Method and apparatus for optimizing control parameters of permanent magnet synchronous generator

Abstract

A method and apparatus for optimizing control parameters of a permanent magnet synchronous generator, relating to the field of power device maintenance. The method comprises: performing impedance modeling on a permanent magnet synchronous generator on the basis of a topology structure and a control structure of the permanent magnet synchronous generator, and analyzing impedance characteristics (S101); establishing a generator transient reactive power-voltage model on the basis of dynamic response characteristics of a phase-locked loop, and analyzing the effect of controller bandwidth and damping-ratio parameters on transient characteristics (S102); and optimizing control parameters of the permanent magnet synchronous generator on the basis of the impedance characteristics and the transient characteristics (S103). The method can optimize control parameters of a permanent magnet synchronous generator by taking into account both impedance characteristics and fault ride-through characteristics of the permanent magnet synchronous generator.

Description

A method and apparatus for optimizing control parameters of a direct-drive wind turbine. This application claims priority to Chinese Patent Application No. 202411736003.1, filed on November 29, 2024, the entire contents of which are incorporated herein by reference. Technical Field This application relates to the field of power equipment maintenance, specifically a method and device for optimizing control parameters of a direct-drive wind turbine. Background Technology As the installed capacity of new energy sources such as wind power gradually increases, the scale of wind farms connected to the grid is further expanding. Because the grid strength in areas where new energy sources converge is generally weak, and the "negative resistance" characteristic of new energy generator converters is prominent in certain frequency ranges, large-scale connection of new energy generators to weak grids faces a more severe risk of broadband oscillations, seriously threatening the safe and stable operation of the grid. However, by optimizing the control parameters of new energy generators, it is possible to increase the phase margin of the generators in certain frequency ranges, optimize the impedance characteristics of the generators, and improve the stability of the grid-connected system, enabling the grid to operate safely and stably. Meanwhile, regarding fault ride-through characteristics, when a voltage dip occurs in the system, injecting a certain amount of reactive power into the system can ensure that grid-connected renewable energy units can continue operating without disconnecting from the grid when entering the Low Voltage Ride-Through (LVRT) state. However, when renewable energy units are connected to a weak grid, transient overvoltage problems caused by the instantaneous reactive power surplus at the point of common coupling (PCC) after a system fault can easily occur, leading to large-scale disconnection of renewable energy units. Similarly, by optimizing the control parameters of renewable energy units, transient overvoltages at the unit terminals can be suppressed, the reactive power response speed of the units can be improved, the transient characteristics of the units can be optimized, and the risk of renewable energy units disconnecting from the grid can be reduced. Therefore, the impedance and fault ride-through characteristics of direct-drive wind turbines (PMSGs) are both affected by their control parameters. Currently, the impedance and fault ride-through characteristics of PMSGs must meet impedance phase requirements and grid requirements, respectively, which has become a primary prerequisite for grid connection. To this end, the North China Grid Dispatch Center and the Hebei Provincial Dispatch Center, based on the original national standard requirements for fault ride-through characteristics of new energy units, have added impedance phase requirements for new energy unit sites connected to the Zhangxiong UHV and Shangdu regions, to ensure the safe and stable operation of the system after the new energy unit sites are connected to the grid. Therefore, it is necessary to study a PMSG control parameter optimization method that takes into account both impedance and fault ride-through characteristics, based on engineering practice. In recent years, both domestic and international scholars have achieved certain results in research on PMSG impedance modeling and parameter optimization, as well as transient modeling and parameter optimization. However, existing research still has the following shortcomings: (1) Regarding the impedance characteristics of PMSG, most optimizations aim to suppress unit oscillations, with few involving parameter optimization based on the actual engineering unit impedance phase requirements. (2) Regarding the transient characteristics of PMSG, most analyses focus on the influence of unit low-voltage parameters on transient overvoltage, while few studies analyze the influence of unit controller bandwidth and damping ratio parameters on transient reactive voltage and provide parameter optimization suggestions for suppressing transient overvoltage. (3) Current analyses of PMSG control parameter optimization mainly focus on the characteristics of a single unit, lacking a PMSG control parameter optimization theory that takes into account both impedance characteristics and fault ride-through characteristics. Considering the dual requirements of impedance phase and the power grid, parameter optimization that only considers the single characteristic of the unit may no longer be applicable to complex application scenarios. This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section. Summary of the Invention To address the problems in the prior art, this application provides a method and apparatus for optimizing the control parameters of a direct-drive wind turbine, which can optimize the control parameters of the direct-drive wind turbine by taking into account both its impedance characteristics and fault ride-through characteristics. To solve the above-mentioned technical problems, this application provides the following technical solution: In a first aspect, this application provides a method for optimizing control parameters of a direct-drive wind turbine generator, including: Impedance modeling of the direct-drive wind turbine is performed based on its topology and control structure, and the influence of the bandwidth and damping ratio parameters of each controller on the impedance characteristics is analyzed. A transient reactive voltage model of the unit is established based on the dynamic response characteristics of the phase-locked loop, and the influence of the bandwidth and damping ratio parameters of each controller on the transient characteristics is analyzed. The control parameters of the direct-drive wind turbine are optimized based on the impedance characteristics and transient characteristics. Furthermore, the impedance modeling of the direct-drive wind turbine based on its topology and control structure, and the analysis of the influence of the bandwidth and damping ratio parameters of each controller on the impedance characteristics, include: Based on the topology and control structure, generate voltage and current relationship expressions for the grid-side converter, passive filter, and common connection point; Based on the aforementioned topology and control structure, the voltage component expression of the DC bus, the sine and cosine frequency domain expression of the phase-locked loop, and the relationship expression between the grid-side converter current and the modulation signal are generated. Substituting the voltage component expression, the sine and cosine frequency domain expression, and the relationship expression between the grid-side converter current and the modulation signal into the voltage-current relationship expression, the positive and negative sequence analytical impedance model of the direct-drive wind turbine is obtained. Furthermore, the impedance modeling of the direct-drive wind turbine based on its topology and control structure, and the analysis of the influence of the bandwidth and damping ratio parameters of each controller on the impedance characteristics, include: Based on the topology and control structure, determine the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller. Based on the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller and the positive and negative sequence analytical impedance model, the effects of the DC voltage loop control bandwidth and damping ratio, the phase-locked loop control bandwidth and damping ratio, and the current loop control bandwidth and damping ratio on the impedance characteristics are analyzed, and impedance characteristic optimization suggestions are generated. Furthermore, the transient reactive power and voltage model of the unit is established based on the dynamic response characteristics of the phase-locked loop, and the influence of the bandwidth and damping ratio parameters of each controller on the transient characteristics is analyzed, including: Based on the dynamic response characteristics of the phase-locked loop, determine the filter model expression, terminal voltage expression, voltage-current relationship expression at the output port of the grid-side converter, and grid voltage expression before and after the fault. By substituting the filter model of the grid-side converter, the generator terminal voltage expression, and the grid voltage before and after the fault into the voltage-current relationship expression at the output port of the grid-side converter, the transient reactive voltage model of the unit is obtained. Furthermore, the transient reactive power and voltage model of the unit is established based on the dynamic response characteristics of the phase-locked loop, and the influence of the bandwidth and damping ratio parameters of each controller on the transient characteristics is analyzed, including: Based on the topology and control structure, determine the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller. Based on the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller and the transient reactive voltage model of the unit, the influence of the current inner loop control bandwidth and damping ratio, and the phase-locked loop control bandwidth and damping ratio on the transient characteristics are analyzed, and transient characteristic optimization suggestions are generated. Furthermore, the optimization of the control parameters of the direct-drive wind turbine based on the impedance characteristics and the transient characteristics includes: Construct an optimization objective function based on the impedance characteristic optimization suggestions and transient characteristic optimization suggestions; Recommended control parameters for the direct-drive wind turbine are generated using an improved multi-objective particle swarm optimization algorithm and the aforementioned optimization objective function. Furthermore, the step of generating recommended control parameters for the direct-drive wind turbine using the improved multi-objective particle swarm optimization algorithm and the optimization objective function includes: Initialize the algorithm parameters, particle swarm velocity information, and position information of the multi-objective particle swarm optimization algorithm; The initial fitness value of each particle is calculated based on the optimization objective function, and the particle's external profile, individual historical best particle, and global best particle are initialized. The following steps are executed iteratively until the preset maximum number of iterations is reached: update the velocity and position information of the particles using an improved multi-objective particle swarm optimization algorithm; calculate the fitness value of the updated particle population according to a preset particle mutation strategy, and update the external files of the particles according to a preset dominance relationship; update the globally optimal particle in a random selection manner according to the maximum capacity of the external files of the particles, and determine the historically optimal particle of the individual according to the preset dominance relationship. Determine whether the number of executions has reached the maximum number of iterations; if so, output the globally optimal particle currently saved in the particle external file and the individual historical optimal particle to obtain a non-dominated solution set; wherein, the non-dominated solution set includes the recommended control parameters of the direct-drive wind turbine. Secondly, this application provides a device for optimizing control parameters of a direct-drive wind turbine generator, comprising: Impedance characteristic analysis unit is used to perform impedance modeling of the direct-drive wind turbine based on its topology and control structure, and to analyze the influence of the bandwidth and damping ratio parameters of each controller on the impedance characteristics. The transient characteristic analysis unit is used to establish a transient reactive voltage model of the unit based on the dynamic response characteristics of the phase-locked loop, and to analyze the influence of the bandwidth and damping ratio parameters of each controller on the transient characteristics. The control parameter optimization unit is used to optimize the control parameters of the direct-drive wind turbine based on the impedance characteristics and the transient characteristics. Furthermore, the impedance characteristic analysis unit includes: The first expression generation module is used to generate voltage and current relationship expressions at the grid-side converter, passive filter and common connection point based on the topology and control structure. The second expression generation module is used to generate the voltage component expression of the DC bus, the sine and cosine frequency domain expression of the phase-locked loop, and the relationship expression between the grid-side converter current and the modulation signal based on the topology and control structure. The impedance model construction module is used to substitute the voltage component expression, the sine and cosine frequency domain expression, and the relationship expression between the grid-side converter current and the modulation signal into the voltage-current relationship expression to obtain the positive and negative sequence analytical impedance model of the direct-drive wind turbine. Furthermore, the impedance characteristic analysis unit includes: The first three-parameter relationship generation module is used to determine the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller according to the topology and control structure. The impedance optimization suggestion generation module is used to analyze the influence of DC voltage loop control bandwidth and damping ratio, phase-locked loop control bandwidth and damping ratio, and current loop control bandwidth and damping ratio on the impedance characteristics based on the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller and the positive and negative sequence analytical impedance model, and generate impedance characteristic optimization suggestions. Furthermore, the transient characteristic analysis unit includes: The third expression generation module is used to determine the filter model expression of the grid-side converter, the terminal voltage expression, the voltage-current relationship expression of the grid-side converter output port, and the grid voltage expression before and after the fault, based on the dynamic response characteristics of the phase-locked loop. The transient model construction module is used to substitute the filter model of the grid-side converter, the terminal voltage expression, and the grid voltage before and after the fault into the voltage-current relationship expression of the output port of the grid-side converter to obtain the transient reactive voltage model of the unit. Furthermore, the transient characteristic analysis unit includes: The second three-parameter relationship generation module is used to determine the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller according to the topology and control structure. The transient optimization suggestion generation module is used to analyze the influence of the current inner loop control bandwidth and damping ratio, and the phase-locked loop control bandwidth and damping ratio on the transient characteristics based on the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller and the transient reactive voltage model of the unit, and generate transient characteristic optimization suggestions. Furthermore, the control parameter optimization unit includes: The objective function construction module is used to construct the optimization objective function based on the impedance characteristic optimization suggestion and the transient characteristic optimization suggestion. The control parameter recommendation module is used to generate recommended control parameters for the direct-drive wind turbine using an improved multi-objective particle swarm optimization algorithm and the optimization objective function. Furthermore, the control parameter recommendation module includes: The algorithm initialization module is used to initialize the algorithm parameters, particle swarm velocity information, and position information of the multi-objective particle swarm optimization algorithm. The particle initialization module is used to calculate the initial fitness value of each particle according to the optimization objective function, and to initialize the particle's external file, the individual's historical best particle, and the global best particle. The iterative execution module is used to iteratively execute the following steps until a preset maximum number of iterations is reached: update the velocity and position information of particles using an improved multi-objective particle swarm optimization algorithm; calculate the fitness value of the updated particle population according to a preset particle mutation strategy, and update the particle external files according to a preset dominance relationship; update the globally optimal particle in a random selection manner according to the maximum capacity of the particle external files, and determine the individual historical optimal particle according to the preset dominance relationship. The non-dominated solution set generation module is used to determine whether the number of executions has reached the maximum number of iterations; if so, it outputs the globally optimal particle currently saved in the particle external file and the individual historical optimal particle to obtain the non-dominated solution set; wherein, the non-dominated solution set includes the recommended control parameters of the direct-drive wind turbine. Thirdly, this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the direct-drive wind turbine control parameter optimization method. Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the direct-drive wind turbine control parameter optimization method. Fifthly, this application provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the direct-drive wind turbine control parameter optimization method. To address the problems in existing technologies, the direct-drive wind turbine control parameter optimization method and apparatus provided in this application can analyze the influence of the unit's control bandwidth parameters and damping ratio parameters on its impedance and transient characteristics by establishing impedance and transient voltage models of the direct-drive wind turbine. Based on the influence law of unit parameters on the impedance and transient characteristics of the direct-drive wind turbine, the main influencing factors are determined. Using phase-locked loop control bandwidth and damping ratio as decision variables, and employing an improved multi-objective particle swarm optimization algorithm, the control parameters of the direct-drive wind turbine are optimized to balance impedance and fault ride-through characteristics. This comprehensive optimization of the impedance and fault ride-through characteristics of the direct-drive wind turbine has certain engineering guiding significance. Attached Figure Description To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 is a flowchart of the control parameter optimization method for direct-drive wind turbines that takes into account both impedance characteristics and fault ride-through characteristics in an embodiment of this application. Figure 2 shows the main circuit topology and control structure of the grid-side converter of the direct-drive wind turbine in this embodiment of the application. Figure 3 is a comparison of the positive sequence (left a) and negative sequence (right b) impedance analysis curves and frequency sweep results in the embodiments of this application; Figure 4 shows the f in the embodiment of this application. gic (left a) and ξ gic (Right b) Effect on PMSG impedance characteristics; Figure 5 shows the f in the embodiment of this application. pll (left a) and ξ pll (Right b) Effect on PMSG impedance characteristics; Figure 6 shows the f in the embodiment of this application. dvc (left a) and ξ dvc (Right b) Effect on PMSG impedance characteristics; Figure 7 shows the converted main circuit topology of the direct-drive wind turbine in the embodiment of this application. Figure 8 is a comparison chart of the transient reactive voltage model and simulation results in the embodiments of this application; Figure 9 shows the f in the embodiment of this application. gic (left a) and ξ gic (Right b) Effect on PMSG transient characteristics; Figure 10 shows the f in the embodiment of this application. pll (left a) and ξ pll (Right b) Effect on PMSG transient characteristics; Figure 11 shows a comparison of two ω-decreasing strategies in the embodiments of this application; Figure 12 is a flowchart of the improved multi-objective particle swarm optimization algorithm in the embodiments of this application; Figure 13 shows the optimization results of ZDT1 and ZDT3 in the embodiments of this application; Figure 14 shows the optimization results of the improved multi-objective particle swarm optimization algorithm for equation (18) in the embodiments of this application; Figure 15 shows the optimized impedance characteristic (left a) and transient characteristic (right b) parameter schemes (point A) in the embodiments of this application; Figure 16 is a flowchart of the control parameter optimization method for direct-drive wind turbine generators in an embodiment of this application; Figure 17 is one of the flowcharts for impedance modeling and impedance characteristic analysis in the embodiments of this application; Figure 18 is a second flowchart of impedance modeling and impedance characteristic analysis in an embodiment of this application; Figure 19 is one of the flowcharts for transient modeling and transient characteristic analysis in the embodiments of this application; Figure 20 is a second flowchart of transient modeling and transient characteristic analysis in an embodiment of this application; Figure 21 is a flowchart of optimizing the control parameters of the direct-drive wind turbine in an embodiment of this application; Figure 22 is a flowchart of generating recommended control parameters for direct-drive wind turbine generators in an embodiment of this application; Figure 23 is a structural diagram of the direct-drive wind turbine control parameter optimization device in an embodiment of this application; Figure 24 is one of the structural diagrams of the impedance characteristic analysis unit in the embodiments of this application; Figure 25 is a second structural diagram of the impedance characteristic analysis unit in an embodiment of this application; Figure 26 is one of the structural diagrams of the transient characteristic analysis unit in the embodiments of this application; Figure 27 is a second structural diagram of the transient characteristic analysis unit in an embodiment of this application; Figure 28 is a structural diagram of the control parameter optimization unit in an embodiment of this application; Figure 29 is a structural diagram of the control parameter recommendation module in an embodiment of this application; Figure 30 is a schematic diagram of the structure of the electronic device in the embodiment of this application. Detailed Implementation To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention. The information collected in the technical solution of this application is information and data authorized by the user or fully authorized by all parties. The collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant countries and regions, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding operation portals are provided for users to choose to authorize or refuse. Provide users with corresponding operation entry points, allowing them to choose to agree to or reject the automated decision results; if the user chooses to reject, the process will proceed to the expert decision-making process. In one embodiment, referring to Figure 16, in order to balance the impedance characteristics and fault ride-through characteristics of direct-drive wind turbines, the control parameters of direct-drive wind turbines are optimized. This application provides a method for optimizing the control parameters of direct-drive wind turbines, including: S101: Based on the topology and control structure of the direct-drive wind turbine, impedance modeling is performed on the direct-drive wind turbine, and the influence of the bandwidth and damping ratio parameters of each controller on the impedance characteristics is analyzed. S102: Based on the dynamic response characteristics of the phase-locked loop, establish a transient reactive voltage model of the unit and analyze the influence of the bandwidth and damping ratio parameters of each controller on the transient characteristics. S103: Optimize the control parameters of the direct-drive wind turbine based on the impedance characteristics and the transient characteristics. Understandably, this invention, based on the control bandwidth and damping ratio parameters of the PMSG, comprehensively analyzes the impact of parameter variations on the aforementioned characteristics of the PMSG from two aspects: impedance characteristics and fault ride-through characteristics of direct-drive wind turbines. Then, with the optimization objectives of meeting the unit's impedance phase requirements and suppressing transient overvoltages, an improved Multi-objective Particle Swarm Optimization (MOPSO) algorithm is employed to achieve PMSG control parameter optimization that considers both impedance characteristics and fault ride-through characteristics. This comprehensively optimizes both the unit's impedance characteristics and fault ride-through characteristics, addressing the lack of PMSG control parameter optimization methods that simultaneously consider impedance characteristics and fault ride-through characteristics in existing practical engineering projects. In practical implementation, based on a specific PMSG power station, firstly, a positive and negative sequence impedance model of the generating unit is established to analyze the influence of the unit's control bandwidth and damping ratio parameters on its impedance characteristics. Secondly, a transient reactive voltage model considering the dynamic response characteristics of the phase-locked loop (PLL) is established to analyze the influence of the unit's control bandwidth and damping ratio parameters on its transient characteristics. Finally, based on the influence law of the unit parameters on the characteristics of both parameters, the main influencing factors are determined, with the PLL bandwidth f as an example. pll Damping ratio ξ pll Using the parameters as decision variables, a multi-objective optimization function is constructed. A PMSG control parameter optimization method based on an improved multi-objective particle swarm optimization algorithm is proposed, which takes into account both impedance characteristics and fault ride-through characteristics. The correctness of the theoretical results is verified by simulation. Figure 1 is a flowchart of a PMSG control parameter optimization method that considers both impedance characteristics and fault ride-through characteristics provided by the present invention. Referring to Figure 1, the specific steps of the PMSG control parameter optimization method that considers both impedance characteristics and fault ride-through characteristics described in the present invention are as follows: S1: PMSG impedance modeling and impedance characteristic analysis. Based on a PMSG power station, and according to the typical topology and control structure of PMSG, a unit sequence impedance model was established, and the influence of parameters such as unit control bandwidth and damping ratio on its impedance characteristics was analyzed. S2: PMSG transient modeling and its transient characteristics analysis. Based on the typical topology and control structure of PMSG, and considering the dynamic response process of PLL, a transient reactive voltage model of the unit was established, and the influence of parameters such as the unit control bandwidth and damping ratio on its transient characteristics was analyzed. S3: A PMSG control parameter optimization method that takes into account both impedance characteristics and fault ride-through characteristics. The optimization objectives are to meet the unit impedance phase requirements and suppress the unit transient overvoltage, with f pll ξ pll The parameters are decision variables. A PMSG control parameter optimization method based on an improved multi-objective particle swarm optimization algorithm is proposed, which takes into account both impedance characteristics and fault ride-through characteristics, and comprehensively optimizes the unit's impedance characteristics and transient characteristics. As described above, the direct-drive wind turbine control parameter optimization method provided in this application can analyze the influence of the unit's control bandwidth parameter and damping ratio parameter on its impedance and transient characteristics by establishing the impedance model and transient reactive voltage model of the direct-drive wind turbine. Based on the influence law of the unit parameters on the impedance and transient characteristics of the direct-drive wind turbine, and using the phase-locked loop control bandwidth and damping ratio as decision variables, the improved multi-objective particle swarm optimization algorithm is adopted to achieve the optimization of the control parameters of the direct-drive wind turbine that takes into account both impedance characteristics and fault ride-through characteristics. The method comprehensively optimizes the impedance characteristics and fault ride-through characteristics of the direct-drive wind turbine, which has certain engineering guiding significance. In one embodiment, referring to Figure 17, the impedance modeling of the direct-drive wind turbine based on its topology and control structure, and the analysis of its impedance characteristics, include: S201: Generate voltage-current relationship expressions for the grid-side converter, passive filter, and common connection point based on the topology and control structure; S202: Generate the voltage component expression of the DC bus, the sine and cosine frequency domain expression of the phase-locked loop, and the relationship expression between the grid-side converter current and the modulation signal based on the topology and control structure. S203: Substitute the voltage component expression, the sine and cosine frequency domain expression, and the relationship expression between the grid-side converter current and the modulation signal into the voltage-current relationship expression to obtain the positive and negative sequence analytical impedance model of the direct-drive wind turbine. It is understood that the above steps S201 to S203 can be implemented according to the following process. S1: PMSG impedance modeling and impedance characteristic analysis. S11: The PMSG impedance analytical model is established using the harmonic linearization method and verified by impedance scanning. Taking a PMSG wind farm as an example, the GSC main circuit topology and control block diagram of the PMSG after the direct-drive wind farm is aggregated and equivalent are shown in Figure 2. The PMSG impedance phase requirements are shown in Table 1. Table 1 PMSG Impedance Phase Requirements Referring to Figure 2, considering the DC voltage outer loop, PLL, current inner loop, and dq axis voltage feedforward of the grid side converter (GSC), the positive and negative sequence impedance models of the PMSG are established using the harmonic linearization method and verified by impedance scanning. In Figure 2, i g For the high-voltage side of the unit's box-type transformer, N is the number of PMSG units; k1 and k2 are the transformer turns ratios; R1, R2, L1, and L2 are the resistances and inductances of the 35kV and 110kV transmission lines, respectively; L3 and R... sc BRK represent the current-limiting impedance, short-circuit impedance, and short-circuit switch in the fault-generating device, respectively; KI n =i max K is the maximum allowable current for the unit, and I is taken as 1.5; n U is the rated current of the unit; g This is the per-unit value of the voltage amplitude at the PCC point. The voltage and current relationships at the GSC output port, LC filter, and PCC point in Figure 2 are analyzed and modeled using the symmetrical component method: In the formula, v ia v ib v ic These are the three-phase output port voltages of the GSC, respectively; i a i b i c and v a v b v c These are the three-phase AC current and voltage of the PCC in the PMSG, respectively; L f R f C f For filter inductors, resistors, and capacitors; v ia =m a k m v dc m a For the a-phase AC modulation signal of GSC, k m v is the modulation coefficient. dc denoted as DC bus voltage, and s as a frequency domain operator. The DC bus serves as the connecting link between the generator-side and grid-side converters. Based on the equality of instantaneous power on both the AC and DC sides of the unit, the frequency [0, ±(f)] can be obtained. p -f1),±(fn DC voltage component under +f1)]: In the formula, C dc V dc0 For DC bus capacitance and steady-state voltage values; I load V1 and f1 are the steady-state output current on the machine side; V1 and f1 are the amplitude and frequency of the fundamental positive sequence voltage, respectively. p f p , These are the amplitude, frequency, and initial phase of the harmonic positive sequence voltage, V. n f n , These represent the amplitude, frequency, and initial phase of the harmonic negative sequence voltage, respectively. I1 is the amplitude of the fundamental positive sequence current, I... p , These are the amplitude and initial phase of the harmonic current and voltage, respectively, I. n , These are the amplitude and initial phase of the harmonic negative sequence current, respectively. V1, V p V n 、I1、I p I n These are the frequency domain components of the positive and negative sequence voltage and current, respectively. V1 = V1 / 2, I1 = I1 / 2, V1* and I1* are conjugate functions of V1 and I1. The positive and negative sequence harmonic voltages of the PCC will cause disturbances in the PLL output phase angle. When there are harmonic frequency disturbance voltages at the grid connection point, the PLL output angle includes not only the angle θ1 of the grid fundamental frequency positive sequence voltage component, but also the disturbance angle Δθ of the harmonic voltage. At this time, the grid angle θ obtained by PLL control is... PLL Represented as θ PLL =θ1+Δθ. Then, based on the harmonic linearization method, the relationship between harmonic voltage and disturbance angle is analyzed to obtain the sine and cosine frequency domain expressions for the PLL. In the formula, H PLL (s) is the transfer function of the PLL controller, T PLL (s)=H PLL (s) / [1+V1H PLL (s)];H PLL (s)=(k pllp +k plli / s) / s,k pllp kplli These are the proportional-integral coefficients of the PLL. Based on the control circuit shown in Figure 2, the relationship between the PCC current and the modulation signal is obtained using the harmonic linearization method, and a small-signal model of the current loop and modulation is established: m a =m d cosθ-m q sinθ m d =H i (S)[H v (S)(v dc -v dcr )-i d ]-K d i q +k f v d (4) m q =H i (s)[i qr -i q ]+K d i d +k f v q In the formula, v d v q i d i q m d m q These represent the voltage, current, and modulation signal in the dq coordinate system, respectively; i dr i qr This is the reference value for the dq-axis current; v dc v dcr DC bus voltage and its reference value; K d k is the decoupling coefficient for AC current control. f H is the dq-axis voltage feedforward coefficient; v (s), H i (s) are the transfer functions of the DC voltage outer loop and the current inner loop controller, respectively, H i (s)=k ip +k ii / s, H v (s)=k vp +k vi / s,k ip k ii k vp k vi These are the proportional-integral coefficients for the inner current loop and the outer voltage loop, respectively. In summary, by combining the main circuit model of PMSG and the frequency domain components of each key component, the positive and negative sequence impedance model of PMSG can be obtained after analytical derivation and simplification, as shown in Equation (5). In the formula, the fundamental angular frequency ω1 = 2πf1 (f1 is the fundamental frequency); M1 is the steady-state fundamental frequency component of the modulating signal, M1 = (M dr ±j M qr ) / 2, and M1=(V1+sL f I1) / k m V dc0 M1* and M1 are conjugates, M dr M qr This is the DC component of the dq-axis modulated signal. Figure 3 is a comparison of the PMSG impedance analysis curve and the frequency sweep result. The comparison results show that the PMSG positive and negative sequence analytical impedances are basically consistent with the frequency sweep results, verifying the accuracy of the obtained PMSG positive and negative sequence analytical impedance models. Furthermore, it can be found that the PMSG positive sequence impedance does not meet the unit impedance phase requirements at some frequencies within the [41, 100] Hz band, while the positive sequence impedance meets the unit impedance phase requirements at the remaining frequency bands and all frequency bands of the PMSG negative sequence impedance. Considering practical engineering needs, this invention focuses on the frequency bands with the highest priority unit impedance phase requirements in Table 1, namely [1, 30] Hz, [61, 100] Hz, and [201, 1000] Hz. Since the impedance characteristics of the unit in the high-frequency band are mainly dominated by filter parameters, the control parameters have little impact on them. Therefore, this invention subsequently mainly analyzes the influence of PMSG control parameters on the positive sequence impedance characteristics within the frequency bands [1, 30] Hz and [61, 100] Hz. As can be seen from the above description, the direct-drive wind turbine control parameter optimization method provided in this application can perform impedance modeling of the direct-drive wind turbine based on its topology and control structure, and analyze the influence of the bandwidth and damping ratio parameters of each controller on the impedance characteristics. In one embodiment, referring to Figure 18, the impedance modeling of the direct-drive wind turbine based on its topology and control structure, and the analysis of the influence of the bandwidth and damping ratio parameters of each controller on the impedance characteristics, includes: S301: Determine the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller based on the topology and control structure. S302: Based on the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller and the positive and negative sequence analytical impedance model, analyze the influence of the DC voltage loop control bandwidth and damping ratio, the phase-locked loop control bandwidth and damping ratio, and the current loop control bandwidth and damping ratio on the impedance characteristics, and generate impedance characteristic optimization suggestions. Understandably, the next step, S12, is to analyze the impact of changes in control parameters on the impedance characteristics of the PMSG based on the PMSG control bandwidth and damping ratio parameters, and to provide parameter optimization suggestions that are conducive to meeting the impedance phase requirements of actual engineering units. A1: Control bandwidth, damping ratio and PI parameter conversion relationship. In the typical PMSG structure, the closed-loop transfer functions of the DC voltage loop, PLL, and current loop controller can all be equivalent to second-order systems, thus obtaining the relationship between the bandwidth and damping ratio of each controller and its PI parameters, as shown in Equation (6). The subsequent analysis of the influencing factors on the impedance and transient characteristics of the PMSG will be based on the control bandwidth and damping ratio parameters of the inner current loop, PLL, and outer voltage loop. In the formula, ω v =2πf dvc f dvc and ξ dvc For the DC voltage loop control bandwidth and damping ratio; ω p =2πf pll f pll and ξ pll For PLL control bandwidth and damping ratio; ω i =2πf gic f gic and ξ gic Herein lies the current loop bandwidth and damping ratio. A2: Analysis of factors affecting the impedance characteristics of PMSG. A21: Current inner loop control bandwidth and damping ratio parameters. In order to analyze f gic and ξ gic The effect of parameters on the phase frequency characteristics of internal impedance in the PMSG band [1,30]Hz and [61,100]Hz is achieved by changing fgic and ξ respectively. gic The control parameters were used to obtain the Bode plot of the unit's impedance characteristics as a function of the parameters, as shown in Figure 4. In Figure 4(a), f gic With values ​​of 100Hz, 160Hz, and 280Hz respectively, in Figure 4(b), ξ gic The values ​​are 0.45, 1.15, and 1.85 respectively.

