A method and system for constructing equivalent simulation models of multi-machine wind farms

Abstract

The present invention relates to the field of distributed energy simulation, and in particular to a method and system for constructing an equivalent simulation model for a multi-type wind farm. The method comprises: collecting wind turbine information and constructing a multivariate scaled wind farm model, wherein the multivariate scaled wind farm model mainly includes a wind turbine dynamics model and a converter control model; constructing an embedded constant admittance equivalent model and collecting the control characteristics of an ideal switch, solving the control parameters of the embedded constant admittance equivalent model based on the control characteristics, and performing parameter optimization based on steady-state errors to replace the converter control model; performing model equivalence processing on several wind turbine categories according to the multivariate scaled wind farm model to obtain a multivariate wind turbine equivalent simulation model. The present invention effectively solves the problems in existing wind farm simulations, such as low simulation efficiency due to excessive converter calculations and inaccurate simulation results caused by the inability of the equivalent modeling method for multi-type wind farms to fully consider the dynamic characteristics and control strategies of different types of wind turbines.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed energy simulation, and in particular to a method and system for constructing an equivalent simulation model of a multi-machine wind farm. Background Art

[0002] With the rapid development of the new energy industry and the continuous advancement of smart grid technology, wind power generation, as an important component of green energy, occupies an increasingly important position in the global energy structure transformation. The operating efficiency and grid-connected safety of wind farms are highly dependent on the dynamic characteristics, precise electrical control and fast system simulation capabilities of wind turbines. Modern wind turbines usually adopt diversified structures, including doubly fed induction generators, direct-drive permanent magnet synchronous generators and other types, and are equipped with complex converter control systems and intelligent pitch adjustment to maximize energy capture and improve grid connection quality.

[0003] Traditional simulation methods usually use the electromagnetic transient full topology model of the binary resistance switch model. Although it can ensure accuracy, the simulation scale is huge, the matrix order is high, and the amount of calculation increases sharply during the simulation process, which seriously reduces the simulation efficiency and even causes the computing equipment to freeze or crash. It is difficult to meet the real-time analysis needs, especially in large wind farms. In addition, the research on equivalent modeling of wind farms mainly focuses on the simplification of single machines. Common methods include single-machine equivalent method, group equivalent method and dynamic aggregation model. Although the single-machine equivalent method can improve the calculation speed, it ignores the essential differences in the dynamic characteristics of different machine types. In particular, in hybrid wind farms containing doubly fed and direct-drive models, its transient response error is significant. The group equivalent method does not fully consider core factors such as machine type and converter control strategy, resulting in blurred group boundaries and the equivalent model is difficult to accurately reflect the coupling effect of multiple machine types.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present disclosure and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0005] The present invention provides a method and system for constructing a multi-machine wind farm equivalent simulation model, which can effectively solve the problems in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A method for constructing an equivalent simulation model of a multi-machine wind farm, the method comprising:

[0008] Collecting wind turbine information and building a multi-scale wind farm model, which mainly includes a wind turbine dynamics model and a converter control model;

[0009] Constructing an embedded constant admittance equivalent model and collecting control characteristics of an ideal switch, solving control parameters of the embedded constant admittance equivalent model based on the control characteristics, optimizing the parameters based on the steady-state error, and replacing the converter control model, wherein the control characteristics include steady-state characteristics and transient characteristics;

[0010] Model equivalence processing is performed on several types of wind turbines according to the multivariate large-scale wind farm model to obtain a multivariate wind turbine equivalent simulation model.

[0011] Furthermore, model equivalence processing is performed on several wind turbine types according to the multivariate scale wind farm model, including:

[0012] Collect the real-time wind speed, perform wind speed equalization processing on the real-time wind speed based on the wind turbine information, and obtain the effective wind speed:

[0013] ;

[0014] Where K is the wake attenuation coefficient, is the thrust coefficient, is the wake diffusion coefficient, is the effective wind speed, is the real-time wind speed, is the wake radius from the fan x along the wind speed and direction, and R is the fan blade radius;

[0015] The wind turbine performance parameters of each wind turbine are obtained according to the wind turbine group information, and a plurality of the wind turbine performance parameters are subjected to equivalent processing to obtain a multivariate wind turbine equivalent simulation model.

