A method and device for optimizing control parameters of a direct-drive wind turbine generator set

Abstract

The present application provides a method and device for optimizing the control parameters of a direct-drive wind turbine set, which relates to the field of power equipment maintenance, including: impedance modeling of the direct-drive wind turbine set according to the topological structure and control structure of the direct-drive wind turbine set, and analyzing the impedance characteristics; establishing a transient reactive voltage model of the set based on the dynamic response characteristics of the phase-locked loop, and analyzing the influence of each controller bandwidth and damping ratio parameter on the transient characteristics; optimizing the control parameters of the direct-drive wind turbine set according to the impedance characteristics and the transient characteristics. The present application can take into account both the impedance characteristics and the fault ride-through characteristics of the direct-drive wind turbine set, and optimize the control parameters of the direct-drive wind turbine set.

Description

Technical Field

[0001] The present application relates to the field of power equipment maintenance, and specifically to a method and device for optimizing control parameters of a direct-drive wind turbine set. Background Art

[0002] As the proportion of wind power and other renewable energy installed capacity gradually increases, the scale of wind farm grid connection is further expanded. Since the grid strength in the areas where renewable energy converges is generally weak, and the "negative resistance" characteristics of the converters of renewable energy units are prominent in certain frequency ranges, large-scale renewable energy units connected to weak grids face more severe broadband oscillation risks, which seriously threatens the safe and stable operation of the grid. However, by optimizing the control parameters of renewable energy units, the phase margin of the units in certain frequency ranges can be increased, the impedance characteristics of the units can be optimized, and the stability of the grid-connected system can be improved, so that the grid can operate safely and stably.

[0003] At the same time, in terms of fault ride-through characteristics, when the system voltage drops, by injecting a certain amount of reactive power into the system, it can ensure that the grid-connected new energy units can operate continuously without disconnecting from the grid when they enter the low voltage ride-through (LVRT) state. However, when the new energy units are connected to a weak power grid, transient overvoltage problems caused by the instantaneous reactive surplus of the unit's grid connection point (Point of Common Coupling, PCC) are prone to occur after a system failure, causing large-scale new energy units to disconnect from the grid. Similarly, by optimizing the control parameters of new energy units, it is possible to suppress transient overvoltages at the unit end, improve the reactive response speed of the unit, optimize the transient characteristics of the unit, and reduce the risk of new energy units disconnecting from the grid.

[0004] Therefore, the impedance characteristics and fault ride-through characteristics of direct-drive wind turbines (Permanent Magnet Synchronous Generator, PMSG) will be affected by their control parameters. At present, the impedance characteristics and fault ride-through characteristics of PMSG need to meet their impedance phase requirements and grid requirements respectively, which has become the primary prerequisite for its grid connection. To this end, the North China Grid Control and the Northern Hebei Provincial Control have added impedance phase requirements for new energy generator stations connected to Zhangxiong UHV and Shangdu and other areas on the basis of the original national standard requirements for new energy generator fault ride-through characteristics, to ensure that the system can operate safely and stably after the new energy generator station is connected to the grid. Therefore, it is necessary to study the PMSG control parameter optimization method that takes into account both impedance characteristics and fault ride-through characteristics in combination with engineering practice.

[0005] In recent years, domestic and foreign scholars have achieved certain results in the research of PMSG impedance modeling and its parameter optimization and transient modeling and its parameter optimization. However, the existing research still has the following shortcomings:

[0006] (1) Regarding PMSG impedance characteristics, most optimization targets are to suppress unit oscillation, and few involve parameter optimization based on the impedance phase requirements of actual engineering units;

[0007] (2) Regarding the transient characteristics of PMSG, most studies analyze the influence of the unit's low-through parameters on transient overvoltage, but few studies analyze the influence of the unit's controller bandwidth and damping ratio parameters on transient reactive voltage, and provide parameter optimization suggestions for suppressing transient overvoltage.

[0008] (3) At present, the analysis of PMSG control parameter optimization mainly focuses on the characteristics of a single unit, and there is a lack of 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 power grid, parameter optimization that only considers a single characteristic of the unit may no longer be applicable to application scenarios in complex situations.

[0009] This section is intended to provide a background or context to the embodiments of the invention recited in the claims. No admission is made that the description herein is prior art by inclusion in this section. Summary of the invention

[0010] In response to the problems in the prior art, the present application provides a method and device for optimizing the control parameters of a direct-drive wind turbine set, which can take into account the impedance characteristics and fault ride-through characteristics of the direct-drive wind turbine set and optimize the control parameters of the direct-drive wind turbine set.

[0011] In order to solve the above technical problems, this application provides the following technical solutions:

[0012] In a first aspect, the present application provides a method for optimizing control parameters of a direct-drive wind turbine generator system, comprising:

[0013] The impedance modeling of the direct-drive wind turbine is performed according to the topological structure and control structure of the direct-drive wind turbine, and the influence of each controller bandwidth and damping ratio parameter on the impedance characteristics is analyzed;

[0014] The transient reactive voltage model of the unit is established based on the dynamic response characteristics of the phase-locked loop, and the influence of each controller bandwidth and damping ratio parameters on the transient characteristics is analyzed;

[0015] The control parameters of the direct-drive wind turbine generator set are optimized according to the impedance characteristics and the transient characteristics.

[0016] Furthermore, the impedance modeling of the direct-drive wind turbine set is performed according to the topological structure and control structure of the direct-drive wind turbine set, and the influence of each controller bandwidth and damping ratio parameter on the impedance characteristics is analyzed, including:

[0017] Generate a voltage-current relationship expression at the grid-side converter, the passive filter and the common connection point according to the topological structure and the control structure;

[0018] Generate a voltage component expression of the DC bus, a sine and cosine frequency domain expression of the phase-locked loop, and a relationship expression between the grid-side converter current and the modulation signal according to the topological structure and the control structure;

[0019] The voltage component expression, the sine-cosine frequency domain expression, and the relationship expression between the grid-side converter current and the modulation signal are substituted into the voltage-current relationship expression to obtain the positive and negative sequence analytical impedance model of the direct-drive wind turbine set.

[0020] Furthermore, the impedance modeling of the direct-drive wind turbine set is performed according to the topological structure and control structure of the direct-drive wind turbine set, and the influence of each controller bandwidth and damping ratio parameter on the impedance characteristics is analyzed, including:

[0021] Determine the relationship between the bandwidth, damping ratio and proportional-integral parameter of each controller according to the topological structure and the control structure;

[0022] According to the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller and the positive and negative sequence analytical impedance model, 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 are analyzed respectively, and impedance characteristic optimization suggestions are generated.

[0023] Furthermore, the transient reactive voltage model of the unit is established based on the dynamic response characteristics of the phase-locked loop, and the influence of each controller bandwidth and damping ratio parameter on the transient characteristics is analyzed, including:

[0024] Determine the filter model expression of the grid-side converter, the machine-end voltage expression, the voltage-current relationship expression of the output port of the grid-side converter, and the grid voltage expression before and after the fault occurs according to the dynamic response characteristics of the phase-locked loop;

[0025] The filter model of the grid-side converter, the machine-end voltage expression, and the grid voltage before and after the fault occurs are substituted 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.

[0026] Furthermore, the transient reactive voltage model of the unit is established based on the dynamic response characteristics of the phase-locked loop, and the influence of each controller bandwidth and damping ratio parameter on the transient characteristics is analyzed, including:

[0027] Determine the relationship between the bandwidth, damping ratio and proportional-integral parameter of each controller according to the topological structure and the control structure;

[0028] According to 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, the phase-locked loop control bandwidth and damping ratio on the transient characteristics are analyzed respectively, and transient characteristic optimization suggestions are generated.

[0029] Furthermore, the optimizing the control parameters of the direct-drive wind turbine generator set according to the impedance characteristics and the transient characteristics includes:

[0030] Construct the optimization objective function based on the impedance characteristic optimization suggestions and transient characteristic optimization suggestions;

[0031] The recommended control parameters of the direct-drive wind turbine generator system are generated using an improved multi-objective particle swarm optimization algorithm and the optimization objective function.

[0032] Furthermore, the use of the improved multi-objective particle swarm optimization algorithm and the optimization objective function to generate the recommended control parameters of the direct-drive wind turbine generator system includes:

[0033] Initialize the algorithm parameters of the multi-objective particle swarm optimization algorithm, the speed information and position information of the particle population;

[0034] Calculate the initial fitness value of each particle according to the optimization objective function, and initialize the particle external archive, individual historical optimal particle and global optimal particle;

[0035] Iterate the following steps until the preset maximum number of iterations is reached: update the velocity information and position information of the particles using the improved multi-objective particle swarm optimization algorithm; calculate the fitness value of the updated particle population according to the preset particle mutation strategy, and update the particle external archive according to the preset dominance relationship; update the global optimal particle in a random selection manner according to the maximum capacity of the particle external archive, and determine the individual historical optimal particle according to the preset dominance relationship;

[0036] Determine whether the number of executions reaches the maximum number of iterations; if so, output the global optimal particle and the individual historical optimal particle currently saved in the particle external archive to obtain a non-inferior solution set; wherein the non-inferior solution set includes the recommended control parameters of the direct-drive wind turbine group.

[0037] In a second aspect, the present application provides a direct-drive wind turbine control parameter optimization device, comprising:

[0038] An impedance characteristic analysis unit, used to perform impedance modeling on the direct-drive wind turbine set according to the topological structure and control structure of the direct-drive wind turbine set, and analyze the influence of each controller bandwidth and damping ratio parameter on the impedance characteristic;

[0039] The transient characteristics 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 analyze the influence of each controller bandwidth and damping ratio parameters on the transient characteristics;

[0040] A control parameter optimization unit is used to optimize the control parameters of the direct-drive wind turbine generator set according to the impedance characteristics and the transient characteristics.

[0041] Furthermore, the impedance characteristic analysis unit comprises:

[0042] A first expression generation module, used to generate a voltage-current relationship expression at a grid-side converter, a passive filter and a common connection point according to the topology structure and the control structure;

[0043] A second expression generation module is used to generate a voltage component expression of the DC bus, a sine and cosine frequency domain expression of the phase-locked loop, and a relationship expression between the grid-side converter current and the modulation signal according to the topological structure and the control structure;

[0044] 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 group.