[0154] As shown in Figure 4, with f gic Decrease, ξ gic As f increases, the amplitude-frequency response of the PMSG impedance shifts downward in the frequency bands [1,30] Hz and [61,100] Hz; the phase-frequency response shifts downward in the frequency band [1,30] Hz and upward in the frequency band [61,100] Hz. The results show that as f... gic Decrease, ξ gicThe increase in frequency range allows the unit to meet impedance phase requirements within the [1,30]Hz and [61,100]Hz bands, increasing the system phase margin, optimizing the PMSG impedance characteristics, and improving system stability. A22: PLL control bandwidth and damping ratio parameters. In order to analyze f pll and ξ pll The effect of parameters on the phase frequency characteristics of internal impedance in the PMSG band [1,30]Hz and [61,100]Hz, respectively, by changing f pll and ξ pll The control parameters were used to obtain the Bode plot of the unit's impedance characteristics as a function of the parameters, as shown in Figure 5. In Figure 5(a), f pll When the values ​​are 3Hz, 12Hz, and 25Hz respectively, in Figure 5(b), ξ pll The values ​​are 0.45, 1.15, and 1.85 respectively.

[0157] As shown in Figure 5, with f pll and ξ pll As f decreases, the unit's impedance amplitude-frequency characteristic shifts upward within the frequency band [f1-fpll, f1+fpll] Hz; the phase-frequency characteristic shifts downward within the frequency band [1, 30] Hz and upward within the frequency band [61, 100] Hz. The results show that as f... pll and ξ pll The reduction in frequency increases the range of frequencies within the [1,30]Hz and [61,100]Hz bands where the unit meets the impedance phase requirements, increases the system phase margin, optimizes the PMSG impedance characteristics, and improves system stability. A23: Voltage outer loop control bandwidth and damping ratio parameters. In order to analyze f dvc and ξ dvc The effect of parameters on the phase frequency characteristics of internal impedance in the PMSG band [1,30]Hz and [61,100]Hz, respectively, by changing f dvc and ξ dvc The control parameters were used to obtain the Bode plot of the unit's impedance characteristics as a function of the parameters, as shown in Figure 6. In part (a) of Figure 6, f dvc When the values ​​are taken as 10Hz, 25Hz, and 40Hz respectively, in part (b) of Figure 6, ξ dvc The values ​​are 0.45, 1.15, and 1.85 respectively.