[0016] Furthermore, the wind turbine performance parameters of each wind turbine are obtained according to the wind turbine group information, and several wind turbine performance parameters are subjected to equivalent processing to obtain a multivariate wind turbine equivalent simulation model, including:

[0017] The wind turbine generator set includes the doubly-fed wind turbine generator and the direct-drive permanent-magnet synchronous wind turbine generator, and the doubly-fed wind turbine generator and the direct-drive permanent-magnet synchronous wind turbine generator have the same capacity equivalence:

[0018] Calculating the proportion of the capacity of each wind turbine in the total capacity of the same type based on the plurality of wind turbine performance parameters, and performing weighted integration based on the respective capacity proportions of the wind turbine performance parameters to obtain a parameter equivalence factor;

[0019] ;

[0020] S represents the sum of the rated capacities of all fans. represents the electromagnetic power of the fan, represents the mechanical power input by the wind turbine, e represents the wind turbine of the equivalent wind turbine, Representative The electromagnetic power of the typhoon, Indicates the Rated capacity of the typhoon, Indicates the The capacity of the typhoon machine accounts for the proportion of the equivalent value machine, Indicates the rated capacity of the first fan;

[0021] The doubly-fed wind turbine generator is weighted and equivalently evaluated according to the parameter equivalent factor:

[0022] ;

[0023] is the excitation reactance of the equivalent wind turbine, is the stator reactance of the equivalent wind turbine, Indicates the The stator reactance of the typhoon turbine, Indicates the Rated capacity of the typhoon, Indicates the rated capacity of the equivalent fan;

[0024] For the direct-drive permanent magnet synchronous wind turbine generator, weighted equivalence is performed on the direct-drive permanent magnet synchronous wind turbine generator according to the parameter equivalent factor:

[0025] ;

[0026] represents the stator winding inductance of the equivalent motor, represents the stator winding resistance of the equivalent motor, is the equivalent motor torque damping coefficient, Indicates the The inductance of the stator winding of a direct-drive permanent magnet wind turbine generator, Indicates the stator winding resistance of a direct-drive permanent magnet wind turbine generator, Indicates the The torque damping coefficient of a direct-drive permanent magnet wind turbine generator is n, and n represents the number of the direct-drive permanent magnet wind turbine generators.

[0027] Furthermore, solving the control parameters of the embedded constant admittance equivalent model according to the control characteristics includes:

[0028] According to the steady-state characteristics of the ideal switch, the final value theorem is used to solve the steady-state characteristics parameters:

[0029] ;

[0030] in, is the voltage coefficient of the equivalent current source, is the current coefficient of the equivalent current source, the subscript on represents the on-coefficient, and off represents the off-coefficient.

[0031] Furthermore, solving the control parameters of the embedded constant admittance equivalent model according to the control characteristics includes:

[0032] The discrete-time system is modeled according to the transient characteristics and organized into a matrix form to solve the parameters corresponding to the transient characteristics and the matrix equation that characterizes the relationship between the voltage and current of the switching device:

[0033] ;

[0034] Wherein, subscripts 1 and 2 represent the upper and lower corresponding switches of a single bridge arm of the switching device in the embedded constant admittance equivalent model, subscripts n and n-1 represent the nth and n-1th moments, G is the equivalent constant admittance parameter, 、 is the instantaneous DC and AC voltage at both ends of the single bridge arm, ;

[0035] According to the matrix equation, a mathematical method is used to calculate the zero point of the spectrum radius and obtain the voltage coefficient and current coefficient:

[0036]

[0037] Wherein, subscripts 1 and 2 represent the upper and lower corresponding switches of a single bridge arm of the switching device in the embedded constant admittance equivalent model. Indicates voltage coefficient represents the current coefficient, and G is the equivalent constant admittance parameter.

[0038] Furthermore, parameter optimization is performed based on the steady-state error, including:

[0039] Construct a steady-state error analysis model:

[0040] ;

[0041] Wherein, subscripts 1 and 2 represent the upper and lower corresponding switches of a single bridge arm of the switch device in the embedded constant admittance equivalent model, G is the equivalent constant admittance parameter, 、 is the instantaneous DC and AC voltage at both ends of the single bridge arm, ;

[0042] Will and Substitute into the steady-state error analysis model respectively:

[0043] ;

[0044] when , then select As the conduction steady-state parameter;

[0045] For shutdown conditions, and Substitute them into the steady-state error analysis model respectively, calculate the steady-state errors respectively, compare them, and select the one with the smallest steady-state error as the shutdown steady-state parameter.