[0045] Furthermore, the impedance characteristic analysis unit comprises:

[0046] A first three-parameter relationship generating module is used to determine the relationship between the bandwidth, damping ratio and proportional-integral parameter of each controller according to the topological structure and the control structure;

[0047] The impedance optimization suggestion generation module is used to 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 according to the relationship between the bandwidth, damping ratio and proportional-integral parameter of each controller and the positive-negative sequence analytical impedance model, and generate impedance characteristic optimization suggestions.

[0048] Furthermore, the transient characteristic analysis unit comprises:

[0049] A third expression generation module is used to determine the filter model expression of the grid-side converter, the machine-end voltage expression, the voltage-current relationship expression of the output port of the grid-side converter, and the grid voltage expression before and after the fault occurs according to the dynamic response characteristics of the phase-locked loop;

[0050] The transient model construction module is used to substitute the filter model of the grid-side converter, the machine-end voltage expression and the grid voltage before and after the fault occurs 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.

[0051] Furthermore, the transient characteristic analysis unit comprises:

[0052] A second three-parameter relationship generating module is used to determine the relationship between the bandwidth, damping ratio and proportional-integral parameter of each controller according to the topological structure and the control structure;

[0053] The transient optimization suggestion generation module is used to analyze the influence of the current inner loop control bandwidth and damping ratio, the phase-locked loop control bandwidth and damping ratio on the transient characteristics according to the relationship between the bandwidth of each controller, the damping ratio and the proportional-integral parameter and the transient reactive voltage model of the unit, and generate transient characteristic optimization suggestions.

[0054] Furthermore, the control parameter optimization unit includes:

[0055] An objective function building module is used to build an optimization objective function based on impedance characteristic optimization suggestions and transient characteristic optimization suggestions;

[0056] The control parameter recommendation module is used to generate the recommended control parameters of the direct-drive wind turbine generator system by using the improved multi-objective particle swarm optimization algorithm and the optimization objective function.

[0057] Furthermore, the control parameter recommendation module includes:

[0058] Algorithm initialization module, used to initialize the algorithm parameters of the multi-objective particle swarm optimization algorithm, the speed information and position information of the particle population;

[0059] A particle initialization module is used to calculate the initial fitness value of each particle according to the optimization objective function, and initialize the particle external archive, the individual historical optimal particle and the global optimal particle;

[0060] The iterative execution module is used to iteratively execute the following steps until a preset maximum number of iterations is reached: using an improved multi-objective particle swarm optimization algorithm to update the velocity information and position information of the particles; calculating the fitness value of the updated particle population according to a preset particle mutation strategy, and updating the particle external archive according to a preset dominance relationship; updating the global optimal particle in a random selection manner according to the maximum capacity of the particle external archive, and determining the individual historical optimal particle according to the preset dominance relationship;

[0061] The non-inferior solution set generation module is used to determine whether the number of executions reaches the maximum number of iterations; if so, output the global optimal particle and the individual historical optimal particle currently saved in the particle external archive to obtain a non-inferior solution set; wherein the non-inferior solution set includes the recommended control parameters of the direct-drive wind turbine group.

[0062] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the direct-drive wind turbine control parameter optimization method when executing the program.

[0063] In a fourth aspect, the present 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.

[0064] In a fifth aspect, the present application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the direct-drive wind turbine control parameter optimization method.

[0065] In response to the problems in the prior art, the direct-drive wind turbine control parameter optimization method and device provided in the present application can establish an impedance model and a transient reactive voltage model of the direct-drive wind turbine, and analyze the influence of the unit control bandwidth parameters and the damping ratio parameters on its impedance characteristics and transient characteristics respectively. Then, based on the influence law of the unit parameters on the impedance characteristics and transient characteristics of the direct-drive wind turbine, the main influencing factors are determined. The phase-locked loop control bandwidth and the damping ratio are used as decision variables, and an 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 the impedance characteristics and the fault ride-through characteristics, and comprehensively optimize the impedance characteristics and the fault ride-through characteristics of the direct-drive wind turbine, which has certain engineering guidance significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0067] Figure 1 It is a flow chart of a control parameter optimization method for a direct-drive wind turbine generator set taking into account both impedance characteristics and fault ride-through characteristics in an embodiment of the present application;

[0068] Figure 2 The main circuit topology and control structure of the direct-drive wind turbine grid-side converter in the embodiment of the present application;

[0069] Figure 3 A comparison diagram of the positive sequence (left a) and negative sequence (right b) impedance analysis curves and the frequency sweep results in the embodiment of the present application;

[0070] Figure 4 In the embodiment of this application f gic (left a) and ξ gic (right b) Impact on PMSG impedance characteristics;

[0071] Figure 5 In the embodiment of this application f pll (left a) and ξ pll (right b) Impact on PMSG impedance characteristics;

[0072] Figure 6 In the embodiment of this application f dvc (left a) and ξ dvc (right b) Impact on PMSG impedance characteristics;

[0073] Figure 7 The main circuit topology structure after conversion of the direct-drive wind turbine in the embodiment of the present application;

[0074] Figure 8 A comparison diagram of the transient reactive voltage model and the simulation results in the embodiment of the present application;

[0075] Fig. 9 In the embodiment of this application f gic (left a) and ξ gic (right b) Impact on PMSG transient characteristics

[0076] Fig.10 In the embodiment of this application f pll (left a) and ξ pll (right b) Impact on PMSG transient characteristics

[0077] Fig.11 In the present application, two ω Comparison of decreasing strategies;

[0078] Fig.12 A flowchart of an improved multi-objective particle swarm optimization algorithm in an embodiment of the present application;

[0079] Fig.13 The optimization results of ZDT1 and ZDT3 in the examples of this application;

[0080] Fig.14 The optimization result of formula (18) by the improved multi-objective particle swarm optimization algorithm in the embodiment of the present application;

[0081] Fig.15 This is the parameter optimization scheme (point A) of the impedance characteristics (left a) and transient characteristics (right b) in the embodiment of this application;

[0082] Fig.16 This is a flow chart of a method for optimizing control parameters of a direct-drive wind turbine generator system in an embodiment of the present application;

[0083] Fig.17 One of the flow charts for impedance modeling and impedance characteristic analysis in the embodiment of the present application;

[0084] Fig.18 This is the second flowchart of impedance modeling and impedance characteristics analysis in the embodiment of the present application;

[0085] Fig.19 One of the flow charts for performing transient modeling and analyzing transient characteristics in an embodiment of the present application;

[0086] Fig. 20 This is the second flow chart of transient modeling and transient characteristic analysis in the embodiment of the present application;

[0087] Fig.21 This is a flow chart of optimizing the control parameters of a direct-drive wind turbine generator system in an embodiment of the present application;

[0088] Fig. 22 A flowchart for generating recommended control parameters for a direct-drive wind turbine generator system in an embodiment of the present application;

[0089] Fig.23 This is a structural diagram of a control parameter optimization device for a direct-drive wind turbine generator system in an embodiment of the present application;

[0090] Fig.24 This is one of the structural diagrams of the impedance characteristic analysis unit in the embodiment of the present application;

[0091] Fig.25 This is the second structural diagram of the impedance characteristic analysis unit in the embodiment of the present application;

[0092] Fig.26 This is one of the structural diagrams of the transient characteristic analysis unit in the embodiment of the present application;

[0093] Fig. 27 This is the second structural diagram of the transient characteristic analysis unit in the embodiment of the present application;

[0094] Fig.28 This is a structural diagram of a control parameter optimization unit in an embodiment of the present application;

[0095] Fig.29 This is a structural diagram of a control parameter recommendation module in an embodiment of the present application;

[0096] Fig.30 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0097] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0098] The information collected in the technical solution of this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0099] Provide users with corresponding operation entrances for them to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered.

[0100] In one embodiment, see Fig.16 In order to take into account both the impedance characteristics and the fault ride-through characteristics of the direct-drive wind turbine generator set, the control parameters of the direct-drive wind turbine generator set are optimized. The present application provides a method for optimizing the control parameters of the direct-drive wind turbine generator set, including:

[0101] S101: performing impedance modeling on the direct-drive wind turbine generator set according to the topological structure and control structure of the direct-drive wind turbine generator set, and analyzing the influence of each controller bandwidth and damping ratio parameter on the impedance characteristics;

[0102] S102: Establishing a transient reactive voltage model of the unit based on the dynamic response characteristics of the phase-locked loop, and analyzing the influence of each controller bandwidth and damping ratio parameter on the transient characteristics;

[0103] S103: Optimizing control parameters of the direct-drive wind turbine generator system according to the impedance characteristics and the transient characteristics.

[0104] It can be understood that the present invention comprehensively analyzes the influence of parameter changes on the above two characteristics of PMSG from the two aspects of impedance characteristics and fault ride-through characteristics of direct-drive wind turbines based on the control bandwidth and damping ratio parameters of PMSG. Then, with the optimization goals of meeting the impedance phase requirements of the unit and suppressing the transient overvoltage of the unit, the improved multi-objective particle swarm optimization (MOPSO) algorithm is adopted to achieve the optimization of PMSG control parameters that take into account both impedance characteristics and fault ride-through characteristics, and comprehensively optimize the unit impedance characteristics and fault ride-through characteristics to solve the problem of the lack of PMSG control parameter optimization methods that simultaneously consider impedance characteristics and fault ride-through characteristics in existing practical projects.

[0105] In the specific implementation, based on a PMSG station, first, the positive and negative sequence impedance model of the unit is established to analyze the influence of the unit 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 control bandwidth and damping ratio parameters on its transient characteristics. Finally, the main influencing factors are determined based on the influence of the unit parameters on the two characteristics, and the PLL bandwidth is used as the input parameter. f pll , Damping ratio ξ pll Parameters are taken as decision variables, and 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 through simulation.