[0160] As shown in Figure 6, with f dvc and ξ dvc As f increases, the amplitude-frequency response of the PMSG impedance shifts upwards within the frequency band [30, 70] Hz. dvc Decrease, ξ dvcAs f increases, the unit's impedance phase-frequency characteristic shifts downward in the frequency band [1, 30] Hz and upward in the frequency band [61, 100] Hz; however, the change in the unit's impedance phase-frequency characteristic curve is very small. The results indicate that as f increases... dvc Decrease, ξ dvc Although the range of frequencies within the [1,30]Hz and [61,100]Hz bands where the unit meets impedance phase requirements increases, f dvc and ξ dvc The parameters have little effect on the phase frequency characteristics of the PMSG impedance and have a negligible effect on optimizing the unit impedance characteristics. In summary, by increasing ξ gic ξ dvc Decrease f gic f pll ξ pll f dvc The PMSG impedance phase-frequency characteristic shifts downwards in the frequency band [1, 30] Hz and upwards in the frequency band [61, 100] Hz. This indicates that the frequency range within which the PMSG meets the impedance phase requirements in the frequency bands [1, 30] Hz and [61, 100] Hz increases, thereby increasing the system phase margin, optimizing the PMSG impedance characteristics, and improving system stability. Wherein, f pll ξ pll The most significant impact, f gic ξ gic Secondly, f dvc ξ dvc Minimum. As can be seen from the above description, the direct-drive wind turbine control parameter optimization method provided in this application can perform impedance modeling of the direct-drive wind turbine based on its topology and control structure, and analyze the influence of the bandwidth and damping ratio parameters of each controller on the impedance characteristics. In one embodiment, referring to Figure 19, the establishment of a transient reactive power voltage model of the unit based on the dynamic response characteristics of the phase-locked loop, and the analysis of the influence of the bandwidth and damping ratio parameters of each controller on the transient characteristics, includes: S401: Determine the filter model expression, terminal voltage expression, voltage-current relationship expression at the output port of the grid-side converter, and grid voltage expression before and after the fault based on the dynamic response characteristics of the phase-locked loop. S402: Substitute the filter model of the grid-side converter, the terminal voltage expression, and the grid voltage before and after the fault into the voltage-current relationship expression of the output port of the grid-side converter to obtain the transient reactive voltage model of the unit. It is understandable that steps S401 to S402 can be implemented in the following way. S2: PMSG transient modeling and its transient characteristics analysis. S21: Establish a PMSG transient reactive voltage model that considers the dynamic response characteristics of the phase-locked loop and verify it through time-domain simulation. In Figure 2, path "0" and path "1" represent the PMSG steady-state control mode and low-voltage ride-through control mode, respectively. The former operates according to the dispatch reactive power command, while the latter provides emergency reactive power support to the grid. The PMSG switches between steady-state and transient operating states, and its reactive current command is determined by the grid voltage amplitude. Considering the PLL, the inner current loop, and the dq-axis voltage feedforward, a transient reactive voltage model for the PMSG is established. To more intuitively illustrate the modeling process, the grid voltage level, transformer impedance, and line impedance in the main circuit topology of Figure 2 are uniformly converted to the PMSG side, as shown in Figure 7. Among them, i ia i ib i ic These are the three-phase output port currents of the GSC, respectively; v RC_abc i RC_abc For the voltage and current of the RC filter branch; R g L g These are the equivalent resistance and inductance on the grid side, respectively. Based on the equivalent main circuit topology in Figure 7, the voltage and current at the GSC output port and the grid voltage can be calculated, which can be expressed in the dq rotating coordinate system as follows: In the formula, v id v iq i id i iq These are the dq components of the terminal voltage and current, respectively; Δv Ld Δv Lq The voltage drop component of the filter inductor dq; v Cd v Cq i RCd i RCq The voltage and current dq components of the RC filter branch. The mathematical model of the filter in the grid-side converter can be expressed as: In the formula, ω g This is the angular frequency of the grid voltage. According to the control block diagram in Figure 2, the PMSG terminal voltage can be expressed as: A real power grid can be equivalent to an ideal voltage source E. s With R g L g If the grid voltage is connected in series, the dq-axis component can be expressed as: In the formula, e d '、e q'This represents the dq component of the grid voltage after the fault occurs. Considering that the dynamic response time of a PLL is longer than that of the inner current loop, and that it takes 20–100 ms (depending on the PLL structure and parameters) to track the actual phase transition, a phase-locked error will occur between the PLL output phase and the actual phase when a fault occurs on the grid side. This will lead to active and reactive power control coupling, thereby affecting the transient characteristics of the PMSG. Based on the phase-locked loop control circuit in Figure 1, let the phase of the input PLL voltage be θ0 and the amplitude be E. m The PLL output phase expression can be obtained as follows: Assuming that after the fault occurs, v abc A voltage phase jump of Δθ is generated, which, when converted to a complex frequency domain, becomes a step signal of Δθ(s) = Δθ / s. By solving for the response Δθ(s), the difference between the PLL output phase and the actual phase can be obtained. PLL (s) is: For Δθ PLL (s) Taking the inverse Laplace transform, we can obtain the phase-locked error Δθ corresponding to the response in the time domain. PLL Therefore, when a short-circuit fault occurs, the PCC voltage amplitude drops instantaneously to E. m ', voltage v dq 'Can be represented as: Because the phase-locked loop deviation was sufficiently small before the fault occurred, θ0-θ PLL It is approximately 0. Therefore, the grid voltage component v after the fault occurs... dq It can be simplified as follows: By transforming equations (7) to (10) and (14) into the complex frequency domain and solving them simultaneously, we can obtain the complex frequency domain solutions for the voltage and current of the RC filter branch and the dq components of the grid current: In the formula, v 0Cd v 0Cq i 0id i 0iq These are the initial values ​​of the filter capacitor voltage and the inductor current along the dq axis, respectively; i 0d i 0q The initial value of the grid inductor current dq axis; i 1dr (s), i 1qr (s) represents the command value in the complex frequency domain of the dq axis after the PMSG grid voltage changes. Taking the inverse Laplace transform of the above equation, we can obtain the time-domain solutions of the filter capacitor voltage, inductor current, and grid inductor current on the dq axis. Substituting these solutions into equation (7), we can obtain the time-domain expressions for the PMSG grid-side voltage and current v.d (t), v q (t), i d (t), i q (t). Therefore, the PCC voltage of the PMSG and the transient reactive power output to the grid can be expressed as: Figure 8 is a comparison between the analytical model and simulation results of the PMSG transient reactive voltage. The comparison results show that the PMSG transient reactive voltage response analytical model and the PSCAD electromagnetic transient simulation results are basically consistent, verifying the accuracy of the PMSG transient reactive voltage response analytical model established in this invention. As can be seen from the above description, the direct-drive wind turbine control parameter optimization method provided in this application can establish a transient reactive voltage model of the unit based on the dynamic response characteristics of the phase-locked loop, and analyze the influence of the bandwidth and damping ratio parameters of each controller on the transient characteristics. In one embodiment, referring to Figure 20, the establishment of a transient reactive power voltage model of the unit based on the dynamic response characteristics of the phase-locked loop, and the analysis of the influence of the bandwidth and damping ratio parameters of each controller on the transient characteristics, includes: S501: Determine the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller based on the topology and control structure. S502: Based on the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller and the transient reactive voltage model of the unit, analyze the influence of the current inner loop control bandwidth and damping ratio, and the phase-locked loop control bandwidth and damping ratio on the transient characteristics, and generate transient characteristic optimization suggestions. Understandably, the next step, S22, is to analyze the impact of changes in control parameters on the transient characteristics of the PMSG based on the PMSG control bandwidth and damping ratio parameters, and to provide parameter optimization suggestions that are beneficial to suppressing transient overvoltages of the unit. A1: Analysis of factors influencing the transient characteristics of PMSG. A11: Current inner loop control bandwidth and damping ratio parameters. In order to analyze f gic and ξ gic The effect of parameters on the transient overvoltage-free characteristics of PMSG, by changing f respectively gic and ξ gic The control parameters were used to obtain the waveform diagram of the unit's transient characteristics as a function of the parameters, as shown in Figure 9. Where f gic and ξ gic The parameter values ​​are consistent with those in A21 of S12. As shown in Figure 9(a), at the fault recovery time, the PMSG transient reactive power overshoot increases with f gic As f increases, the transient overvoltage of the unit increases, and the transient overvoltage of the unit increases with f. gicThe value increases and decreases, but neither change is significant. The results indicate that increasing f... gic Although it can suppress transient overvoltage at the PMSG terminal, it will increase the reactive power overshoot of the unit, and has little effect on the transient reactive voltage characteristics and reactive power response speed of the unit. As shown in Figure 9(b), at the fault recovery time, the PMSG transient reactive power overshoot increases with ξ. gic The voltage increases and decreases, while the unit's transient overvoltage decreases with ξ. gic Increasing ξ results in no significant change for either ξ or ξ. gic While it can reduce PMSG reactive power overshoot, it will increase the unit's transient overvoltage. Similarly, ξ gic The impact on the unit's transient characteristics and reactive power response speed is also relatively small. A12: PLL control bandwidth and damping ratio parameters. In order to analyze f pll and ξ pll The effect of parameters on the transient overvoltage-free characteristics of PMSG, by changing f respectively pll and ξ pll The control parameters were used to obtain the waveform diagram of the unit's transient characteristics as a function of the parameters, as shown in Figure 10. Where f pll and ξ pll The parameter values ​​are consistent with those in A22 of S12. As shown in Figure 10, at the fault recovery time, both the transient reactive power overshoot and its transient overvoltage of the PMSG increase with f. pll ξ pll It increases and decreases. Furthermore, compared to f... pll Increase from 3Hz to 12Hz or ξ pll From 0.45 to 1.15, as f pll Increase from 12Hz to 25Hz or ξ pll As the value increased from 1.15 to 1.85, the degree of change in the unit's transient overvoltage-free characteristics decreased significantly. The results show that, although f pll ξ pll The effect on the transient characteristics of the unit is significant, and can be addressed by increasing f. pll ξ pll This effectively suppresses transient overvoltage at the generator terminals, reduces reactive power overshoot, and improves the reactive power response speed of the unit. However, f pll ξ pll The ability to optimize the transient reactive voltage characteristics of generator units is limited. In summary, although the ability of unit control parameters to optimize its transient characteristics is limited, it is still possible to improve them by reducing ξ. gic and increase f gic fpll ξ pll This is to suppress transient overvoltage at the PMSG terminal, reduce reactive power overshoot, and optimize the transient reactive voltage characteristics of the unit. Among these, the PLL has a greater impact on the transient reactive voltage characteristics of the unit than the inner current loop, and compared to f... gic ξ gic It has no significant impact on the reactive power response speed of the unit, by increasing f pll ξ pll However, it can effectively improve the reactive power response speed of the unit. As can be seen from the above description, the direct-drive wind turbine control parameter optimization method provided in this application can establish a transient reactive voltage model of the unit based on the dynamic response characteristics of the phase-locked loop, and analyze the influence of the bandwidth and damping ratio parameters of each controller on the transient characteristics. In one embodiment, referring to Figure 21, optimizing the control parameters of the direct-drive wind turbine based on the impedance characteristics and the transient characteristics includes: S601: Construct an optimization objective function based on the impedance characteristic optimization suggestions and transient characteristic optimization suggestions; S602: The recommended control parameters for the direct-drive wind turbine are generated using an improved multi-objective particle swarm optimization algorithm and the optimization objective function. It is understood that steps S601 to S602 can be implemented by the following methods. S3: A PMSG control parameter optimization method that takes into account both impedance characteristics and fault ride-through characteristics. S31: Parameter optimization method. Based on the analysis of PMSG impedance characteristics and transient characteristics in S12 and S22, it can be seen that: increasing ξ gic Decrease f gic f pll ξ pll While this can increase the frequency range within the [1,30]Hz and [61,100]Hz bands where the PMSG meets impedance phase requirements and optimize the unit's impedance characteristics, it also exacerbates the transient overvoltage at the PMSG terminals, reduces the unit's reactive power response speed, and deteriorates the unit's transient reactive voltage characteristics. Furthermore, in optimizing the PMSG's impedance and transient characteristics, f pll ξ pll The control parameters play a major role in the influence of both. The results show that, within certain frequency bands, the PMSG impedance characteristics and fault ride-through characteristics may have a certain degree of mutual constraint relationship. This indicates that the control parameters optimized for the unit's impedance (fault ride-through) characteristics may degrade its fault ride-through (impedance) characteristics, resulting in a "one-sided" problem. Therefore, this invention aims to balance the impedance and transient characteristics of the PMSG, with the optimization goals of meeting the unit's impedance phase requirements and suppressing transient overvoltages, using f pll ξ pll The parameters are decision variables. A PMSG control parameter optimization method based on an improved multi-objective particle swarm optimization algorithm is proposed, which takes into account both impedance characteristics and fault ride-through characteristics, and comprehensively optimizes the unit's impedance characteristics and transient characteristics. S32: Construct the objective function. (1) Impedance characteristics When ξ increases gic Decrease f gic f pll ξ pll The phase-frequency characteristics of the internal impedance in the [1,30] Hz and [61,100] Hz frequency bands both change in the direction of meeting the impedance phase angle requirements in Table 1. Therefore, by analyzing and optimizing the parameter values ​​of the phase-frequency characteristics of the internal impedance in the [61,100] Hz frequency band, the internal impedance characteristics in the [1,30] Hz frequency band can also be optimized. Since the impedance phase frequency characteristic curve in the frequency band [61,100]Hz shifts upward as a whole during unit parameter optimization, and in order to improve the running speed of the subsequent improved multi-objective particle swarm optimization algorithm, this paper takes the minimum absolute value of the unit impedance phase angle at a frequency of 61Hz as the objective function f1(x) for impedance characteristic optimization. (2) Transient characteristics To suppress transient overvoltages in the unit, this paper uses the minimum value of the transient overvoltage as the objective function f2(x) for optimizing the transient characteristics. Therefore, the mathematical expression of the multi-objective optimization problem in this paper can be described as follows: In the formula, x i Let f be the decision vector, representing f respectively. pll ξ pll The parameter X represents the two-dimensional decision space. Considering unit f pll ξ pll The initial parameters are 5Hz, 1.4, and f. pll ξ pll Due to the limitations of parameter optimization capabilities, and in order to improve the running speed of subsequent improved multi-objective particle swarm optimization algorithms, the value space of the decision vector is taken as [4,15] and [0.2,2], respectively. S33: An improved multi-objective particle swarm optimization algorithm. A1: Algorithm Basics—PSO Algorithm The Prototype Search (PSO) algorithm is a swarm intelligence optimization algorithm based on information sharing among particles. Each particle