[0046] Furthermore, the wind turbine dynamics model includes:

[0047] The input power of the fan blade is:

[0048] ;

[0049] in, is the mass density of air, R is the radius of the wind wheel, is the real-time wind speed, is the wind energy utilization coefficient, is the pitch angle.

[0050] Furthermore, the converter control model includes:

[0051] ;

[0052] Among them, the subscripts d and q are the d-axis and q-axis components respectively. is the DC bus capacitance, 、 、 、 are the grid side voltage, current, equivalent inductance and resistance respectively, is the AC side output voltage, is the AC side output voltage, is the DC bus voltage, represents the synchronous angular velocity, S is the output power, is the grid-side output current.

[0053] A system for constructing equivalent simulation models of multiple wind farm types, the system comprising:

[0054] The model building module collects wind turbine information and builds a multi-scale wind farm model, which includes a wind turbine dynamics model and a converter control model.

[0055] A parameter solving module constructs an embedded constant admittance equivalent model and collects control characteristics of an ideal switch. The control parameters of the embedded constant admittance equivalent model are solved based on the control characteristics. The parameters are optimized based on the steady-state error and the converter control model is replaced. The control characteristics include steady-state characteristics and transient characteristics.

[0056] The equivalent processing module performs model equivalent processing on several wind turbine types according to the multivariate large-scale wind farm model to obtain a multivariate wind turbine equivalent simulation model.

[0057] The technical solution of the present invention can achieve the following technical effects:

[0058] This effectively solves the problem in existing wind farm simulations that the equivalent modeling method for multi-machine wind farms cannot fully consider the dynamic characteristics and control strategies of different models, resulting in inaccurate simulation results and low calculation efficiency.

[0059] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0061] Figure 1 A flowchart of a method for constructing an equivalent simulation model for a multi-machine wind farm is provided;

[0062] Figure 2 It is the mathematical model diagram of the switch;

[0063] Figure 3 This is a model diagram of a pair of bridge arms in the converter. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0065] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0066] Embodiment 1;

[0067] like Figure 1 、 Figure 2 、 Figure 3 As shown, the present application provides a method for constructing a multi-type wind farm equivalent simulation model, the method comprising:

[0068] S10: Collect wind turbine information and build a multi-scale wind farm model, which includes a wind turbine dynamics model and a converter control model;

[0069] S20: Construct an embedded constant admittance equivalent model and collect the control characteristics of the ideal switch. Based on the control characteristics, solve the control parameters of the embedded constant admittance equivalent model. Optimize the parameters based on the steady-state error and replace the converter control model. The control characteristics include steady-state characteristics and transient characteristics.

[0070] S30: performing model equivalence processing on several types of wind turbines according to the multivariate large-scale wind farm model to obtain a multivariate wind turbine equivalent simulation model.

[0071] Specifically, the basic information of each wind turbine in the wind farm is collected, including the type of turbine, rated power, control strategy, etc. Based on this information, a multi-scale wind farm model is constructed. The multi-scale wind farm model includes a dynamic model of the wind turbine and a converter control model. The wind turbine dynamic model describes the mechanical characteristics of the wind turbine, such as speed, torque response, etc.; the converter control model reflects the electrical coupling relationship between the wind turbine and the power grid, involving parameters such as power control and current control; then an embedded constant admittance equivalent model is constructed, and the steady-state characteristics and transient characteristics of the ideal switch are collected. The steady-state characteristics include the voltage, current and power output relationship of the wind turbine under stable working conditions; the transient characteristics involve the electrical response of the wind turbine under load changes or faults. The steady-state characteristics and transient characteristics are calculated, and the steady-state parameters and transient parameters of the embedded constant admittance equivalent model are solved respectively to accurately reflect the actual electrical behavior of the wind turbine; according to the solved steady-state parameters and transient parameters, the converter control model is replaced. By replacing the original converter control model with a model containing steady-state and transient parameters, the control characteristics of the wind turbine are accurately modeled, thereby improving the accuracy of the simulation model; according to the multivariate large-scale wind farm model, the various types of wind turbines in the wind farm are modeled equivalently. By equating the characteristics of different types of wind turbines, a multivariate wind turbine equivalent simulation model is obtained. The multivariate wind turbine equivalent simulation model can accurately reflect the dynamic characteristics and control strategies of various types of wind turbines in the wind farm, thereby improving the simulation accuracy and computational efficiency.

