller parameters.

[0176] In one exemplary embodiment, the processing module 1007 is used to determine a target transient electrical performance dataset corresponding to a fault transient process when a fault exists between the transformer and the public power grid system; the target transient electrical performance dataset represents the phase-locked loop output angular frequency and dq-axis current measurement values ​​corresponding to multiple sampling times;

[0177] The calculated dq-axis current value corresponding to each sampling time in the target transient electrical performance dataset is determined based on the parameter determination model and the target transient electrical performance dataset.

[0178] The fitting similarity value is determined based on the measured dq-axis current values ​​and the corresponding calculated dq-axis current values ​​at each sampling time in the target transient electrical performance dataset; the fitting similarity value characterizes the degree of similarity between the measured dq-axis current values ​​and the corresponding calculated dq-axis current values ​​at each sampling time.

[0179] The parameters of the proportional-integral controller in the model are determined by iteratively updating the parameters using the particle swarm optimization algorithm until a preset number of iterations is reached. The convergence value of the fitting similarity value in the preset number of iterations is then determined as the target fitting similarity value.

[0180] The proportional-integral controller parameters corresponding to the target fitting similarity value are determined as the target proportional-integral controller parameters.

[0181] In one exemplary embodiment, during the k-th iteration, if the fitting similarity value corresponding to the k-th iteration is greater than or equal to the average fitting similarity value, then the weight coefficient in the particle swarm optimization algorithm is determined as the first preset weight coefficient; the average fitting similarity value is determined based on the fitting similarity value corresponding to each iteration in the k iterations and k; if the fitting similarity value corresponding to the k-th iteration is less than the average fitting similarity value, then the weight coefficient in the particle swarm optimization algorithm is determined as the second preset weight coefficient; k is a natural number.

[0182] The second preset weighting coefficient is determined based on the following formula:

[0183]

[0184] Where, ω min ω represents the third preset weighting coefficient. max Z represents the first preset weighting coefficient; k Z represents the fitting similarity value corresponding to the k-th iteration; minZ represents the minimum fit similarity value in k iterations; avg This represents the average fitting similarity value; the first preset weight coefficient is greater than the third preset weight coefficient.

[0185] In one exemplary embodiment, the processing module 1007 is used to acquire the voltage parameters, proportional-integral controller parameters, and power frequency angular frequency of the common connection point before and after the fault corresponding to the sampling time.

[0186] The phase angle change value of the phase-locked loop output corresponding to the sampling time is determined based on the voltage parameters of the common connection point before and after the fault, the proportional-integral controller parameters, and the power frequency angular frequency.

[0187] The phase-locked loop output angular frequency corresponding to the sampling time is determined based on the phase-locked loop output phase angle change value, the power frequency angular frequency, and the voltage phase angle of the common connection point after the fault.

[0188] In one exemplary implementation, the fitting similarity value is calculated based on the following formula:

[0189]

[0190] Where N represents the number of particles; N is a natural number greater than or equal to 1; i d_mea (k) and i q_mea (k) represents the measured values ​​of the k-th d-axis current and q-axis current, respectively, i d_cal (k) and i q_cal (k) represents the calculated values ​​of the k-th d-axis current and q-axis current, respectively.

[0191] In one exemplary implementation, after the nth iteration, the updated position of the Kth particle can be calculated based on the following formula:

[0192]

[0193] The update rate of the Kth particle can be calculated based on the following formula:

[0194]

[0195] in, Let represent the updated position of the Kth particle after the nth iteration and the updated position of the Kth particle after the (n-1)th iteration, respectively. ω represents the update rate of the Kth particle after the nth iteration and the update rate of the Kth particle after the (n-1)th iteration, respectively; ω represents the weight coefficient; c1 represents the individual learning factor; c2 represents the collective learning factor; r1 and r2 are random numbers in the interval [0,1] to increase the randomness of the search; Let be the optimal position found by the k-th particle. This is the optimal position for group search.