[0106] Figure 1 This is a flow chart of a PMSG control parameter optimization method that takes into account both impedance characteristics and fault ride-through characteristics provided by the present invention. Figure 1 The specific steps of the PMSG control parameter optimization method taking into account both impedance characteristics and fault ride-through characteristics described in the present invention are as follows:

[0107] S1: PMSG impedance modeling and impedance characteristics analysis.

[0108] Based on a PMSG station, according to the typical topology and control structure of PMSG, the sequence impedance model of the unit was established, and the influence of parameters such as the unit control bandwidth and damping ratio on its impedance characteristics was analyzed.

[0109] S2: PMSG transient modeling and transient characteristics analysis.

[0110] According to the typical topology and control structure of PMSG and considering the dynamic response process of PLL, a transient reactive voltage model of the unit is established, and the influence of parameters such as unit control bandwidth and damping ratio on its transient characteristics is analyzed.

[0111] S3: PMSG control parameter optimization method taking into account both impedance characteristics and fault ride-through characteristics.

[0112] The optimization goal is to meet the unit impedance phase requirements and suppress unit transient overvoltage. f pll , ξ pll 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 impedance characteristics and transient characteristics.

[0113] From the above description, it can be seen that the direct-drive wind turbine control parameter optimization method provided in the present application can establish an impedance model and a transient reactive voltage model of the direct-drive wind turbine, and analyze the influence of the unit control bandwidth parameters and the damping ratio parameters on its impedance characteristics and transient characteristics respectively. Then, based on the influence of the unit parameters on the impedance characteristics and transient characteristics of the direct-drive wind turbine, the phase-locked loop control bandwidth and the damping ratio are used as decision variables, and 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 the impedance characteristics and the fault ride-through characteristics, and comprehensively optimize the impedance characteristics and the fault ride-through characteristics of the direct-drive wind turbine, which has certain engineering guidance significance.

[0114] In one embodiment, see Fig.17 , performing impedance modeling on the direct-drive wind turbine generator set according to the topological structure and control structure of the direct-drive wind turbine generator set, and analyzing the impedance characteristics, including:

[0115] S201: Generate a voltage-current relationship expression at a grid-side converter, a passive filter and a common connection point according to the topology structure and the control structure;

[0116] S202: Generate a voltage component expression of the DC bus, a sine and cosine frequency domain expression of the phase-locked loop, and a relationship expression between the grid-side converter current and the modulation signal according to the topology structure and the control structure;

[0117] S203: Substitute the voltage component expression, the sine-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 set.

[0118] It can be understood that the above steps S201 to S203 can be implemented according to the following process.

[0119] S1: PMSG impedance modeling and impedance characteristics analysis.

[0120] S11: The PMSG impedance analytical model is established using the harmonic linearization method and verified by impedance scanning.

[0121] Taking a PMSG wind farm as the background, the GSC main circuit topology and control block diagram of the PMSG after the direct-drive wind farm is aggregated and equalized are shown in the figure below: Figure 2 The PMSG impedance phase requirements are shown in Table 1.

[0122]

[0123] See also Figure 2 , considering the DC voltage outer loop, PLL, current inner loop and dq-axis voltage feed-forward link of the grid side converter (GSC), the positive and negative sequence impedance models of PMSG are established by using the harmonic linearization method and verified by impedance scanning.

[0124] exist Figure 2 middle, i g is the current on the high voltage side of the unit box transformer; N is the number of PMSGs; k 1. k 2 is the transformer ratio; R 1. R 2. L 1. L 2 is the resistance and inductance of 35kV and 110kV transmission lines; L 3. R sc , BRK are respectively the current limiting impedance, short-circuit impedance and short-circuit switch in the fault generating device; KI n = i max is the maximum current allowed to pass through the unit, K Take 1.5; I n is the rated current of the unit; U g is the per unit value of the voltage amplitude at the PCC point.

[0125] The symmetrical component method is used to Figure 2 The voltage and current relationship at the GSC output port, LC filter and PCC point is analyzed and modeled:

[0126] (1)

[0127] In the formula, via , v ib , v ic They are the three-phase output port voltages of GSC respectively; i a , i b , i c and v a , v b , v c They are the three-phase AC current and voltage of the PCC of PMSG; L f , R f , C f For filtering inductance, resistance and capacitance; v ia = m a k m v dc , m a is the a-phase AC modulation signal of GSC, k m is the modulation coefficient, v dc is the DC bus voltage, s is a frequency domain operator.

[0128] The DC bus serves as the link between the generator-side and grid-side converters. According to the equal instantaneous power of the AC and DC sides of the unit, the frequency [0, ±( f p - f 1), ±( f n + f 1)] under the DC voltage component:

[0129] (2)

[0130] In the formula, C dc , V dc0 is the DC bus capacitance and voltage steady-state value; I load is the steady-state output current on the machine side; V 1. f 1 are the amplitude and frequency of the fundamental positive sequence voltage, V p ,f p , φ vp They are the amplitude, frequency, and initial phase of the harmonic positive sequence voltage. V n , f n , φ vn They are the amplitude, frequency and initial phase of the harmonic negative sequence voltage respectively. I 1 is the amplitude of the fundamental positive sequence current, I p , φ ip are the amplitude and initial phase of harmonic current and voltage respectively. I n , φ in They are the amplitude and initial phase of the harmonic negative sequence current respectively. V 1. V p , V n , I 1. I p , I n They are the frequency domain components of the positive and negative sequence voltage and current respectively. V 1= V 1 / 2, V p = V p e ±jφvp / 2, V n = V n e ±jφvn / 2, I 1= I 1 / 2, I p = I p e ±jφip / 2, I n = I n e ±jφin / 2. V 1*、 I 1* and V 1. I 1 are conjugate functions of each other.

[0131] The PCC positive and negative sequence harmonic voltages will cause disturbances in the PLL output phase angle. When there is a disturbance voltage with harmonic frequency at the grid connection point, the PLL output angle will be different from the angle of the positive sequence voltage component of the grid fundamental frequency. θ 1, also includes the disturbance angle Δ of the harmonic voltage θ At this time, the grid angle obtained by PLL control is θ PLL Expressed as θ PLL = θ 1+ Δ θ Then, based on the harmonic linearization method, the relationship between the harmonic voltage and the disturbance angle is analyzed to obtain the PLL sine and cosine frequency domain expressions.

[0132] (3)

[0133] In the formula, H PLL ( s ) is the transfer function of the PLL controller, T PLL ( s ) = H PLL ( s ) / [1+ V 1 H PLL ( s )]; H PLL ( s )= ( k pllp + k plli / s ) / s , k pllp , k plli are the proportional-integral coefficients of the PLL respectively.

[0134] according to Figure 2 Given the control circuit, the harmonic linearization method is used to obtain the relationship between the PCC current and the modulation signal, and the small signal model of the current loop and modulation is established:

[0135] (4)

[0136] In the formula, v d , v q 、i d 、i q 、md , m q They are dq Voltage, current and modulation signal in the coordinate system; i dr , i qr for dq Shaft current reference value; v dc , v dcr is the DC bus voltage and its reference value; K d is the AC current control decoupling coefficient; k f for dq Shaft voltage feed-forward coefficient; H v ( s ), H i ( s ) are the transfer functions of the DC voltage outer loop and current inner loop controllers, H i ( s ) = k ip + k ii / s , H v ( s ) = k vp + k vi / s , k ip , k ii , k vp , k vi They are the proportional-integral coefficients of the inner current loop and the outer voltage loop respectively.

[0137] In summary, by combining the PMSG main circuit model and the frequency domain components of each key link, the positive and negative sequence impedance model of PMSG can be obtained after analytical derivation and organization, as shown in Equation (5).

[0138] (5)

[0139] In the formula, the fundamental angular frequency is ω 1 = 2π f 1( f 1 is the fundamental frequency); M 1 is the steady-state fundamental frequency component of the modulated signal,M 1=( M dr ± JM qr ) / 2, and M 1= ( V 1+ sL f I 1) / k m V dc0 , M 1* and M 1 are mutually conjugated, M dr , M qr is the DC component of the dq-axis modulation signal.

[0140] Figure 3 This is a comparison chart of the PMSG impedance analysis curve and the frequency sweep result.

[0141] 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 PMSG positive and negative sequence analytical impedance models. In addition, it can be found that the PMSG positive sequence impedance does not meet the unit impedance phase requirements at some frequencies in the [41,100] Hz frequency band, while the remaining frequency bands of the positive sequence impedance and all frequency bands of the PMSG negative sequence impedance meet the unit impedance phase requirements. Considering the actual engineering needs, the present invention focuses on the frequency bands of the unit impedance phase requirements with the top three priority levels 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 the filter parameters, the control link parameters have little effect on it. Therefore, the present invention subsequently mainly analyzes the influence of PMSG control parameters on its positive sequence impedance characteristics in the frequency bands [1,30] Hz and [61,100] Hz.

[0142] From the above description, it can be seen that the direct-drive wind turbine control parameter optimization method provided in the present application can perform impedance modeling on the direct-drive wind turbine according to the topological structure and control structure of the direct-drive wind turbine, and analyze the influence of each controller bandwidth and damping ratio parameters on the impedance characteristics.

[0143] In one embodiment, see Fig.18 , the impedance modeling of the direct-drive wind turbine set is performed according to the topological structure and control structure of the direct-drive wind turbine set, and the influence of each controller bandwidth and damping ratio parameter on the impedance characteristics is analyzed, including:

[0144] S301: Determine the relationship between the bandwidth, damping ratio and proportional-integral parameter of each controller according to the topological structure and the control structure;

[0145] S302: According to the relationship between the bandwidth, damping ratio and proportional-integral parameter of each controller and the positive-negative sequence analytical impedance model, respectively 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.

[0146] It can be understood that, next, S12: based on the PMSG control bandwidth and damping ratio parameters, the influence of the control parameter changes on the PMSG impedance characteristics is analyzed, and parameter optimization suggestions are given to meet the impedance phase requirements of the actual engineering unit.

[0147] A1: Control bandwidth, damping ratio and PI parameter conversion relationship.