[0072] The technical solution of the present invention effectively solves the problem in existing wind farm simulation that the equivalent modeling method of multi-type wind farms cannot fully consider the dynamic characteristics and control strategies of different models, resulting in inaccurate simulation results and low calculation efficiency.

[0073] Furthermore, the model equivalence treatment is performed on several wind turbine types according to the multivariate scale wind farm model, including:

[0074] Collect real-time wind speed, perform wind speed equalization processing on the real-time wind speed based on wind turbine information, and obtain the effective wind speed:

[0075] ;

[0076] Where K is the wake attenuation coefficient, is the thrust coefficient, is the wake diffusion coefficient, is the effective wind speed, is the real-time wind speed, is the wake radius from the fan x along the wind speed and direction, and R is the fan blade radius;

[0077] The wind turbine performance parameters of each wind turbine are obtained according to the wind turbine group information, and several wind turbine performance parameters are processed to obtain a multivariate wind turbine equivalent simulation model.

[0078] As a preferred embodiment of the above, sensors are deployed in wind farms or wind turbines to collect wind speed data in the environment in real time. These data are processed based on real-time wind speed and are used to calculate the effective wind speed of each wind turbine after wind speed equivalence processing. The effective wind speed is adjusted by considering specific parameters of the wind turbine, such as power curve, wind turbine working status, etc. Especially in a multi-wind turbine system, wind speed equivalence processing needs to fully consider the influence of the wake effect. In order to simulate this effect more accurately, the wake attenuation coefficient K is introduced into the model to characterize the wind speed attenuation behind the wind turbine. The wake attenuation coefficient is usually determined by experimental data or simulation analysis to adjust the wind speed change and calculate a more accurate effective wind speed; secondly, the wake diffusion coefficient As an important part of the wind speed attenuation model, it is used to describe the diffusion characteristics of the wake behind the wind turbine. The wake diffusion will affect the wind speed distribution behind the wind turbine, and then affect the performance of the downstream wind turbine. According to the performance parameters of each wind turbine, such as blade radius, wind turbine rated power, working environment, etc., these parameters can be uniformly standardized to facilitate the equivalent conversion of the performance of wind turbines of different models in the same model, so that whether a single wind turbine or multiple wind turbines work in parallel, reasonable performance estimation can be made according to their actual load and working status, ensuring that the model can accurately reflect the synergy between wind turbines; Based on the above-processed data and wind turbine performance information, the conclusion Combining the wake attenuation and diffusion effects, a multivariate wind turbine equivalent simulation model was constructed. The multivariate wind turbine equivalent simulation model can dynamically adjust the overall operating status of the wind turbine under different wind speeds, wind turbine layouts and environmental conditions, and simulate the interaction effects between different wind turbines. The design of the multivariate wind turbine equivalent simulation takes into account multiple variables and corrects them through historical data and actual operating conditions to improve its prediction accuracy. During the implementation of the model, the wind turbine performance parameters need to be updated regularly, and the model algorithm needs to be continuously optimized in combination with factors such as wind speed data, wake effect, and wind turbine working status, so as to ensure that the wind farm can achieve the best power generation efficiency and system stability in actual operation.

[0079] Furthermore, the wind turbine performance parameters of each wind turbine are obtained based on the wind turbine group information, and several wind turbine performance parameters are processed to obtain a multivariate wind turbine equivalent simulation model, including:

[0080] The wind turbine generator set includes a doubly-fed wind turbine generator and a direct-drive permanent magnet synchronous wind turbine generator. The capacity of the doubly-fed wind turbine generator and the direct-drive permanent magnet synchronous wind turbine generator are the same:

[0081] Calculate the proportion of each wind turbine's capacity in the total capacity of the same type based on several wind turbine performance parameters, and perform weighted integration based on the proportion of each wind turbine's capacity based on the wind turbine performance parameters to obtain a parameter equivalence factor;

[0082] ;

[0083] S represents the sum of the rated capacities of all fans. represents the electromagnetic power of the fan, represents the mechanical power input by the wind turbine, e represents the wind turbine of the equivalent wind turbine, Representative The electromagnetic power of the typhoon, Indicates the Rated capacity of the typhoon, Indicates the The capacity of the typhoon machine accounts for the proportion of the equivalent value machine, Indicates the rated capacity of the first fan;

[0084] The weighted equivalent value of the doubly fed wind turbine is calculated based on the parameter equivalent factor:

[0085] ;