[0196] In one exemplary implementation, the initial two-dimensional differential equation is expressed as follows:

[0197]

[0198] Where, Δω=ω0-ω PLL , representing the error value between the power frequency angular frequency and the actual angular frequency of the electrical coupling term; i d and i q These represent the d-axis current and the q-axis current, respectively; i d * and i q * These represent the commanded values ​​for the d-axis current and the q-axis current, respectively; L represents the equivalent inductance between the converter and the point of common coupling, and R represents the equivalent resistance between the converter and the point of common coupling; k ip k ii These are the proportional coefficient and integral coefficient in the parameters of the current inner loop proportional-integral controller, respectively.

[0199] In one exemplary implementation, the two conjugate variables can be represented as x1 and x2, respectively;

[0200] Where, x1=i d +ji q x2 = i d -ji q i d and i q These represent the d-axis current and the q-axis current, respectively.

[0201] In one exemplary implementation, the target two-dimensional differential equation can be expressed as follows:

[0202]

[0203] Where, m=(k ip +R) / L,n=Δω,l=k ii / L,p=k ii / L×i d * q = k ii / L×i q * All parameters are constants in the parameter determination model after construction.

[0204] It should be noted that the apparatus provided in the above embodiments is only illustrated by the division of the above functional modules when implementing its functions. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0205] This application provides an electronic device, which includes a processor and a memory. The memory stores at least one instruction or at least one program. The at least one instruction or at least one program is loaded and executed by the processor to implement any of the parameter determination methods for a new energy grid-connected converter provided in the above method embodiments.

[0206] Memory can be used to store software programs and modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory. Memory can primarily include a program storage area and a data storage area. The program storage area can store the operating system, application programs required for the functions, etc.; the data storage area can store data created based on the use of the device, etc. Furthermore, memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, memory can also include a memory controller to provide the processor with access to the memory.

[0207] Embodiments of this application also provide a computer-readable storage medium, which can be disposed in an electronic device to store at least one instruction or at least one program related to implementing a parameter determination method for a new energy grid-connected converter. The at least one instruction or the at least one program is loaded and executed by the processor to implement any of the parameter determination methods for a new energy grid-connected converter provided in the above-described method embodiments.

[0208] Embodiments of this application also provide a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform any of the parameter determination methods for new energy grid-connected converters provided in the above-described method embodiments.

[0209] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0210] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0211] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0212] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0213] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for determining parameters of a new energy grid-connected converter, characterized in that, include: An initial two-dimensional differential equation concerning the dq-axis current and time is constructed based on the preset command value of the dq-axis current, the equivalent energy storage value between the converter and the point of common coupling, the output angular frequency of the phase-locked loop, and the parameters of the proportional-integral controller; the dq-axis current components in the initial two-dimensional differential equation are coupled to each other; the converter includes a proportional-integral controller; The parameters in the initial two-dimensional differential equation are reconstructed to obtain the target two-dimensional differential equation corresponding to two conjugate variables; The two variables mentioned are derived from complex numbers constructed from the dq-axis currents; Based on the linear relationship between the two variables and the dq-axis current, the target two-dimensional differential equation is analyzed to obtain a parameter determination model; the parameter determination model characterizes the mapping relationship between the dq-axis current, the phase-locked loop output angular frequency, and the proportional-integral controller parameters; The proportional-integral controller parameters of the parameter determination model are optimized using the particle swarm optimization algorithm to obtain the target proportional-integral controller parameters. The parameter determination model is represented by the following equation: in, Represents the d-axis current at time t; Represents the q-axis current at time t; and These are the command values ​​for the d-axis current and q-axis current after the fault, respectively. A 1=sqrt(( C 1r ) 2 +( C 1i ) 2 ); A 2=sqrt(( C 2r ) 2 +( C 2i ) 2 ); n 1 = arctan( C 1i / C 1r ); n 2 = arctan( C 2i / C 2r ); C 1r , C 1i and C 2r , C 2i This refers to the parameters obtained by substituting other parameter variables corresponding to the non-abrupt dq axis current at the time of the fault into the parameter determination model; , , and All of these are parameter variables determined based on the preset command value of the dq-axis current, the equivalent energy storage value, the output angular frequency of the phase-locked loop, and the parameters of the proportional-integral controller. The initial two-dimensional differential equation is expressed as follows: in, , representing the error value between the power frequency angular frequency and the actual electrical coupling term angular frequency; and They represent d shaft current and q shaft current; and They represent d Commanded value of shaft current and q The commanded value of the shaft current; L represents the equivalent inductance between the converter and the point of common coupling, and R represents the equivalent resistance between the converter and the point of common coupling; k ip 、k ii These are the proportional coefficient and integral coefficient in the parameters of the current inner loop proportional-integral controller, respectively.