[0148] In the typical structure of PMSG, the closed-loop transfer functions of the DC voltage loop, PLL and current loop controllers can all be equivalent to second-order systems, thereby obtaining the relationship between the bandwidth, damping ratio and PI parameters of each controller, as shown in equation (6). The present invention subsequently analyzes the factors affecting the impedance characteristics and transient characteristics of PMSG based on the control bandwidth and damping ratio parameters of the current inner loop, PLL and voltage outer loop.

[0149] (6)

[0150] In the formula, ω v = 2π f dvc , f dvc and ξ dvc Control bandwidth and damping ratio for the DC voltage loop; ω p = 2π f pll , f pll and ξ pll Control bandwidth and damping ratio for PLL; ω i = 2π f gic , f gic and ξ gic is the current loop bandwidth and damping ratio.

[0151] A2: Analysis of factors affecting PMSG impedance characteristics.

[0152] A21: Current inner loop control bandwidth and damping ratio parameters.

[0153] For analysis fgic and ξ gic The influence of parameters on the phase-frequency characteristics of the PMSG internal impedance in the frequency bands [1,30]Hz and [61,100]Hz is changed respectively. f gic and ξ gic Control parameters and obtain the Bode diagram of the unit impedance characteristics changing with parameters, such as Figure 4 As shown. Figure 4 In (a), f gic The values ​​are 100Hz, 160Hz, and 280Hz respectively. Figure 4 (b), ξ gic The values ​​are 0.45, 1.15, and 1.85 respectively.

[0154] Depend on Figure 4 It can be seen that with f gic Reduce, ξ gic As the impedance increases, the PMSG impedance amplitude-frequency characteristic moves downward in the frequency bands [1,30]Hz and [61,100]Hz; the phase-frequency characteristic moves downward in the frequency band [1,30]Hz and moves upward in the frequency band [61,100]Hz. f gic Reduce, ξ gic The frequency band in which the unit meets the impedance phase requirements in the frequency bands [1,30] Hz and [61,100] Hz increases, the system phase margin increases, the PMSG impedance characteristics are optimized, and the system stability is improved.

[0155] A22: PLL control bandwidth and damping ratio parameters.

[0156] For analysis f pll and ξ pll The influence of parameters on the phase-frequency characteristics of the PMSG internal impedance in the frequency bands [1,30]Hz and [61,100]Hz is changed respectively. f pll and ξ pll Control parameters and obtain the Bode diagram of the unit impedance characteristics changing with parameters, such as Figure 5 As shown. Figure 5 In (a), f pll The values ​​are 3Hz, 12Hz, and 25Hz respectively. Figure 5 (b), ξ pllThe values ​​are 0.45, 1.15, and 1.85 respectively.

[0157] Depend on Figure 5 It can be seen that with f pll and ξ pll Reduced, the unit impedance amplitude-frequency characteristics in the frequency band [ f 1- f pll , f 1+ f pll ]Hz; the phase-frequency characteristic moves down in the frequency band [1,30]Hz and moves up in the frequency band [61,100]Hz. The results show that with f pll and ξ pll The unit meets the impedance phase requirements in the frequency bands [1,30] Hz and [61,100] Hz, which increases the system phase margin, optimizes the PMSG impedance characteristics, and improves system stability.

[0158] A23: Voltage outer loop control bandwidth and damping ratio parameters.

[0159] For analysis f dvc and ξ dvc The influence of parameters on the phase-frequency characteristics of the PMSG internal impedance in the frequency bands [1,30]Hz and [61,100]Hz is changed respectively. f dvc and ξ dvc Control parameters and obtain the Bode diagram of the unit impedance characteristics changing with parameters, such as Figure 6 As shown. Figure 6 Part (a) of f dvc The values ​​are 10Hz, 25Hz, and 40Hz respectively. Figure 6 Part (b) of ξ dvc The values ​​are 0.45, 1.15, and 1.85 respectively.

[0160] Depend on Figure 6 It can be seen that with f dvc and ξ dvc As the impedance increases, the PMSG impedance amplitude-frequency characteristic moves upward in the frequency range [30,70] Hz. f dvc Reduce, ξ dvcAs the impedance increases, the unit impedance phase-frequency characteristic moves down in the frequency band [1,30] Hz and moves up in the frequency band [61,100] Hz. However, the unit impedance phase-frequency characteristic curve changes very little. f dvc Reduce, ξ dvc Increase, although the unit in the frequency band [1,30] Hz, [61,100] Hz to meet the impedance phase requirements of the frequency band increased, but f dvc and ξ dvc The parameters have little effect on the PMSG impedance phase-frequency characteristics and have little effect on optimizing the unit impedance characteristics.

[0161] In summary, by increasing ξ gic , ξ dvc , reduce f gic , f pll , ξ pll , f dvc , the PMSG impedance phase-frequency characteristic moves down in the frequency band [1,30]Hz and moves up in the frequency band [61,100]Hz. This shows that the frequency band range in which the PMSG meets the impedance phase requirements in the frequency bands [1,30]Hz and [61,100]Hz increases, increasing the system phase margin, optimizing the PMSG impedance characteristics, and improving the system stability. Among them, f pll , ξ pll The biggest impact, f gic , ξ gic Secondly, f dvc , ξ dvc Minimum.

[0162] From the above description, it can be seen that the direct-drive wind turbine control parameter optimization method provided in the present application can perform impedance modeling on the direct-drive wind turbine according to the topological structure and control structure of the direct-drive wind turbine, and analyze the influence of each controller bandwidth and damping ratio parameters on the impedance characteristics.

[0163] In one embodiment, see Fig.19 The transient reactive voltage model of the unit is established based on the dynamic response characteristics of the phase-locked loop, and the influence of each controller bandwidth and damping ratio parameter on the transient characteristics is analyzed, including:

[0164] S401: Determine a filter model expression of a grid-side converter, a machine-end voltage expression, a voltage-current relationship expression of an output port of the grid-side converter, and grid voltage expressions before and after a fault occurs according to the dynamic response characteristics of the phase-locked loop;

[0165] S402: Substitute the filter model of the grid-side converter, the machine-end voltage expression, and the grid voltage before and after the fault occurs 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.

[0166] It is understandable that step S401 to step S402 can be implemented in the following method.

[0167] S2: PMSG transient modeling and transient characteristics analysis.

[0168] S21: Establish a PMSG transient reactive voltage model considering the dynamic response characteristics of the phase-locked loop and verify it through time domain simulation.

[0169] exist Figure 2 In the figure, 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 dispatched reactive power command, and the latter provides emergency reactive power support to the power grid. The PMSG switches between the steady-state operation state and the transient operation state, and its reactive current command is determined by the voltage amplitude of the power grid.

[0170] Considering PLL, inner current loop and dq The PMSG transient reactive voltage model is established by using the shaft voltage feedforward link. Figure 2 The grid voltage level, transformer impedance and line impedance in the main circuit topology are uniformly converted to the PMSG side, such as Figure 7 shown.

[0171] in, i ia , i ib , i ic They are the three-phase output port currents of GSC respectively; v RC_abc , i RC_abc is the voltage and current of the RC filter branch; R g , L g are the equivalent resistance and inductance on the grid side respectively.

[0172] according to Figure 7 The equivalent main circuit topology in can calculate the voltage and current at the GSC output port and the grid voltage. dqThe rotated coordinate system can be expressed as:

[0173] (7)

[0174] In the formula, v id , v iq , i id , i iq The terminal voltage and current are dq Component; Δ v Ld , Δ v Lq For filter inductance dq Pressure drop component; v Cd , v Cq , i RCd , i RCq is the voltage and current of the RC filter branch dq Quantity.

[0175] The mathematical model of the filter of the grid-side converter can be expressed as:

[0176] (8)

[0177] In the formula, ω g is the grid voltage angular frequency.

[0178] according to Figure 2 Control block diagram, the PMSG terminal voltage can be expressed as:

[0179] (9)

[0180] The actual power grid can be equivalent to an ideal voltage source E s and R g , L g The grid voltage is dq The axis components can be expressed as:

[0181] (10)

[0182] In the formula, e d ' , e q ' is the grid voltage after the fault occurs dqQuantity.

[0183] Considering that the dynamic response time of PLL is longer than that of the current inner loop, and it takes 20~100ms (depending on the PLL structure and parameters) to track the actual phase jump, when a fault occurs on the grid side, there will be a certain phase-locking deviation between the PLL output phase and the actual phase, which will lead to active and reactive control coupling, thus affecting the transient characteristics of PMSG.

[0184] according to Figure 1 In the phase-locked control link, the input PLL voltage phase is set to θ 0, the amplitude is E m , we can get the PLL output phase expression:

[0185] (11)

[0186] If a fault occurs, v abc Voltage generation Δ θ Phase jump, converted to the complex frequency domain is Δ θ ( s )=Δ θ / s At this time, by solving Δ θ ( s ) response, the Δ difference between the PLL output phase and the actual phase can be obtained θ PLL ( s )for:

[0187] (12)

[0188] Δ θ PLL ( s ) is transformed by inverse Laplace transform, and the phase-locking deviation Δ corresponding to the response in the time domain can be obtained. θ PLL Therefore, when a short circuit occurs, the PCC voltage amplitude instantly drops to E m ' ,Voltage v dq ' It can be expressed as:

[0189] (13)

[0190] Because the phase-locking deviation is small enough before the fault occurs, θ 0- θ PLL is approximately 0. Therefore, the grid voltage component after the fault occursv dq ' It can be simplified as follows:

[0191] (14)

[0192] Converting equations (7) to (10) and (14) to the complex frequency domain and solving them simultaneously, we can obtain the voltage and current of the RC filter branch and the grid current: dq The complex frequency domain solution under the components:

[0193] (15)

[0194] (16)

[0195] In the formula, v 0Cd , v 0Cq , i 0id , i 0iq They are the filter capacitor voltage and the inductor current respectively. dq Axis initial value; i 0d , i 0q is the grid inductor current dq Axis initial value; i 1dr ( s ), i 1qr ( s ) is the PMSG grid voltage after change dq The command value of the axis in the complex frequency domain.