[0086] is the excitation reactance of the equivalent wind turbine, is the stator reactance of the equivalent wind turbine, Indicates the The stator reactance of the typhoon turbine, Indicates the Rated capacity of the typhoon, Indicates the rated capacity of the equivalent fan;

[0087] For the direct-drive permanent magnet synchronous wind turbine generator, the weighted equivalent value of the direct-drive permanent magnet synchronous wind turbine generator is calculated according to the parameter equivalent factor:

[0088] ;

[0089] Indicates the stator winding inductance of the equivalent motor, represents the stator winding resistance of the equivalent motor, is the equivalent motor torque damping coefficient, Indicates the The inductance of the stator winding of a direct-drive permanent magnet wind turbine generator, Indicates the stator winding resistance of a direct-drive permanent magnet wind turbine generator, Indicates the is the torque damping coefficient of a direct-drive permanent magnet wind turbine generator, and n is the number of direct-drive permanent magnet wind turbine generators.

[0090] As a preferred embodiment of the above, based on the type and performance parameters of the wind turbine, especially the difference between the doubly fed wind turbine and the direct-drive permanent magnet synchronous wind turbine, it is necessary to perform equivalent treatment on the wind turbine according to its rated capacity, operating characteristics and performance indicators. The doubly fed wind turbine and the direct-drive permanent magnet synchronous wind turbine can be equivalent in capacity, which means that they have the same capacity specifications and power generation efficiency. Therefore, they can be processed according to the same capacity ratio in the model. In order to ensure that the performance of each wind turbine can be reasonably reflected in the simulation model, it is first necessary to calculate the capacity ratio of each wind turbine in the total capacity of wind turbines of the same type. First, the total capacity of all wind turbines is obtained, and then the electromagnetic power of each wind turbine, that is, the output power of the wind turbine, is calculated. The capacity ratio of each wind turbine in the entire system is determined by the ratio of the rated capacity of the wind turbine to the total capacity. For each wind turbine, other performance parameters are weighted and integrated according to its capacity ratio to obtain a comprehensive parameter equivalence factor. The parameter equivalence factor can effectively integrate the electromagnetic power, mechanical power and other key performance parameters of the wind turbine into a representative value, which is convenient for unified calculation in subsequent wind turbine simulations. By calculating the first The capacity ratio of each wind turbine can be used to obtain the parameter equivalent factor of each wind turbine, which can then be used for the subsequent weighted equivalent processing of the wind turbines; next, based on the obtained parameter equivalent factor, the doubly fed wind turbine is subjected to weighted equivalent processing. The weighted equivalent process of the doubly fed wind turbine is mainly achieved through the excitation reactance and stator reactance of the equivalent wind turbine. In this process, the stator reactance of the wind turbine needs to be weighted according to the electromagnetic power and rated capacity of each wind turbine to ensure that the reactance of the equivalent wind turbine can accurately reflect the electrical characteristics of the actual wind turbine. Therefore, the excitation reactance and stator reactance of the equivalent wind turbine should be weighted and integrated through the capacity ratio of each wind turbine, and finally the total reactance of the equivalent wind turbine is calculated; for the equivalent processing of direct-drive permanent magnet synchronous wind turbines, a similar weighted equivalent method is used. In this process, the stator winding inductance, stator winding resistance and torque damping coefficient of each direct-drive permanent magnet synchronous wind turbine are first calculated. These parameters are closely related to the capacity, rated power and working status of the wind turbine. Therefore, they need to be weighted and calculated according to the capacity ratio of each wind turbine to obtain the overall electrical characteristics of the equivalent direct-drive permanent magnet synchronous wind turbine. In the multivariate wind turbine equivalent simulation model, the weighted parameters, such as the stator winding inductance, stator winding resistance and torque damping coefficient of the equivalent motor, can simulate the behavior of the entire wind turbine. The purpose of the whole process is to obtain a multivariate wind turbine equivalent simulation model by performing weighted equivalent processing on the doubly fed wind turbine and the direct-drive permanent magnet synchronous wind turbine. The multivariate wind turbine equivalent simulation model can comprehensively evaluate the overall operating efficiency and power generation capacity of the wind turbine based on the rated capacity, mechanical power, electromagnetic power and other performance parameters of each wind turbine, and provide accurate data support for the operation optimization and performance analysis of the wind farm. Through these weighted equivalent processing methods, efficient simulation and system-level optimization of wind turbines can be achieved.