represents a potential solution to the problem and has a fitness value determined by a fitness function. The PSO algorithm continuously updates the position and velocity of particles by interacting with the individual historical best (Pbest) and global best (Gbest) values, thus iteratively searching for the optimal solution within the feasible solution space by tracking the behavior of the best particle. In the PSO algorithm, the particle velocity and position update strategies are shown in equations (19) and (20). In addition, considering the convergence of the PSO algorithm, boundary conditions should be imposed on the particle velocity and position. That is, when the particle velocity and position exceed their respective set boundary ranges, the particle velocity and position at this time should be adjusted to the boundary values. In the formula, i is the particle number; ω is the inertia weight; k is the current iteration number; r1 and r2 are random numbers between [0,1]; c1 and c2 are the individual and collective learning factors, respectively, with a value of 1.5. A2: Population Update For the MOPSO algorithm, changing the value of ω can significantly improve the algorithm's search performance. A larger ω is beneficial for global search, while a smaller ω is beneficial for local search. Therefore, a larger ω is needed in the early stages of algorithm iteration to enhance global search capability, while a smaller ω is needed in the later stages for accurate local search. Currently, the most commonly used method to improve ω is based on a linearly decreasing inertial weight strategy, as shown in equation (21). However, this method maintains a constant ω-decreasing slope, and if good particles are not generated in the initial iteration, it is prone to getting trapped in local optima. To overcome this problem, this paper adopts a dynamic inertia weight based on a cosine-decreasing strategy, as shown in Equation (22). Its ω change gradually increases first and then gradually decreases later, thereby improving the performance of the algorithm, as shown in Figure 11. In the formula, C is the control factor, with a value of 1.2; ω max ω min The maximum and minimum inertia weights are 0.9 and 0.4, respectively; T is the maximum number of iterations, with a value of 100. A3: External File Selection Compared to the Pareto algorithm (PSO) for single-objective optimization problems, the MOPSO algorithm for multi-objective optimization does not yield a unique optimal solution, but rather a set of non-dominant Pareto solutions. Therefore, an appropriate strategy is needed to obtain the global optimum. This paper employs an adaptive grid method to select elite individuals in the algorithm and to update external files. The specific steps are as follows: (1) Using f1(x) and f2(x) as objective functions, the objective function space is divided into M×M grids, and the maximum and minimum boundaries of each dimension in the objective space are determined, thereby calculating the grid modulus: (2) Traverse all particles in the external archive, number and record the grid where the particles are located, calculate the particle density in the grid, and thus determine the probability of each grid being selected. Among them, i Nm Let be the number of particles in the m-th grid, and also the grid crowding degree. Therefore, the smaller the grid crowding degree, the greater the probability that a particle will be selected. To improve the algorithm's running speed, it's necessary to limit the number of particles in the external archive. Specifically, when the maximum capacity is exceeded, the top 50% of particles with the highest mesh density are deleted. In this paper, the maximum capacity of the external archive is set to 150. (3) The grid and optimal particle positions are determined by roulette wheel selection to avoid the MOPSO algorithm getting trapped in local optima. At the same time, to avoid the algorithm repeatedly searching for boundary positions, when the population exceeds the limit boundary, the algorithm does not use the update method of directly assigning boundary values ​​to individuals, but instead uses the update method of assigning the corresponding values ​​that exceed the limit to the particle positions. A4: Mutation Strategy Since the search space of the population in the MOPSO algorithm will continuously shrink during the iteration process, in order to maintain the diversity and randomness of the particles, this paper introduces the mutation mechanism in the genetic algorithm to divide the particle population into three parts: no mutation, uniform mutation, and non-uniform mutation. This ensures the global search capability of the algorithm in the early stage and that it can gradually converge to the vicinity of the optimal solution in the later stage. A5: Algorithm Flow This article takes f pll ξ pll The parameters are decision variables. The model to be optimized is constructed according to equations (1) to (18), and the improved MOPSO algorithm is used for calculation. The specific flowchart of the algorithm execution is shown in Figure 12. As can be seen from the above description, the direct-drive wind turbine control parameter optimization method provided in this application can optimize the control parameters of the direct-drive wind turbine based on the impedance characteristics and the transient characteristics. In one embodiment, referring to Figure 22, the step of generating recommended control parameters for the direct-drive wind turbine using an improved multi-objective particle swarm optimization algorithm and the optimization objective function includes: S701: Initialize the algorithm parameters, particle swarm velocity information, and position information of the multi-objective particle swarm optimization algorithm; S702: Calculate the initial fitness value of each particle according to the optimization objective function, and initialize the particle's external profile, the individual's historical best particle, and the global best particle; S703: Iteratively execute the following steps until the preset maximum number of iterations is reached: Update the velocity and position information of the particles using an improved multi-objective particle swarm optimization algorithm; calculate the fitness value of the updated particle population according to a preset particle mutation strategy, and update the external files of the particles according to a preset dominance relationship; update the globally optimal particle in a random selection manner according to the maximum capacity of the external files of the particles, and determine the historically optimal particle of the individual according to the preset dominance relationship. S704: Determine whether the number of executions has reached the maximum number of iterations; if so, output the globally optimal particle and the individual historical optimal particle currently saved in the particle external file to obtain a non-dominated solution set; wherein, the non-dominated solution set includes the recommended control parameters of the direct-drive wind turbine. Understandably, the following steps can be followed during implementation. Step 1: Load the objective function in equation (18), initialize the velocity and position information of the particle population, and set the relevant parameters of the algorithm; Step 2: Calculate the fitness values ​​f1(x) and f2(x) for each initial particle, and initialize the external archive, Pbest, and Gbest; Step 3: Update the particle velocity and position information according to equations (19), (20) and (22), while considering the particle mutation strategy; Step 4: Calculate the fitness value of the updated particle population, and update and maintain the external files according to the Pareto dominance relationship; Step 5: Determine if the external file has reached the maximum capacity limit. If so, delete the top 50% of particles with the highest mesh density. If it has not exceeded the maximum limit, proceed directly to Step 6. Step 6: In the external archive, Gbest is randomly selected and updated using a roulette wheel method, and Pbest corresponding to each particle is selected based on the Pareto dominance relationship; Step 7: Determine if the algorithm has reached the maximum number of iterations. If yes, output the external file, i.e., the obtained non-dominated solution set. Otherwise, jump to Step 3 and continue the loop. As can be seen from the above description, the direct-drive wind turbine control parameter optimization method provided in this application can generate recommended control parameters for the direct-drive wind turbine using an improved multi-objective particle swarm optimization algorithm and the optimization objective function. The following is a specific example: ①Algorithm verification To test the feasibility and effectiveness of the improved multi-objective particle swarm optimization algorithm proposed in this paper, two test functions, ZDT1 and ZDT3, were selected for verification. The expressions for ZDT1 and ZDT3 are shown below. (1) ZDT1 expression (2) ZDT3 expression In the formula, the decision variable x i The value range of is [0,1], and n is 30. For the ZDT test function, the improved multi-objective particle swarm optimization algorithm was initialized with a population size of 100, a maximum number of iterations of 150, and a maximum external file capacity of 200. The results of the improved multi-objective particle swarm optimization algorithm on the test functions ZDT1 and ZDT3 are shown in Figure 13. As shown in Figure 13, the Pareto solutions of the ZDT1 and ZDT3 test functions obtained based on the improved multi-objective particle swarm optimization algorithm have a good overlap with their respective true Pareto optimal fronts, which verifies the feasibility and effectiveness of the improved multi-objective particle swarm optimization algorithm proposed in this paper. ②Results Analysis This paper focuses on the optimization objective function (18) for the impedance and transient characteristics of PMSG, with f pll ξ pll The parameters are decision variables, and their value spaces are selected as [4,15] and [0.2,2], respectively. Then, an improved multi-objective particle swarm optimization algorithm is used to perform parameter optimization analysis. For optimizing the objective function (18), the improved multi-objective particle swarm optimization algorithm is set to initialize the population size to 100, the maximum number of iterations to 100, and the maximum capacity of the external file to 150. The parameter optimization results of the improved multi-objective particle swarm optimization algorithm for the objective function (18) are shown in Figure 14. As shown in Figure 14, in the improved multi-objective particle swarm optimization algorithm, as f pll ξ pll The fitness values ​​of the objective functions f1(x) and f2(x) show a negative correlation when the parameters are changed. This indicates that within certain frequency bands, the PMSG impedance characteristics and fault ride-through characteristics do indeed have a certain degree of mutual constraint, which can easily lead to a "choosing one over the other" problem. Furthermore, based on the fitness values ​​of the objective function under the initial system parameters: f1(x) = 1.5945 (91.36°) and f2(x) = 1.1428, and the optimization objective of the objective function in this paper, a parameter optimization scheme that takes into account both the PMSG impedance characteristics and transient characteristics can be obtained in Figure 14 (the part enclosed by the red rectangle). Therefore, in order to optimize the unit impedance characteristics as much as possible while suppressing PMSG transient overvoltage, this paper selects the parameters at point A in Figure 14 as a parameter optimization scheme that takes into account both PMSG impedance characteristics and transient characteristics. At point A, f pll ξ pll The parameters are 11.1702 and 0.3015, respectively. At this time, the fitness values ​​of the objective function are: f1(x) = 1.2869 (73.73°) and f2(x) = 1.0863. Both of them have decreased compared with the initial parameters. The waveforms of the unit impedance characteristics and transient characteristics are shown in Figure 15. As shown in Figure 15(a), compared to the initial parameters, the impedance amplitude of the PMSG under the optimized parameter scheme at point A is reduced in the frequency band [20, 90] Hz. Meanwhile, the unit impedance phase angle at 61 Hz increases from -91.36° to -73.73°, ensuring that the unit fully meets the impedance phase requirements in the frequency band [61, 100] Hz, increasing the system phase margin, and optimizing the PMSG impedance characteristics. As shown in Figure 15(b), compared to the initial parameters, the parameters at point A, ξ pll The reduction causes reactive power fluctuations in the PMSG during fault recovery and increases the transient recovery time; while f pll The increase in voltage increases the reactive power response speed of the PMSG during fault recovery, and reduces the transient overvoltage amplitude from 1.141 pu to 1.082 pu, effectively suppressing the transient overvoltage at the PMSG terminal. In summary, the parameter optimization scheme at point A satisfies the optimization objective of suppressing transient overvoltages at the unit terminals while effectively optimizing its impedance characteristics, thus comprehensively optimizing both the unit's impedance and transient characteristics. The results show that the improved multi-objective particle swarm optimization algorithm proposed in this paper can achieve PMSG parameter optimization that balances impedance characteristics and fault ride-through characteristics. In summary, the PMSG control parameter optimization method proposed in this invention, which takes into account both impedance characteristics and fault ride-through characteristics based on an improved multi-objective particle swarm optimization algorithm, solves the aforementioned problem of "choosing one over the other" in generating units. It comprehensively optimizes the impedance characteristics and transient characteristics of generating units, providing a theoretical basis for the stable operation control of systems after new energy power plants are connected to the grid, and has certain engineering guiding significance. In summary: 1) In this embodiment of the application, based on the main circuit topology and control structure of the PMSG unit, an impedance model of the PMSG and a transient reactive voltage analytical model of the PMSG considering the dynamic response characteristics of the phase-locked loop are established. The accuracy of the impedance analytical model and the transient analytical model are verified by impedance frequency sweep and time-domain simulation, respectively. 2) In this embodiment, the impact of changes in control parameters on the PMSG characteristics is comprehensively analyzed from two aspects: the unit's impedance characteristics and fault ride-through characteristics, by controlling the PMSG bandwidth and damping ratio parameters. Then, with the optimization objectives of meeting the unit's impedance phase requirements and suppressing transient overvoltages, f... pll ξ pll For decision variables, a PMSG control parameter optimization method based on an improved multi-objective particle swarm optimization algorithm is proposed, which takes into account both impedance characteristics and fault ride-through characteristics, and comprehensively optimizes the unit's impedance characteristics and fault ride-through characteristics. Based on the same inventive concept, this application also provides a direct-drive wind turbine control parameter optimization device, which can be used to implement the method described in the above embodiments, as described in the following embodiments. Since the principle of the direct-drive wind turbine control parameter optimization device in solving the problem is similar to that of the direct-drive wind turbine control parameter optimization method, the implementation of the direct-drive wind turbine control parameter optimization device can refer to the implementation of the software performance benchmark determination method, and will not be repeated. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated. In one embodiment, referring to Figure 23, in order to optimize the control parameters of a direct-drive wind turbine while taking into account both its impedance characteristics and fault ride-through characteristics, this application provides a direct-drive wind turbine control parameter optimization device, including: an impedance characteristic analysis unit 2301, a transient characteristic analysis unit 2302, and a control parameter optimization unit 2303. Impedance characteristic analysis unit 2301 is used to perform impedance modeling of the direct-drive wind turbine based on the topology and control structure of the direct-drive wind turbine, and to analyze the influence of the bandwidth and damping ratio parameters of each controller on the impedance characteristics. The transient characteristic analysis unit 2302 is used to establish a transient reactive voltage model of the unit based on the dynamic response characteristics of the phase-locked loop, and to analyze the influence of the bandwidth and damping ratio parameters of each controller on the transient characteristics. The control parameter optimization unit 2303 is used to optimize the control parameters of the direct-drive wind turbine based on the impedance characteristics and the transient characteristics. In one embodiment, referring to FIG24, the impedance characteristic analysis unit 2301 includes: a first expression generation module 2401, a second expression generation module 2402, and an impedance model construction module 2403. The first expression generation module 2401 is used to generate voltage and current relationship expressions for the grid-side converter, passive filter and common connection point according to the topology and control structure. The second expression generation module 2402 is used to generate the voltage component expression of the DC bus, the sine and cosine frequency domain expression of the phase-locked loop, and the relationship expression between the grid-side converter current and the modulation signal according to the topology and control structure. Impedance model construction module 2403 is used to substitute the voltage component expression, the sine and cosine frequency domain expression, and the relationship expression between the grid-side converter current and the modulation signal into the voltage-current relationship expression to obtain the positive and negative sequence analytical impedance model of the direct-drive wind turbine. In one embodiment, referring to FIG25, the impedance characteristic analysis unit 2301 includes: a first three-parameter relationship generation module 2501 and an impedance optimization suggestion generation module 2502. The first three-parameter relationship generation module 2501 is used to determine the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller according to the topology and control structure. The impedance optimization suggestion generation module 2502 is used to analyze the influence of DC voltage loop control bandwidth and damping ratio, phase-locked loop control bandwidth and damping ratio, and current loop control bandwidth and damping ratio on the impedance characteristics based on the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller and the positive and negative sequence analytical impedance model, and generate impedance characteristic optimization suggestions. In one embodiment, referring to FIG26, the transient characteristic analysis unit 2302 includes: a third expression generation module 2601 and a transient model construction module 2602. The third expression generation module 2601 is used to determine the filter model expression of the grid-side converter, the terminal voltage expression, the voltage-current relationship expression of the grid-side converter output port, and the grid voltage expression before and after the fault, based on the dynamic response characteristics of the phase-locked loop. The transient model construction module 2602 is used to substitute the filter model of the grid-side converter, the terminal voltage expression, and the grid voltage before and after the fault into the voltage-current relationship expression of the output port of the grid-side converter to obtain the transient reactive voltage model of the unit. In one embodiment, referring to FIG27, the transient characteristic analysis unit 2302 includes: a second three-parameter relationship generation module 2701 and a transient optimization suggestion generation module 2702. The second three-parameter relationship generation module 2701 is used to determine the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller according to the topology and control structure. The transient optimization suggestion generation module 2702 is used to analyze the influence of the current inner loop control bandwidth and damping ratio, and the phase-locked loop control bandwidth and damping ratio on the transient characteristics based on the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller and the transient reactive voltage model of the unit, and generate transient characteristic optimization suggestions. In one embodiment, referring to FIG28, the control parameter optimization unit 2303 includes: an objective function construction module 2801 and a control parameter recommendation module 2802. Objective function construction module 2801 is used to construct an optimization objective function based on impedance characteristic optimization suggestions and transient characteristic optimization suggestions; The control parameter recommendation module 2802 is used to generate recommended control parameters for the direct-drive wind turbine using an improved multi-objective particle swarm optimization algorithm and the optimization objective function. In one embodiment, referring to FIG29, the control parameter recommendation module 2801 includes: an algorithm initialization module 2901, a particle initialization module 2902, an iterative execution module 2903, and a non-dominated solution set generation module 2904. The algorithm initialization module 2901 is used to initialize the algorithm parameters, particle swarm velocity information, and position information of the multi-objective particle swarm optimization algorithm. The particle initialization module 2902 is used to calculate the initial fitness value of each particle according to the optimization objective function, and to initialize the particle external file, the individual historical best particle and the global best particle; The iterative execution module 2903 is used to iteratively execute the following steps until a preset maximum number of iterations is reached: update the velocity and position information of particles using an improved multi-objective particle swarm optimization algorithm; calculate the fitness value of the updated particle population according to a preset particle mutation strategy, and update the particle external file according to a preset dominance relationship; update the globally optimal particle in a random selection manner according to the maximum capacity of the particle external file, and determine the individual historical optimal particle according to the preset dominance relationship. The non-dominated solution set generation module 2904 is used to determine whether the number of executions has reached the maximum number of iterations; if so, it outputs the globally optimal particle and the individual historical optimal particle currently saved in the particle external file to obtain the non-dominated solution set; wherein, the non-dominated solution set includes the recommended control parameters of the direct-drive wind turbine. From a hardware perspective, in order to balance the impedance characteristics and fault ride-through characteristics of direct-drive wind turbines and optimize the control parameters of direct-drive wind turbines, this application provides an embodiment of an electronic device for implementing all or part of the control parameter optimization method for direct-drive wind turbines. The electronic device specifically includes the following components: The system comprises a processor, a memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to realize information transmission between the direct-drive wind turbine control parameter optimization device and core business systems, user terminals, and related databases and other related devices; the logic controller can be a desktop computer, tablet computer, or mobile terminal, etc., and this embodiment is not limited to these. In this embodiment, the logic controller can be implemented with reference to the embodiments of the direct-drive wind turbine control parameter optimization method and the direct-drive wind turbine control parameter optimization device in the embodiments, the contents of which are incorporated herein, and repeated details will not be described again. It is understood that the user terminal may include smartphones, tablet computers, network set-top boxes, portable computers, desktop computers, personal digital assistants (PDAs), in-vehicle devices, smart wearable devices, etc. Among these, the smart wearable devices may include smart glasses, smartwatches, smart bracelets, etc. In practical applications, some of the control parameter optimization methods for direct-drive wind turbines can be executed on the electronic device side as described above, or all operations can be completed in the client device. The choice can be made based on the processing power of the client device and the limitations of the user's usage scenario. This application does not impose any limitations on this. If all operations are completed in the client device, the client device may further include a processor. The aforementioned client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission. The server may include a server on the task scheduling center side; in other implementation scenarios, it may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a distributed server structure. Figure 30 is a schematic block diagram of the system configuration of an electronic device 9600 according to an embodiment of this application. As shown in Figure 30, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that Figure 30 is exemplary; other types of structures may also be used to supplement or replace this structure to achieve telecommunications functions or other functions. In one embodiment, the function of optimizing control parameters for a direct-drive wind turbine can be integrated into a central processing unit 9100. The central processing unit 9100 can be configured to perform the following control: S101: Based on the topology and control structure of the direct-drive wind turbine, impedance modeling is performed on the direct-drive wind turbine, and the influence of the bandwidth and damping ratio parameters of each controller on the impedance characteristics is analyzed. S102: Based on the dynamic response characteristics of the phase-locked loop, establish a transient reactive voltage model of the unit and analyze the influence of the bandwidth and damping ratio parameters of each controller on the transient characteristics. S103: Optimize the control parameters of the direct-drive wind turbine based on the impedance characteristics and the transient characteristics. As described above, the direct-drive wind turbine control parameter optimization method and apparatus provided in this application can analyze the influence of the unit's control bandwidth parameter and damping ratio parameter on its impedance and transient characteristics by establishing the impedance model and transient reactive voltage model of the direct-drive wind turbine. Then, based on the influence law of the unit parameters on the impedance and transient characteristics of the direct-drive wind turbine, the main influencing factors are determined. Using the phase-locked loop control bandwidth and damping ratio as decision variables, and employing an improved multi-objective particle swarm optimization algorithm, the control parameters of the direct-drive wind turbine are optimized to take into account both impedance characteristics and fault ride-through characteristics. This comprehensively optimizes the impedance characteristics and fault ride-through characteristics of the direct-drive wind turbine, and has certain engineering guiding significance. In another embodiment, the direct-drive wind turbine control parameter optimization device can be configured separately from the central processing unit 9100. For example, the data composite transmission device for direct-drive wind turbine control parameter optimization can be configured as a chip connected to the central processing unit 9100, and the function of the direct-drive wind turbine control parameter optimization method can be realized through the control of the central processing unit. As shown in Figure 30, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily include all the components shown in Figure 30; furthermore, the electronic device 9600 may also include components not shown in Figure 30, as can be found in existing technologies. As shown in Figure 30, the central processing unit 9100, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600. The memory 9140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 9100 may execute the program stored in the memory 9140 to perform information storage or processing, etc. Input unit 9120 provides input to central processing unit 9100. Input unit 9120 may be, for example, a keypad or touch input device. Power supply 9170 provides power to electronic device 9600. Display 9160 displays images and text. Display may be, for example, an LCD display, but is not limited thereto. The memory 9140 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs or processes for executing the operation of the electronic device 9600 via the central processing unit 9100. The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for the electronic device's communication functions and / or for performing other functions of the electronic device (such as messaging applications, address book applications, etc.). The communication module 9110 is a transmitter / receiver 9110 that transmits and receives signals via the antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processing unit 9100 to provide input signals and receive output signals, which can be the same as in a conventional mobile communication terminal. Based on different communication technologies, multiple communication modules 9110 can be configured in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby realizing typical telecommunications functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Additionally, the audio processor 9130 is also coupled to a central processing unit 9100, enabling on-device recording via the microphone 9132 and on-device playback of stored sound via the speaker 9131. Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the direct-drive wind turbine control parameter optimization method with a server or client as the execution subject in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the direct-drive wind turbine control parameter optimization method with a server or client as the execution subject in the above embodiments. For example, when the processor executes the computer program, it implements the following steps: S101: Based on the topology and control structure of the direct-drive wind turbine, impedance modeling is performed on the direct-drive wind turbine, and the influence of the bandwidth and damping ratio parameters of each controller on the impedance characteristics is analyzed. S102: Based on the dynamic response characteristics of the phase-locked loop, establish a transient reactive voltage model of the unit and analyze the influence of the bandwidth and damping ratio parameters of each controller on the transient characteristics. S103: Optimize the control parameters of the direct-drive wind turbine based on the impedance characteristics and the transient characteristics. As described above, the direct-drive wind turbine control parameter optimization method and apparatus provided in this application can analyze the influence of the unit's control bandwidth parameter and damping ratio parameter on its impedance and transient characteristics by establishing the impedance model and transient reactive voltage model of the direct-drive wind turbine. Then, based on the influence law of the unit parameters on the impedance and transient characteristics of the direct-drive wind turbine, the main influencing factors are determined. Using the phase-locked loop control bandwidth and damping ratio as decision variables, and employing an improved multi-objective particle swarm optimization algorithm, the control parameters of the direct-drive wind turbine are optimized to take into account both impedance characteristics and fault ride-through characteristics. This comprehensively optimizes the impedance characteristics and fault ride-through characteristics of the direct-drive wind turbine, and has certain engineering guiding significance. Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams. Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for optimizing control parameters of a direct-drive wind turbine generator set, characterized in that, include: Impedance modeling of the direct-drive wind turbine is performed based on its topology and control structure, and its impedance characteristics are analyzed. A transient reactive voltage model of the unit is established based on the dynamic response characteristics of the phase-locked loop, and the influence of the bandwidth and damping ratio parameters of each controller on the transient characteristics is analyzed. The control parameters of the direct-drive wind turbine are optimized based on the impedance characteristics and transient characteristics. The optimization of the control parameters of the direct-drive wind turbine based on the impedance characteristics and the transient characteristics includes: An optimization objective function is constructed based on the impedance characteristic optimization suggestions and the transient characteristic optimization suggestions; among them, the minimum absolute value of the unit impedance phase angle is used as the objective function for impedance characteristic optimization; and the minimum value of the unit transient overvoltage is used as the objective function for transient characteristic optimization. Recommended control parameters for the direct-drive wind turbine are generated using an improved multi-objective particle swarm optimization algorithm and the aforementioned optimization objective function. The step of generating recommended control parameters for the direct-drive wind turbine using an improved multi-objective particle swarm optimization algorithm and the optimization objective function includes: Initialize the algorithm parameters, particle swarm velocity information, and position information of the multi-objective particle swarm optimization algorithm; The initial fitness value of each particle is calculated based on the optimization objective function, and the particle's external profile, individual historical best particle, and global best particle are initialized. The following steps are executed iteratively until the preset maximum number of iterations is reached: update the velocity and position information of the particles using an improved multi-objective particle swarm optimization algorithm; calculate the fitness value of the updated particle population according to a preset particle mutation strategy, and update the external files of the particles according to a preset dominance relationship; update the globally optimal particle in a random selection manner according to the maximum capacity of the external files of the particles, and determine the historically optimal particle of the individual according to the preset dominance relationship. Determine whether the number of executions has reached the maximum number of iterations; if so, output the globally optimal particle currently saved in the particle external file and the individual historical optimal particle to obtain a non-dominated solution set; wherein, the non-dominated solution set includes the recommended control parameters of the direct-drive wind turbine.