[0196] Performing an inverse Laplace transform on the above equation, we can obtain the filter capacitor voltage, inductor current and grid inductor current. dq The time domain solution under the axis and substituting it into equation (7) can obtain the time domain expression of PMSG grid-side voltage and current: v d ( t ), v q ( t ), i d ( t ), i q ( t ). Therefore, the PCC voltage of PMSG and the transient reactive power output to the grid can be expressed as:

[0197] (17)

[0198] Figure 8 This is a comparison chart between the PMSG transient reactive voltage analytical model and the simulation results.

[0199] The comparison results show that the PMSG transient reactive power voltage response analytical model is basically consistent with the PSCAD electromagnetic transient simulation result trend, which verifies the accuracy of the PMSG transient reactive power voltage response analytical model established in the present invention.

[0200] From the above description, it can be seen that 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 each controller bandwidth and damping ratio parameters on the transient characteristics.

[0201] In one embodiment, see Fig. 20 The transient reactive voltage model of the unit is established based on the dynamic response characteristics of the phase-locked loop, and the influence of each controller bandwidth and damping ratio parameter on the transient characteristics is analyzed, including:

[0202] S501: Determine the relationship between the bandwidth, damping ratio and proportional-integral parameter of each controller according to the topological structure and the control structure;

[0203] S502: According to the relationship between the bandwidth, damping ratio and proportional-integral parameter of each controller and the transient reactive voltage model of the unit, respectively analyze the influence of the current inner loop control bandwidth and damping ratio, the phase-locked loop control bandwidth and damping ratio on the transient characteristics, and generate transient characteristics optimization suggestions.

[0204] It can be understood that, next, S22: based on the PMSG control bandwidth and damping ratio parameters, the influence of the control parameter changes on the PMSG transient characteristics is analyzed, and parameter optimization suggestions are given to suppress the transient overvoltage of the unit.

[0205] A1: Analysis of factors affecting PMSG transient characteristics.

[0206] A11: Current inner loop control bandwidth and damping ratio parameters.

[0207] For analysis f gic and ξ gic The influence of parameters on the transient no-overvoltage characteristics of PMSG is changed respectively f gic and ξ gic Control parameters and obtain the waveform of the unit transient characteristics changing with the parameters, such as Fig. 9 As shown. Among them, f gic and ξ gicThe parameter value is consistent with that in A21 in S12.

[0208] Depend on Fig. 9 From (a), we can see that at the moment of fault recovery, the PMSG transient reactive overshoot increases with f gic increases with the increase of f gic The results show that the increase f gic Although it can suppress the transient overvoltage at the PMSG machine end, it will also increase the reactive overshoot generated by the unit, and has little impact on the transient reactive voltage characteristics of the unit and its reactive response speed.

[0209] Depend on Fig. 9 From (b), we can see that at the moment of fault recovery, the PMSG transient reactive overshoot increases with ξ gic increases and decreases, while the transient overvoltage of the unit decreases with ξ gic The results show that increasing ξ gic Although it can reduce the PMSG reactive overshoot, it will increase the transient overvoltage of the unit. ξ gic The impact on the transient characteristics and reactive power response speed of the unit is also relatively small.

[0210] A12: PLL control bandwidth and damping ratio parameters.

[0211] For analysis f pll and ξ pll The influence of parameters on the transient no-overvoltage characteristics of PMSG is changed respectively f pll and ξ pll Control parameters and obtain the waveform of the unit transient characteristics changing with the parameters, such as Fig.10 As shown. Among them, f pll and ξ pll The parameter value is consistent with that in A22 in S12.

[0212] Depend on Fig.10 It can be seen that at the moment of fault recovery, the PMSG transient reactive overshoot and its transient overvoltage increase with f pll , ξ pll In addition, compared with fpll 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 increases from 1.15 to 1.85, the degree of change in the unit's transient no-overvoltage characteristics becomes significantly smaller.

[0213] The results show that although f pll , ξ pll The impact on the transient characteristics of the unit is obvious, which can be achieved by increasing f pll , ξ pll , effectively suppressing transient overvoltage at the machine end, reducing the reactive overshoot of the unit, and improving the reactive response speed of the unit. f pll , ξ pll The ability to optimize the transient reactive power and voltage characteristics of the unit is limited.

[0214] In summary, although the ability of the unit control parameters to optimize its transient characteristics is limited, it can still be reduced ξ gic and increase f gic , f pll , ξ pll , to suppress the transient overvoltage at the PMSG machine end, reduce the reactive overshoot of the unit, and optimize the transient reactive voltage characteristics of the unit. Among them, the influence of PLL on the transient reactive voltage characteristics of the unit is greater than that of the current inner loop, and compared with f gic , ξ gic There is no significant effect on the reactive power response speed of the unit. f pll , ξ pll， , but can effectively improve the reactive power response speed of the unit.

[0215] From the above description, it can be seen that 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 each controller bandwidth and damping ratio parameters on the transient characteristics.

[0216] In one embodiment, see Fig.21, the optimizing the control parameters of the direct-drive wind turbine generator set according to the impedance characteristics and the transient characteristics includes:

[0217] S601: constructing an optimization objective function according to the impedance characteristic optimization suggestion and the transient characteristic optimization suggestion;

[0218] S602: Generate recommended control parameters for the direct-drive wind turbine generator system using an improved multi-objective particle swarm optimization algorithm and the optimization objective function.

[0219] It can be understood that step S601 to step S602 can be implemented by the following method.

[0220] S3: PMSG control parameter optimization method taking into account both impedance characteristics and fault ride-through characteristics.

[0221] S31: Parameter optimization method.

[0222] Combining the analysis of the PMSG impedance characteristics and transient characteristics in S12 and S22, it can be seen that: ξ gic , reduce f gic , f pll , ξ pll Although it can increase the frequency range of PMSG that meets the impedance phase requirements in the frequency bands [1,30] Hz and [61,100] Hz and optimize the unit impedance characteristics, it will also aggravate the PMSG machine-side transient overvoltage, reduce the unit's reactive power response speed, and deteriorate the unit's transient reactive power voltage characteristics. In addition, in optimizing PMSG impedance characteristics and transient characteristics, f pll , ξ pll The control parameters play a major role in affecting both.

[0223] The results show that within certain frequency bands, the PMSG impedance characteristics and fault ride-through characteristics may have a certain degree of mutual restriction, showing that the control parameters optimized for the unit impedance (fault ride-through) characteristics may degrade its fault ride-through (impedance) characteristics, resulting in the problem of "losing one thing while focusing on the other".

[0224] Therefore, the present invention takes into account the impedance characteristics and transient characteristics of PMSG, and takes meeting the unit impedance phase requirements and suppressing the unit transient overvoltage as the optimization goals. f pll , ξ pllParameters 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 impedance characteristics and transient characteristics.

[0225] S32: Constructing the objective function.

[0226] (1) Impedance characteristics

[0227] When the increase ξ gic , reduce f gic , f pll , ξ pll , the unit's internal impedance phase-frequency characteristics in the frequency bands [1,30] Hz and [61,100] Hz 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 internal impedance phase-frequency characteristics in the frequency band [61,100] Hz, the internal impedance characteristics in the frequency band [1,30] Hz can also be optimized.

[0228] Since the impedance phase-frequency characteristic curve in the frequency band [61,100] Hz is overall upward when the unit parameters are optimized, 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 61 Hz as the optimization objective function of the impedance characteristic. f 1(x).

[0229] (2) Transient characteristics

[0230] In order to suppress the transient overvoltage of the unit, this paper takes the minimum value of the transient overvoltage of the unit as the optimization objective function of the transient characteristics. f 2(x). Therefore, the mathematical expression of the multi-objective optimization problem in this paper can be described as follows:

[0231] (18)

[0232] In the formula, x i is the decision vector, representing f pll , ξ pll parameters, X is a two-dimensional decision space.

[0233] Considering the unit f pll , ξ pll The initial parameters are 5Hz, 1.4, and f pll , ξ pllDue to the limited parameter optimization capability and in order to improve the running speed of the subsequent improved multi-objective particle swarm optimization algorithm, the value space of the decision vector is taken as [4,15] and [0.2,2] respectively.

[0234] S33: Improved multi-objective particle swarm optimization algorithm.

[0235] A1: Algorithm basis - PSO algorithm

[0236] The PSO algorithm is an optimization algorithm based on swarm intelligence of information sharing between particles. Each particle in the algorithm represents a potential solution to the problem to be optimized, and each particle corresponds to a fitness value determined by the fitness function. The PSO algorithm continuously updates the position and speed of particles based on the information interaction of individual historical optimal (Pbest) and global optimal (Gbest), thereby tracking the behavior of the optimal particle in the feasible solution space and iteratively searching for the optimal solution.

[0237] In the PSO algorithm, the particle speed 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 speed and position, that is, when the particle speed and position exceed their respective set boundary ranges, the particle speed and position at this time are adjusted to the boundary value.

[0238] (19)

[0239] (20)

[0240] In the formula, i Number the particles; ω is the inertia weight; k is the current iteration number; r 1. r 2 is a random number between [0,1]; c 1. c 2 is the individual and overall learning factor, and its value is 1.5.

[0241] A2: Population Update

[0242] For the MOPSO algorithm, change ω The size of can improve the algorithm search performance. ω Good for global search, smaller ω It is conducive to local search. Therefore, the algorithm needs a large ω Enhance global search capabilities, and later require smaller ω Perform precise local search. The most commonly used improvement ω The method is based on a linearly decreasing inertia weight strategy, as shown in formula (21).

[0243] (twenty one)

[0244] However, this method ω The decreasing slope remains unchanged. If no good particles are generated in the initial iteration process, it is easy to fall into the local optimum. To overcome this problem, this paper adopts the dynamic inertia weight based on the cosine decreasing strategy, as shown in formula (22). ω The change gradually increases at first and then decreases later, thereby improving the performance of the algorithm, such as Fig.11 shown.