[0091] Furthermore, the control parameters of the embedded constant admittance equivalent model are solved based on the control characteristics, including:

[0092] According to the steady-state characteristics of the ideal switch, the final value theorem is used to solve the steady-state characteristics parameters:

[0093] ;

[0094] in, is the voltage coefficient of the equivalent current source, is the current coefficient of the equivalent current source, the subscript on represents the on-coefficient, and off represents the off-coefficient.

[0095] As a preferred embodiment of the above, the steady-state characteristics of the ideal switch are used in combination with the final value theorem to analyze the behavior of the wind turbine under steady-state conditions. The ideal switch model assumes that the switch has ideal current and voltage characteristics in the on and off states, respectively, which can simplify the modeling process of the electrical system. Under steady-state conditions, the current and voltage of the system reach a certain stable state, so the final value theorem can be used to derive the steady-state parameters, and then obtain the embedded constant admittance equivalent model; in this process, it is first necessary to define the voltage coefficient and current coefficient of the equivalent current source. These two coefficients determine the voltage and current output characteristics of the equivalent current source and are key parameters for establishing a steady-state model. Through the final value theorem, the steady-state voltage and current of the system can be obtained through its initial conditions and the input characteristics of the system. Specifically, the final value theorem provides a mathematical method to solve the steady-state value of the system output by the limit value of the input signal. Therefore, in the steady-state analysis, by applying the final value theorem, the voltage and current values ​​of the system that tend to be stable after long-term operation can be effectively calculated; the voltage coefficient of the equivalent current source reflects the relationship between the current source and the voltage The current coefficient of the equivalent current source reflects the relationship between the current source and the current. Under the steady-state conditions of the wind turbine, the voltage and current output of the current source are affected by the on-state and off-state, respectively. Therefore, the on-coefficient and off-coefficient of the switch need to be considered. In the on-state, the voltage and current output of the current source are affected by the voltage coefficient and current coefficient; in the off-state, the on-coefficient and off-coefficient of the switch will change the voltage and current output characteristics of the equivalent current source; the on-coefficient and off-coefficient can be obtained by analyzing the electrical behavior of the ideal switch in the on and off states. When on, the current and voltage of the wind turbine show a stable relationship. The voltage coefficient and current coefficient in the on-state are calculated based on the steady-state characteristics; in the off-state, the current and voltage of the wind turbine are affected by the switch off and need to be adjusted according to the off-coefficient; based on these steady-state parameters, the steady-state electrical characteristics of the embedded constant admittance equivalent model can be obtained. The steady-state electrical characteristics can be used to describe the electrical performance of the wind turbine in the steady state and further applied to the dynamic regulation and performance optimization of the wind turbine.

[0096] Furthermore, the control parameters of the embedded constant admittance equivalent model are solved based on the control characteristics, including:

[0097] The discrete-time system is modeled based on the transient characteristics and organized into a matrix form to solve the parameters corresponding to the transient characteristics and the matrix equation that characterizes the relationship between the voltage and current of the switching device:

[0098] ;

[0099] Wherein, subscripts 1 and 2 represent the upper and lower corresponding switches of the single bridge arm of the switching device in the embedded constant admittance equivalent model, subscripts n and n-1 represent the nth and n-1th moments, G is the equivalent constant admittance parameter, 、 is the instantaneous DC and AC voltage at both ends of a single bridge arm, ;

[0100] The zero point of the spectrum radius is calculated mathematically based on the matrix equation to obtain the voltage coefficient and current coefficient:

[0101]

[0102] Among them, subscript 1 and subscript 2 represent the upper and lower corresponding switches of the single bridge arm of the switching device in the embedded constant admittance equivalent model. Indicates voltage coefficient represents the current coefficient, and G is the equivalent constant admittance parameter.