2. The method for optimizing control parameters of a direct-drive wind turbine generator according to claim 1, characterized in that, The impedance modeling and impedance characteristic analysis of the direct-drive wind turbine based on its topology and control structure includes: Based on the topology and control structure, generate voltage and current relationship expressions for the grid-side converter, passive filter, and common connection point; Based on the aforementioned topology and control structure, the voltage component expression of the DC bus, the sine and cosine frequency domain expression of the phase-locked loop, and the relationship expression between the grid-side converter current and the modulation signal are generated. Substituting the voltage component expression, the sine and cosine frequency domain expression, and the relationship expression between the grid-side converter current and the modulation signal into the voltage-current relationship expression, the positive and negative sequence analytical impedance model of the direct-drive wind turbine is obtained.

3. The method for optimizing control parameters of a direct-drive wind turbine generator according to claim 2, characterized in that, The impedance modeling and impedance characteristic analysis of the direct-drive wind turbine based on its topology and control structure includes: Based on the topology and control structure, determine the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller. Based on the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller and the positive and negative sequence analytical impedance model, the effects of the DC voltage loop control bandwidth and damping ratio, the phase-locked loop control bandwidth and damping ratio, and the current loop control bandwidth and damping ratio on the impedance characteristics are analyzed, and impedance characteristic optimization suggestions are generated.