[0245] (twenty two)

[0246] In the formula, C is the control factor, and its value is 1.2; ω max , ω min are the maximum and minimum inertia weights, with values ​​of 0.9 and 0.4; T is the maximum number of iterations, which is set to 100.

[0247] A3: External file selection

[0248] Compared with the PSO algorithm for single-objective optimization problems, the optimal solution obtained by the MOPSO algorithm for multi-objective optimization is not unique, but a set of non-dominant Pareto solutions. Therefore, a suitable strategy is needed to obtain the global optimal solution. This paper adopts the adaptive grid method to select elite individuals in the algorithm and update external archives. The specific steps are as follows:

[0249] (1) f 1(x) and f 2(x) is the objective function. 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 to calculate the modulus of the grid:

[0250] (twenty three)

[0251] (2) Traverse all particles in the external archive, number and record the grids where the particles are located, calculate the density of particles in the grid, and thus determine the probability of each grid being selected.

[0252] (twenty four)

[0253] in, i Nm is the number of particles in the mth grid, which is also the grid crowding degree. Therefore, the smaller the grid crowding degree, the greater the probability of the particle being selected.

[0254] At the same time, in order to improve the running speed of the algorithm, it is necessary to limit the number of particles in the external archive, that is, when the maximum capacity is exceeded, the first 50% of the particles with the largest grid congestion will be deleted. In this paper, the maximum capacity of the external archive is selected as 150.

[0255] (3) The grid and the optimal particle position are determined by roulette selection to prevent the MOPSO algorithm from falling into the local optimum. At the same time, in order to avoid the algorithm from repeatedly searching for the boundary position, when the population exceeds the limit, the algorithm does not use the update method of directly assigning the boundary value to the individual, but instead uses the update method of assigning the corresponding value beyond the limit to the particle position.

[0256] A4: Mutation Strategy

[0257] Since the population search space of the MOPSO algorithm will continue to 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 and divides the particle population into three parts, namely no mutation, uniform mutation and uneven mutation, thereby ensuring the global search capability of the algorithm in the early stage and the ability to gradually converge to the optimal solution in the later stage.

[0258] A5: Algorithm Process

[0259] This article f pll , ξ pll The parameters are decision variables. According to the model to be optimized constructed by equations (1) to (18), the improved MOPSO algorithm program is used for calculation. The specific flow chart of the algorithm execution is as follows: Fig.12 shown.

[0260] It can be seen from the above description that the control parameter optimization method of the direct-drive wind turbine set provided in the present application can optimize the control parameters of the direct-drive wind turbine set according to the impedance characteristics and the transient characteristics.

[0261] In one embodiment, see Fig. 22 , the use of the improved multi-objective particle swarm optimization algorithm and the optimization objective function to generate the recommended control parameters of the direct-drive wind turbine generator system includes:

[0262] S701: Initialize algorithm parameters of the multi-objective particle swarm optimization algorithm, speed information and position information of the particle population;

[0263] S702: Calculate the initial fitness value of each particle according to the optimization objective function, and initialize the particle external archive, individual historical optimal particle and global optimal particle;

[0264] S703: Iteratively execute the following steps until the preset maximum number of iterations is reached: using the improved multi-objective particle swarm optimization algorithm to update the velocity information and position information of the particles; calculating the fitness value of the updated particle population according to the preset particle mutation strategy, and updating the particle external archive according to the preset dominance relationship; updating the global optimal particle in a random selection manner according to the maximum capacity of the particle external archive, and determining the individual historical optimal particle according to the preset dominance relationship;

[0265] S704: Determine whether the number of executions reaches the maximum number of iterations; if so, output the global optimal particle and the individual historical optimal particle currently saved in the particle external archive to obtain a non-inferior solution set; wherein the non-inferior solution set includes the recommended control parameters of the direct-drive wind turbine group.

[0266] It is understandable that, during specific implementation, it can be performed according to the following steps.

[0267] Step 1: Load the objective function in equation (18), initialize the velocity and position information of the particle population, and set algorithm-related parameters;

[0268] Step 2: Calculate the fitness value of each initial particle f 1(x), f 2(x), initialize external files, Pbest and Gbest;

[0269] Step 3: Update the particle velocity and position information according to equations (19), (20) and (22), while considering the particle mutation strategy;

[0270] Step 4: Calculate the fitness value of the updated particle population, update and maintain the external archive according to the Pareto dominance relationship;

[0271] Step 5: Determine whether the external file has reached the maximum capacity limit. If so, delete the first 50% of the particles with the largest grid congestion. If not, proceed directly to Step 6.

[0272] Step 6: Update Gbest by randomly selecting in the external file using a roulette wheel, and select Pbest corresponding to each particle based on the Pareto dominance relationship;

[0273] Step 7: Determine whether the algorithm has reached the maximum number of iterations. If so, output the external file, that is, the obtained non-dominated solution set. Otherwise, jump to Step 3 and continue the loop.

[0274] From the above description, it can be seen that the direct-drive wind turbine control parameter optimization method provided in the present application can generate the recommended control parameters of the direct-drive wind turbine using the improved multi-objective particle swarm optimization algorithm and the optimization objective function.

[0275] Here is a specific embodiment:

[0276] ①Algorithm verification

[0277] In order 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, are selected for verification. The expressions of ZDT1 and ZDT3 are shown in the following formulas.

[0278] (25)

[0279] (1) ZDT1 expression

[0280] (26)

[0281] (2) ZDT3 expression

[0282] (27)

[0283] In the formula, the decision variable x i The value range of is [0,1], n is 30.

[0284] For the ZDT test function, the initial population size in the improved multi-objective particle swarm optimization algorithm is set to 100, the maximum number of iterations is 150, and the maximum capacity of the external archive is 200. The results of the improved multi-objective particle swarm optimization algorithm for the test functions ZDT1 and ZDT3 are shown in the figure. Fig.13 shown.

[0285] Depend on Fig.13 It can be seen that the Pareto solutions of the ZDT1 and ZDT3 test functions obtained based on the improved multi-objective particle swarm optimization algorithm have a good degree of overlap with their respective true Pareto optimal frontiers, which verifies the feasibility and effectiveness of the improved multi-objective particle swarm optimization algorithm proposed in this paper.

[0286] ②Results analysis

[0287] In this paper, the optimization objective function (18) of PMSG impedance characteristics and transient characteristics is used to f pll , ξ pllThe parameters are decision variables, and their value spaces are selected as [4,15] and [0.2,2] respectively. Then, the improved multi-objective particle swarm optimization algorithm is used to perform parameter optimization analysis.

[0288] For the optimization objective function (18), the initial population size in the improved multi-objective particle swarm optimization algorithm is set to 100, the maximum number of iterations is 100, and the maximum capacity of the external archive is 150. The parameter optimization results of the improved multi-objective particle swarm optimization algorithm for the objective function (18) are as follows: Fig.14 shown.

[0289] Depend on Fig.14 It can be seen that in the improved multi-objective particle swarm optimization algorithm, as f pll , ξ pll Parameter changes, objective function f 1(x) and f The fitness value of 2(x) is negatively correlated, which indicates that within certain frequency bands, the PMSG impedance characteristics and fault ride-through characteristics do have a certain degree of mutual restriction, which easily leads to the problem of "taking care of one and losing the other".

[0290] In addition, according to the fitness value of the objective function under the initial parameters of the system: f 1(x)=1.5945(91.36°) f 2(x)=1.1428, and the optimization goal of the objective function of this paper can be found in Fig.14 The parameter optimization scheme (enclosed by the red rectangle) that takes into account both the PMSG impedance characteristics and transient characteristics is obtained.

[0291] Therefore, in order to optimize the unit impedance characteristics as much as possible while suppressing the PMSG transient overvoltage, this paper selects Fig.14 The parameters at point A in the figure are used as a parameter optimization scheme that takes into account both the PMSG impedance characteristics and transient characteristics. f pll , ξ pll The parameters are 11.1702 and 0.3015 respectively. At this time, the fitness value of the objective function is: f 1(x)=1.2869(73.73°) f 2(x)=1.0863, both of them have decreased compared with the initial parameters. The unit impedance characteristics and transient characteristics waveforms are shown in Fig.15 shown.

[0292] Depend on Fig.15From (a), we can see that compared with the initial parameters, the impedance amplitude of PMSG in the frequency band [20,90] Hz under the parameter optimization scheme at point A is reduced. The unit impedance phase angle at the frequency of 61 Hz is increased from the original -91.36° to -73.73°, so that the unit meets the impedance phase requirements in the frequency band [61,100] Hz, increases the system phase margin, and optimizes the PMSG impedance characteristics.

[0293] Depend on Fig.15 From (b), we can see that compared with the initial parameters, the parameters at point A ξ pll This causes reactive power fluctuations in the PMSG at the time of fault recovery and increases the transient recovery process time; f pll The increase makes the reactive power response speed of PMSG faster at the moment of fault recovery, and the transient overvoltage amplitude decreases from 1.141pu to 1.082pu, which effectively suppresses the transient overvoltage at the PMSG machine end.

[0294] In summary, the parameter optimization scheme of point A satisfies the optimization goal of suppressing the transient overvoltage at the unit end while effectively optimizing its impedance characteristics, and comprehensively optimizes the unit impedance characteristics 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 takes into account both impedance characteristics and fault ride-through characteristics.

[0295] In summary, the PMSG control parameter optimization method based on the improved multi-objective particle swarm optimization algorithm proposed in the present invention, which takes into account both impedance characteristics and fault ride-through characteristics, solves the "loss of one thing while focusing on another" problem of the above-mentioned units, comprehensively optimizes the impedance characteristics and transient characteristics of the units, and provides a theoretical basis for the stable operation control of the system after the new energy station is connected to the power grid, which has certain engineering guidance significance.

[0296] In summary:

[0297] 1) In the embodiment of the present application, an impedance model of the PMSG and a PMSG transient reactive voltage analysis model considering the dynamic response characteristics of the phase-locked loop are established according to the main circuit topology and control structure of the PMSG unit, and the accuracy of the impedance analysis model and the transient analysis model are verified respectively by means of impedance sweep and time domain simulation.