2. The parameter determination method according to claim 1, characterized in that, The method involves analytically processing the target two-dimensional differential equation based on the linear relationship between the two variables and the dq-axis current to obtain a parameter determination model, including: Solving the target two-dimensional differential equation yields an analytical equation corresponding to any one of the two variables. The exponential terms in the analytical equation are reconstructed using complex numbers to obtain the target analytical equation after complex number reconstruction. Based on the linear relationship between the two variables and the dq-axis current, the target analytical equation is analyzed to obtain the time-domain analytical equation of the dq-axis current; The time-domain analytical equation of the dq-axis current is determined as the parameter determination model.

3. The parameter determination method according to claim 1, characterized in that, The step of using the particle swarm optimization algorithm to optimize the proportional-integral controller parameters of the parameter determination model to obtain the target proportional-integral controller parameters includes: In the event of a fault between the transformer and the public power grid system, a target transient electrical performance dataset corresponding to the fault transient process is determined; the target transient electrical performance dataset characterizes the phase-locked loop output angular frequency and dq-axis current measurement values ​​corresponding to multiple sampling times; the transformer is located between the converter and the public power grid system; Based on the parameters, the model is determined, and the target transient electrical performance dataset is used to determine the calculated dq-axis current value corresponding to each sampling time in the target transient electrical performance dataset. A fitting similarity value is determined based on the measured dq-axis current values ​​and the corresponding calculated dq-axis current values ​​at each sampling time in the target transient electrical performance dataset; the fitting similarity value characterizes the degree of similarity between the measured dq-axis current values ​​and the corresponding calculated dq-axis current values ​​at each sampling time. The parameters of the proportional-integral controller in the model are determined by iteratively updating the parameters using the particle swarm optimization algorithm until a preset number of iterations is reached, and the convergence value of the fitting similarity value in the preset number of iterations is determined as the target fitting similarity value. The proportional-integral controller parameters corresponding to the target fitting similarity value are determined as the target proportional-integral controller parameters.

4. The parameter determination method according to claim 3, characterized in that, In the k-th iteration, if the fitting similarity value corresponding to the k-th iteration is greater than or equal to the average fitting similarity value, then the weight coefficient in the particle swarm optimization algorithm is determined as the first preset weight coefficient; the average fitting similarity value is determined based on the fitting similarity value corresponding to each iteration in the k iterations and k; if the fitting similarity value corresponding to the k-th iteration is less than the average fitting similarity value, then the weight coefficient in the particle swarm optimization algorithm is determined as the second preset weight coefficient; k is a natural number. The second preset weighting coefficient is determined based on the following formula: in, This represents the third preset weighting coefficient; This represents the first preset weighting coefficient; This represents the fitting similarity value corresponding to the k-th iteration; This represents the minimum fitting similarity value in k iterations; The average fitting similarity value is represented; the first preset weight coefficient is greater than the third preset weight coefficient.

5. The parameter determination method according to claim 3, characterized in that, The fitting similarity value is calculated based on the following formula: Where N represents the number of particles; N is a natural number greater than or equal to 1; i d_mea ( k )and i q_mea ( k ) are respectively the first k The measured values ​​of the d-axis current and q-axis current. i d_cal ( k )and i q_cal ( k ) are respectively the first k Calculated values ​​of d-axis current and q-axis current.

6. The parameter determination method according to claim 3, characterized in that, After the nth iteration, the updated position of the Kth particle is calculated based on the following formula: ; The update rate of the Kth particle is calculated based on the following formula: ; in, , Let represent the updated position of the Kth particle after the nth iteration and the updated position of the Kth particle after the (n-1)th iteration, respectively. , Let represent the update rate of the Kth particle after the nth iteration and the update rate of the Kth particle after the (n-1)th iteration, respectively. w Indicates the weighting coefficient; c 1 represents the individual learning factor; c 2 represents the collective learning factor; r 1 and r 2 is a random number within the interval [0,1] to increase the randomness of the search; Let be the optimal position found by the k-th particle. This is the optimal position for group search.