4. The method for optimizing control parameters of a direct-drive wind turbine generator according to claim 1, characterized in that, The transient reactive power and voltage model of the unit is established based on the dynamic response characteristics of the phase-locked loop, and the influence of the bandwidth and damping ratio parameters of each controller on the transient characteristics is analyzed, including: Based on the dynamic response characteristics of the phase-locked loop, determine the filter model expression, terminal voltage expression, voltage-current relationship expression at the output port of the grid-side converter, and grid voltage expression before and after the fault. By substituting the filter model of the grid-side converter, the generator terminal voltage expression, and the grid voltage before and after the fault into the voltage-current relationship expression at the output port of the grid-side converter, the transient reactive voltage model of the unit is obtained.

5. The method for optimizing control parameters of a direct-drive wind turbine generator according to claim 4, characterized in that, The transient reactive power and voltage model of the unit is established based on the dynamic response characteristics of the phase-locked loop, and the influence of the bandwidth and damping ratio parameters of each controller on the transient characteristics is analyzed, including: Based on the topology and control structure, determine the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller. Based on the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller and the transient reactive voltage model of the unit, the influence of the current inner loop control bandwidth and damping ratio, and the phase-locked loop control bandwidth and damping ratio on the transient characteristics are analyzed, and transient characteristic optimization suggestions are generated.

6. A device for optimizing control parameters of a direct-drive wind turbine generator set, characterized in that, include: Impedance characteristic analysis unit is used to perform impedance modeling of the direct-drive wind turbine based on its topology and control structure, and to analyze its impedance characteristics. The transient characteristic analysis unit is used to establish a transient reactive voltage model of the unit based on the dynamic response characteristics of the phase-locked loop, and to analyze the influence of the bandwidth and damping ratio parameters of each controller on the transient characteristics. A control parameter optimization unit is used to optimize the control parameters of the direct-drive wind turbine based on the impedance characteristics and the transient characteristics. The control parameter optimization unit includes: The objective function construction module is used to construct optimization objective functions based on impedance characteristic optimization suggestions and transient characteristic optimization suggestions; wherein, the minimum absolute value of the unit impedance phase angle is used as the objective function for impedance characteristic optimization; and the minimum value of the unit transient overvoltage is used as the objective function for transient characteristic optimization. The control parameter recommendation module is used to generate recommended control parameters for the direct-drive wind turbine using an improved multi-objective particle swarm optimization algorithm and the optimization objective function. The control parameter recommendation module includes: The algorithm initialization module is used to initialize the algorithm parameters, particle swarm velocity information, and position information of the multi-objective particle swarm optimization algorithm. The particle initialization module is used to calculate the initial fitness value of each particle according to the optimization objective function, and to initialize the particle's external file, the individual's historical best particle, and the global best particle. The iterative execution module is used to iteratively execute the following steps until a preset maximum number of iterations is reached: update the velocity and position information of particles using an improved multi-objective particle swarm optimization algorithm; calculate the fitness value of the updated particle population according to a preset particle mutation strategy, and update the particle external files according to a preset dominance relationship; update the globally optimal particle in a random selection manner according to the maximum capacity of the particle external files, and determine the individual historical optimal particle according to the preset dominance relationship. The non-dominated solution set generation module is used to determine whether the number of executions has reached the maximum number of iterations; if so, it outputs the globally optimal particle currently saved in the particle external file and the individual historical optimal particle to obtain the non-dominated solution set; wherein, the non-dominated solution set includes the recommended control parameters of the direct-drive wind turbine.

7. The direct-drive wind turbine control parameter optimization device according to claim 6, characterized in that, The impedance characteristic analysis unit includes: The first expression generation module is used to generate voltage and current relationship expressions at the grid-side converter, passive filter and common connection point based on the topology and control structure. The second expression generation module is used to generate the voltage component expression of the DC bus, the sine and cosine frequency domain expression of the phase-locked loop, and the relationship expression between the grid-side converter current and the modulation signal based on the topology and control structure. The impedance model construction module is used to substitute the voltage component expression, the sine and cosine frequency domain expression, and the relationship expression between the grid-side converter current and the modulation signal into the voltage-current relationship expression to obtain the positive and negative sequence analytical impedance model of the direct-drive wind turbine.

8. The direct-drive wind turbine control parameter optimization device according to claim 7, characterized in that, The impedance characteristic analysis unit includes: The first three-parameter relationship generation module is used to determine the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller according to the topology and control structure. The impedance optimization suggestion generation module is used to analyze the influence of DC voltage loop control bandwidth and damping ratio, phase-locked loop control bandwidth and damping ratio, and current loop control bandwidth and damping ratio on the impedance characteristics based on the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller and the positive and negative sequence analytical impedance model, and generate impedance characteristic optimization suggestions.

9. The direct-drive wind turbine control parameter optimization device according to claim 6, characterized in that, The transient characteristic analysis unit includes: The third expression generation module is used to determine the filter model expression of the grid-side converter, the terminal voltage expression, the voltage-current relationship expression of the grid-side converter output port, and the grid voltage expression before and after the fault, based on the dynamic response characteristics of the phase-locked loop. The transient model construction module is used to substitute the filter model of the grid-side converter, the terminal voltage expression, and the grid voltage before and after the fault into the voltage-current relationship expression of the output port of the grid-side converter to obtain the transient reactive voltage model of the unit.

10. The direct-drive wind turbine control parameter optimization device according to claim 9, characterized in that, The transient characteristic analysis unit includes: The second three-parameter relationship generation module is used to determine the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller according to the topology and control structure. The transient optimization suggestion generation module is used to analyze the influence of the current inner loop control bandwidth and damping ratio, and the phase-locked loop control bandwidth and damping ratio on the transient characteristics based on the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller and the transient reactive voltage model of the unit, and generate transient characteristic optimization suggestions.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the direct-drive wind turbine control parameter optimization method according to any one of claims 1 to 5.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the direct-drive wind turbine control parameter optimization method according to any one of claims 1 to 5.

13. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the direct-drive wind turbine control parameter optimization method according to any one of claims 1 to 5.

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