[0298] 2) In the embodiment of the present application, the PMSG control bandwidth and damping ratio parameters are used to comprehensively analyze the influence of the control parameter changes on the two characteristics of the PMSG from the two aspects of the unit's impedance characteristics and fault ride-through characteristics. Then, the optimization goal is to meet the unit impedance phase requirements and suppress the unit transient overvoltage. f pll , ξ pllAs the decision variable, 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 impedance characteristics and fault ride-through characteristics.

[0299] Based on the same inventive concept, the embodiment of the present application also provides a direct-drive wind turbine control parameter optimization device, which can be used to implement the method described in the above embodiment, as described in the following embodiment. Since the principle of solving the problem by the direct-drive wind turbine control parameter optimization device is similar to 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 method based on software performance benchmark determination, and the repeated parts will not be repeated. As used below, the term "unit" or "module" can be a combination of software and / or hardware that implements predetermined functions. Although the system described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0300] In one embodiment, see Fig.23 In order to take into account both the impedance characteristics and fault ride-through characteristics of a direct-drive wind turbine generator set and optimize the control parameters of the direct-drive wind turbine generator set, the present application provides a direct-drive wind turbine generator set control parameter optimization device, including: an impedance characteristic analysis unit 2301, a transient characteristic analysis unit 2302 and a control parameter optimization unit 2303.

[0301] The impedance characteristic analysis unit 2301 is used to perform impedance modeling on the direct-drive wind turbine according to the topological structure and control structure of the direct-drive wind turbine, and analyze the influence of each controller bandwidth and damping ratio parameter on the impedance characteristic;

[0302] 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 analyze the influence of each controller bandwidth and damping ratio parameter on the transient characteristics;

[0303] The control parameter optimization unit 2303 is used to optimize the control parameters of the direct-drive wind turbine generator system according to the impedance characteristics and the transient characteristics.

[0304] In one embodiment, see Fig.24 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.

[0305] A first expression generation module 2401 is used to generate a voltage-current relationship expression at a grid-side converter, a passive filter and a common connection point according to the topology structure and the control structure;

[0306] A second expression generation module 2402 is used to generate a voltage component expression of the DC bus, a sine and cosine frequency domain expression of the phase-locked loop, and a relationship expression between the grid-side converter current and the modulation signal according to the topology structure and the control structure;

[0307] The 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 group.

[0308] In one embodiment, see Fig.25 The impedance characteristic analysis unit 2301 includes: a first three-parameter relationship generation module 2501 and an impedance optimization suggestion generation module 2502.

[0309] A first three-parameter relationship generating module 2501 is used to determine the relationship between the bandwidth, damping ratio and proportional-integral parameter of each controller according to the topological structure and the control structure;

[0310] The impedance optimization suggestion generating module 2502 is used to 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 according to the relationship between the bandwidth, damping ratio and proportional-integral parameter of each controller and the positive-negative sequence analytical impedance model, and generate impedance characteristic optimization suggestions.

[0311] In one embodiment, see Fig.26 The transient characteristic analysis unit 2302 includes: a third expression generation module 2601 and a transient model construction module 2602.

[0312] The third expression generation module 2601 is used to determine the filter model expression of the grid-side converter, the machine-end voltage expression, the voltage-current relationship expression of the output port of the grid-side converter, and the grid voltage expression before and after the fault occurs according to the dynamic response characteristics of the phase-locked loop;

[0313] The transient model construction module 2602 is used to substitute the filter model of the grid-side converter, the machine-end voltage expression and the grid voltage before and after the fault occurs 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.

[0314] In one embodiment, see Fig. 27 The transient characteristic analysis unit 2302 includes: a second three-parameter relationship generation module 2701 and a transient optimization suggestion generation module 2702.

[0315] A second three-parameter relationship generating module 2701 is used to determine the relationship between the bandwidth, damping ratio and proportional-integral parameter of each controller according to the topological structure and the control structure;

[0316] The transient optimization suggestion generation module 2702 is used to analyze the influence of the current inner loop control bandwidth and damping ratio, the phase-locked loop control bandwidth and damping ratio on the transient characteristics according to the relationship between the bandwidth, damping ratio and proportional-integral parameter of each controller and the transient reactive voltage model of the unit, and generate transient characteristic optimization suggestions.

[0317] In one embodiment, see Fig.28 The control parameter optimization unit 2303 includes: an objective function construction module 2801 and a control parameter recommendation module 2802.

[0318] An objective function construction module 2801 is used to construct an optimization objective function according to the impedance characteristic optimization suggestion and the transient characteristic optimization suggestion;

[0319] The control parameter recommendation module 2802 is used to generate the recommended control parameters of the direct-drive wind turbine generator system by using the improved multi-objective particle swarm optimization algorithm and the optimization objective function.

[0320] In one embodiment, see Fig.29 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-inferior solution set generation module 2904.

[0321] Algorithm initialization module 2901, used to initialize algorithm parameters of the multi-objective particle swarm optimization algorithm, speed information and position information of the particle population;

[0322] A particle initialization module 2902 is used to calculate the initial fitness value of each particle according to the optimization objective function, and initialize the particle external archive, the individual historical optimal particle and the global optimal particle;

[0323] Iterative execution module 2903 is used to iteratively execute the following steps until the preset maximum number of iterations is reached: using the improved multi-objective particle swarm optimization algorithm to update the speed information and position information of the particles; calculating the fitness value of the updated particle population according to the preset particle mutation strategy, and updating the particle external archive according to the preset dominance relationship; updating the global optimal particle in a random selection manner according to the maximum capacity of the particle external archive, and determining the individual historical optimal particle according to the preset dominance relationship;

[0324] The non-inferior solution set generation module 2904 is used to determine whether the number of executions reaches the maximum number of iterations; if so, output the global optimal particle and the individual historical optimal particle currently saved in the particle external archive to obtain a non-inferior solution set; wherein the non-inferior solution set includes the recommended control parameters of the direct-drive wind turbine group.

[0325] From the hardware level, in order to take into account the impedance characteristics and fault ride-through characteristics of the direct-drive wind turbine set, the control parameters of the direct-drive wind turbine set are optimized. The present application provides an embodiment of an electronic device for implementing all or part of the contents of the direct-drive wind turbine set control parameter optimization method. The electronic device specifically includes the following contents:

[0326] Processor, memory, communication interface and bus; wherein the processor, memory and communication interface communicate with each other through the bus; the communication interface is used to realize information transmission between the direct-drive wind turbine control parameter optimization device and related equipment such as the core business system, user terminal and related database; the logic controller can be a desktop computer, a tablet computer and a mobile terminal, etc., but the present embodiment is not limited thereto. In the present embodiment, the logic controller can be implemented with reference to the embodiment of the direct-drive wind turbine control parameter optimization method and the embodiment of the direct-drive wind turbine control parameter optimization device in the embodiment, and the contents thereof are incorporated herein, and the repeated parts are not repeated.

[0327] It is understandable that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0328] In practical applications, part of the direct-drive wind turbine control parameter optimization method can be executed on the electronic device side as described above, or all operations can be completed in the client device. The specific selection can be based on the processing capability of the client device and the limitations of the user's usage scenario. This application does not limit this. If all operations are completed in the client device, the client device may also include a processor.

[0329] The client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side, and other implementation scenarios 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, or a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0330] Fig.30 FIG. 9 is a schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Fig.30 As shown, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is worth noting that Fig.30 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0331] In one embodiment, the function of the direct-drive wind turbine control parameter optimization method can be integrated into the central processor 9100. The central processor 9100 can be configured to perform the following control:

[0332] S101: performing impedance modeling on the direct-drive wind turbine generator set according to the topological structure and control structure of the direct-drive wind turbine generator set, and analyzing the influence of each controller bandwidth and damping ratio parameter on the impedance characteristics;

[0333] S102: Establishing a transient reactive voltage model of the unit based on the dynamic response characteristics of the phase-locked loop, and analyzing the influence of each controller bandwidth and damping ratio parameter on the transient characteristics;

[0334] S103: Optimizing control parameters of the direct-drive wind turbine generator system according to the impedance characteristics and the transient characteristics.

[0335] From the above description, it can be seen that the direct-drive wind turbine control parameter optimization method and device provided in the present application can establish an impedance model and a transient reactive voltage model of the direct-drive wind turbine, and analyze the influence of the unit control bandwidth parameters and the damping ratio parameters on its impedance characteristics and transient characteristics respectively. Then, based on the influence law of the unit parameters on the impedance characteristics and transient characteristics of the direct-drive wind turbine, the main influencing factors are determined. The phase-locked loop control bandwidth and the damping ratio are used as decision variables, and the improved multi-objective particle swarm optimization algorithm is adopted to realize the optimization of the control parameters of the direct-drive wind turbine that takes into account the impedance characteristics and the fault ride-through characteristics, and comprehensively optimize the impedance characteristics and the fault ride-through characteristics of the direct-drive wind turbine, which has certain engineering guidance significance.

[0336] 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 direct-drive wind turbine control parameter optimization device 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.

[0337] like Fig.30 As shown, the electronic device 9600 may also 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 have to include Fig.30 In addition, the electronic device 9600 may also include Fig.30 For components not shown, reference may be made to the prior art.

[0338] like Fig.30 As shown, the central processing unit 9100 is sometimes also referred to as a controller or an operation control, and 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.

[0339] The memory 9140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 9100 may execute the program stored in the memory 9140 to implement information storage or processing, etc.

[0340] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display may be, for example, an LCD display, but is not limited thereto.

[0341] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 9140 may 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, which is used to store application programs and function programs or processes for executing the operation of the electronic device 9600 through the central processor 9100.

[0342] 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 communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0343] The communication module 9110 is a transmitter / receiver 9110 that sends and receives signals via an antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0344] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby realizing a common telecommunication function. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, so that the microphone 9132 can be used to record on the local machine, and the sound stored on the local machine can be played through the speaker 9131.