7. The parameter determination method according to claim 3, characterized in that, The method for determining the phase-locked loop output angular frequency corresponding to the sampling time includes: Obtain the voltage parameters, proportional-integral controller parameters, and power frequency angular frequency of the common connection point before and after the fault corresponding to the sampling time; The phase angle change value of the phase-locked loop output corresponding to the sampling time is determined based on the voltage parameters of the common connection point before and after the fault, the proportional-integral controller parameters, and the power frequency angular frequency. The phase-locked loop output angular frequency corresponding to the sampling time is determined based on the phase angle change value of the phase-locked loop output, the power frequency angular frequency, and the voltage phase angle of the common connection point after the fault.

8. The parameter determination method according to claim 1, characterized in that, The two conjugate variables are respectively represented as x 1 and x 2; in, x 1= i d +j i q , x 2= i d -j i q ; and They represent d shaft current and q Axis current.

9. The parameter determination method according to claim 8, characterized in that, The target two-dimensional differential equation is expressed as follows: in, m =( k ip +R ) / L,n = , = k ii / L, p = k ii / L ´ , q = k ii / L ´ All parameters are constants in the parameter determination model after construction.

10. A parameter determination device for a new energy grid-connected converter, characterized in that, The device includes: A construction module is used to construct an initial two-dimensional differential equation about the dq-axis current and time based on the preset command value of the dq-axis current, the equivalent energy storage value between the converter and the point of common coupling, the phase-locked loop output angular frequency, and the proportional-integral controller parameters; the dq-axis current components in the initial two-dimensional differential equation are mutually coupled; the converter includes a proportional-integral controller; The reconstruction module is used to reconstruct the parameters in the initial two-dimensional differential equation to obtain the target two-dimensional differential equation corresponding to two conjugate variables; the two variables are obtained by constructing complex numbers based on the dq-axis current; The analytical module is used to perform analytical processing on the target two-dimensional differential equation based on the linear relationship between the two variables and the dq-axis current to obtain a parameter determination model; the parameter determination model characterizes the mapping relationship between the dq-axis current, the phase-locked loop output angular frequency, and the proportional-integral controller parameters; The processing module is used to optimize the proportional-integral controller parameters of the parameter determination model using the particle swarm optimization algorithm to obtain the target proportional-integral controller parameters. The parameter determination model is represented by the following equation: in, Represents the d-axis current at time t; Represents the q-axis current at time t; and These are the command values ​​for the d-axis current and q-axis current after the fault, respectively. A 1=sqrt(( C 1r ) 2 +( C 1i ) 2 ); A 2=sqrt(( C 2r ) 2 +( C 2i ) 2 ); n 1 = arctan( C 1i / C 1r ); n 2 = arctan( C 2i / C 2r ); C 1r , C 1i and C 2r , C 2i This refers to the parameters obtained by substituting other parameter variables corresponding to the non-abrupt dq axis current at the time of the fault into the parameter determination model; , , and All parameters are determined based on the preset dq-axis current command value, the equivalent energy storage value, the phase-locked loop output angular frequency, and the proportional-integral controller parameters. The initial two-dimensional differential equation is expressed as follows: in, , representing the error value between the power frequency angular frequency and the actual electrical coupling term angular frequency; and They represent d shaft current and q shaft current; and They represent d Commanded value of shaft current and q The commanded value of the shaft current; L represents the equivalent inductance between the converter and the point of common coupling, and R represents the equivalent resistance between the converter and the point of common coupling; k ip 、k ii These are the proportional coefficient and integral coefficient in the parameters of the current inner loop proportional-integral controller, respectively.

11. An electronic device, characterized in that, It includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the parameter determination method for the new energy grid-connected converter as described in any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction or at least one program, which is loaded and executed by a processor to implement the parameter determination method for a new energy grid-connected converter as described in any one of claims 1 to 9.

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