[0345] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the method for optimizing control parameters of a direct-drive wind turbine set in the above embodiment, where the execution subject is a server or a client. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, all the steps of the method for optimizing control parameters of a direct-drive wind turbine set in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0346] S101: performing impedance modeling on the direct-drive wind turbine generator set according to the topological structure and control structure of the direct-drive wind turbine generator set, and analyzing the influence of each controller bandwidth and damping ratio parameter on the impedance characteristics;

[0347] S102: Establishing a transient reactive voltage model of the unit based on the dynamic response characteristics of the phase-locked loop, and analyzing the influence of each controller bandwidth and damping ratio parameter on the transient characteristics;

[0348] S103: Optimizing control parameters of the direct-drive wind turbine generator system according to the impedance characteristics and the transient characteristics.

[0349] From the above description, it can be seen that the direct-drive wind turbine control parameter optimization method and device provided in the present application can establish an impedance model and a transient reactive voltage model of the direct-drive wind turbine, and analyze the influence of the unit control bandwidth parameters and the damping ratio parameters on its impedance characteristics and transient characteristics respectively. Then, based on the influence law of the unit parameters on the impedance characteristics and transient characteristics of the direct-drive wind turbine, the main influencing factors are determined. The phase-locked loop control bandwidth and the damping ratio are used as decision variables, and the improved multi-objective particle swarm optimization algorithm is adopted to realize the optimization of the control parameters of the direct-drive wind turbine that takes into account the impedance characteristics and the fault ride-through characteristics, and comprehensively optimize the impedance characteristics and the fault ride-through characteristics of the direct-drive wind turbine, which has certain engineering guidance significance.

[0350] It should be understood by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented 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.

[0351] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0352] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0353] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0354] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for optimizing control parameters of a direct-drive wind turbine generator system, characterized in that: include: Conducting impedance modeling on the direct-drive wind turbine generator set according to the topological structure and control structure of the direct-drive wind turbine generator set, and analyzing impedance characteristics; The transient reactive voltage model of the unit is established based on the dynamic response characteristics of the phase-locked loop, and the influence of each controller bandwidth and damping ratio parameters on the transient characteristics is analyzed; Optimizing the control parameters of the direct-drive wind turbine generator system according to the impedance characteristics and the transient characteristics; The step of optimizing the control parameters of the direct-drive wind turbine generator system according to the impedance characteristics and the transient characteristics includes: The optimization objective function is constructed based on the impedance characteristic optimization suggestions and the transient characteristic optimization suggestions; the minimum absolute value of the unit impedance phase angle is used as the optimization objective function of the impedance characteristic; the minimum value of the unit transient overvoltage is used as the optimization objective function of the transient characteristic; Generate the recommended control parameters of the direct-drive wind turbine generator system by using the improved multi-objective particle swarm optimization algorithm and the optimization objective function; Wherein, the use of the improved multi-objective particle swarm optimization algorithm and the optimization objective function to generate the recommended control parameters of the direct-drive wind turbine generator system includes: Initialize the algorithm parameters of the multi-objective particle swarm optimization algorithm, the speed information and position information of the particle population; Calculate the initial fitness value of each particle according to the optimization objective function, and initialize the particle external archive, individual historical optimal particle and global optimal particle; Iteratively execute the following steps until the preset maximum number of iterations is reached: using an improved multi-objective particle swarm optimization algorithm to update the velocity information and position information of the particles; calculating the fitness value of the updated particle population according to a preset particle mutation strategy, and updating the particle external archive according to a preset dominance relationship; updating the global optimal particle in a random selection manner according to the maximum capacity of the particle external archive, and determining the individual historical optimal particle according to a preset dominance relationship; Determine whether the number of executions reaches the maximum number of iterations; if so, output the global optimal particle and the individual historical optimal particle currently saved in the particle external archive to obtain a non-inferior solution set; wherein the non-inferior solution set includes the recommended control parameters of the direct-drive wind turbine group.

2. The direct-drive wind turbine control parameter optimization method according to claim 1, characterized in that: The impedance modeling of the direct-drive wind turbine generator set according to the topological structure and control structure of the direct-drive wind turbine generator set and the analysis of the impedance characteristics include: Generate a voltage-current relationship expression at the grid-side converter, the passive filter and the common connection point according to the topological structure and the control structure; Generate a voltage component expression of the DC bus, a sine and cosine frequency domain expression of the phase-locked loop, and a relationship expression between the grid-side converter current and the modulation signal according to the topological structure and the control structure; The voltage component expression, the sine-cosine frequency domain expression, and the relationship expression between the grid-side converter current and the modulation signal are substituted into the voltage-current relationship expression to obtain the positive and negative sequence analytical impedance model of the direct-drive wind turbine set.

3. The direct-drive wind turbine control parameter optimization method according to claim 2, characterized in that: The impedance modeling of the direct-drive wind turbine generator set according to the topological structure and control structure of the direct-drive wind turbine generator set and the analysis of the impedance characteristics include: Determine the relationship between the bandwidth, damping ratio and proportional-integral parameter of each controller according to the topological structure and the control structure; According to the relationship between the bandwidth, damping ratio and proportional-integral parameters of each controller and the positive and negative sequence analytical impedance model, 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 are analyzed respectively, and impedance characteristic optimization suggestions are generated.

4. The direct-drive wind turbine control parameter optimization method according to claim 1, characterized in that: The transient reactive voltage model of the unit is established based on the dynamic response characteristics of the phase-locked loop, and the influence of each controller bandwidth and damping ratio parameter on the transient characteristics is analyzed, including: Determine the filter model expression of the grid-side converter, the machine-end voltage expression, the voltage-current relationship expression of the output port of the grid-side converter, and the grid voltage expression before and after the fault occurs according to the dynamic response characteristics of the phase-locked loop; The filter model of the grid-side converter, the machine-end voltage expression, and the grid voltage before and after the fault occurs are substituted 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.

5. The direct-drive wind turbine control parameter optimization method according to claim 4, characterized in that: The transient reactive voltage model of the unit is established based on the dynamic response characteristics of the phase-locked loop, and the influence of each controller bandwidth and damping ratio parameter on the transient characteristics is analyzed, including: Determine the relationship between the bandwidth, damping ratio and proportional-integral parameter of each controller according to the topological structure and the control structure; According to 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, the phase-locked loop control bandwidth and damping ratio on the transient characteristics are analyzed respectively, and transient characteristic optimization suggestions are generated.

6. A direct-drive wind turbine control parameter optimization device, characterized in that: include: An impedance characteristic analysis unit, used to perform impedance modeling on the direct-drive wind turbine generator set according to the topological structure and control structure of the direct-drive wind turbine generator set, and analyze the impedance characteristics; The transient characteristics 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 analyze the influence of each controller bandwidth and damping ratio parameters on the transient characteristics; A control parameter optimization unit, used for optimizing the control parameters of the direct-drive wind turbine generator set according to the impedance characteristics and the transient characteristics; Wherein, the control parameter optimization unit includes: The objective function construction module is used to construct an optimization objective function according to the impedance characteristic optimization suggestions and the transient characteristic optimization suggestions; wherein the minimum absolute value of the unit impedance phase angle is used as the optimization objective function of the impedance characteristic; and the minimum transient overvoltage of the unit is used as the optimization objective function of the transient characteristic; A control parameter recommendation module, used to generate recommended control parameters of the direct-drive wind turbine generator system by using an improved multi-objective particle swarm optimization algorithm and the optimization objective function; Wherein, the control parameter recommendation module includes: Algorithm initialization module, used to initialize the algorithm parameters of the multi-objective particle swarm optimization algorithm, the speed information and position information of the particle population; A particle initialization module is used to calculate the initial fitness value of each particle according to the optimization objective function, and initialize the particle external archive, the individual historical optimal particle and the global optimal particle; The iterative execution module is used to iteratively execute the following steps until a preset maximum number of iterations is reached: using an improved multi-objective particle swarm optimization algorithm to update the velocity information and position information of the particles; calculating the fitness value of the updated particle population according to a preset particle mutation strategy, and updating the particle external archive according to a preset dominance relationship; updating the global optimal particle in a random selection manner according to the maximum capacity of the particle external archive, and determining the individual historical optimal particle according to the preset dominance relationship; The non-inferior solution set generation module is used to determine whether the number of executions reaches the maximum number of iterations; if so, output the global optimal particle and the individual historical optimal particle currently saved in the particle external archive to obtain a non-inferior solution set; wherein the non-inferior solution set includes the recommended control parameters of the direct-drive wind turbine group.

7. The direct-drive wind turbine control parameter optimization device according to claim 6, characterized in that: The impedance characteristic analysis unit comprises: A first expression generation module, used to generate a voltage-current relationship expression at a grid-side converter, a passive filter and a common connection point according to the topology structure and the control structure; A second expression generation module is used to generate a voltage component expression of the DC bus, a sine and cosine frequency domain expression of the phase-locked loop, and a relationship expression between the grid-side converter current and the modulation signal according to the topological structure and the 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 group.

8. The direct-drive wind turbine control parameter optimization device according to claim 7, characterized in that: The impedance characteristic analysis unit comprises: A first three-parameter relationship generating module is used to determine the relationship between the bandwidth, damping ratio and proportional-integral parameter of each controller according to the topological structure and the control structure; The impedance optimization suggestion generation module is used to 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 according to the relationship between the bandwidth, damping ratio and proportional-integral parameter of each controller and the positive-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 comprises: A third expression generation module is used to determine the filter model expression of the grid-side converter, the machine-end voltage expression, the voltage-current relationship expression of the output port of the grid-side converter, and the grid voltage expression before and after the fault occurs according to 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 machine-end voltage expression and the grid voltage before and after the fault occurs 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 comprises: A second three-parameter relationship generating module is used to determine the relationship between the bandwidth, damping ratio and proportional-integral parameter of each controller according to the topological structure and the control structure; The transient optimization suggestion generation module is used to analyze the influence of the current inner loop control bandwidth and damping ratio, the phase-locked loop control bandwidth and damping ratio on the transient characteristics according to the relationship between the bandwidth of each controller, the damping ratio and the proportional-integral parameter 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, the steps of the direct-drive wind turbine control parameter optimization method according to any one of claims 1 to 5 are implemented.

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

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

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