[0103] As a preferred embodiment of the above, according to the transient characteristics of the wind turbine, the dynamic behavior can be modeled by a discrete time system. The discrete time modeling usually includes converting the differential equation of the continuous time system into a matrix form. This conversion can discretize the state through the time step, so that the input and output in each time step can be clearly represented. On this basis, the relationship between the voltage and current of the wind turbine switch device can be derived according to the transient characteristics, and then a matrix equation can be constructed. It is assumed that the system contains multiple switching devices, and each switching device has a corresponding voltage and current. Usually, the switching devices are connected through a single bridge arm structure. In the embedded constant admittance equivalent model, the upper and lower switches of the single bridge arm represent different voltage and current states respectively. It is assumed that the voltage and current of the switching device at the first and second moments are DC voltage and AC voltage respectively. By arranging the transient characteristics into a matrix form, a matrix equation of the relationship between the voltage and current of the switching device can be obtained. This matrix equation describes the dynamic changes between the voltage and current of the switching device in the system. On this basis, the transient characteristics can be further analyzed by calculating the zero point of the spectral radius. The spectral radius is the maximum modulus of the eigenvalue of the matrix. The zero point of the spectral radius represents the system. The stability point of the system in the transient process can be obtained by calculating the zero point of the spectrum radius. First, the eigenvalues ​​of the matrix need to be calculated, and then the transient response characteristics are determined according to the maximum modulus of these eigenvalues, and then the voltage coefficient and current coefficient are extracted. The voltage coefficient and current coefficient respectively reflect the voltage and current response characteristics of the wind turbine in the transient process, which help to characterize the dynamic behavior of the system. In addition, the parameters in the matrix equation, especially the equivalent constant admittance parameter, also play a key role in the transient characteristics. The equivalent constant admittance parameter determines the relationship between the voltage and current of the switching device. Therefore, in the calculation of the voltage coefficient and When calculating the current coefficient, it is necessary to combine it with the equivalent constant admittance parameter for weighted processing. In this way, the electrical behavior of the wind turbine under transient conditions can be accurately characterized, and effective parameter support can be provided for subsequent simulation and control optimization. Finally, through the above mathematical methods and the solution of the matrix equation, transient parameters can be obtained, including the voltage coefficient and the current coefficient. These parameters are crucial for the transient analysis of wind turbines, and help to evaluate the response of the system under dynamic changes, ensuring that the wind turbine can respond quickly and stably to emergencies such as load changes and fault recovery during actual operation, thereby improving its stability and reliability.

[0104] Furthermore, parameter optimization is performed based on the steady-state error, including:

[0105] Construct a steady-state error analysis model:

[0106] ;

[0107] Wherein, subscripts 1 and 2 represent the upper and lower corresponding switches of a single bridge arm of the switching device in the embedded constant admittance equivalent model, G is the equivalent constant admittance parameter, 、 is the instantaneous DC and AC voltage at both ends of a single bridge arm, ;

[0108] Will and Substitute into the steady-state error analysis model respectively:

[0109] ;

[0110] when , then select As the conduction steady-state parameter;

[0111] For shutdown conditions, and Substitute them into the steady-state error analysis model respectively, calculate the steady-state errors respectively, and compare them. The one with the smallest steady-state error is selected as the shutdown steady-state parameter.

[0112] As a preferred embodiment of the above embodiment, it is necessary to first construct a steady-state error analysis model to accurately evaluate the steady-state parameters of the embedded constant admittance equivalent model under different operating conditions. The core goal of the steady-state error analysis model is to determine the accuracy of the current steady-state parameters by calculating the error between the actual operating voltage and the model-predicted voltage. Specifically, a single-bridge arm structure of a switching device is used in the model, and it is modeled by equivalent constant admittance parameters. The instantaneous voltages at both ends of the single bridge arm are DC voltage and AC voltage, respectively. By substituting these voltage values, the steady-state error of the model can be obtained. The steady-state error reflects the deviation between the actual wind turbine and the model, and is therefore a key indicator for determining the effectiveness of the steady-state parameters. Next, after the steady-state error analysis model is constructed, the DC voltage and AC voltage measured at each moment need to be substituted into the model to calculate the errors in the on and off states, respectively. In the on state, the actually measured DC voltage and AC voltage are first substituted into the error model to calculate the error between the model and the actual voltage. This error value is used to determine whether the currently selected steady-state parameters are appropriate. If the error is less than a preset threshold, the current parameters are considered to be valid steady-state conduction parameters. If the error is too large, the model automatically adjusts its parameters and recalculates the error until it finds the appropriate steady-state parameters that minimize the error. Similarly, in the off-state, the error analysis method is similar to that in the on-state. However, due to the stronger nonlinear characteristics of the system under the off-state condition, more candidate parameters need to be considered. In this case, the DC voltage and AC voltage under the off-state condition are substituted into the steady-state error analysis model to obtain the error values ​​of multiple candidate parameters. By comparing these errors, the parameters with the smallest error are selected as the steady-state parameters used by the embedded constant admittance equivalent model in the off-state. This process ensures that the model can find the most suitable steady-state parameters under various operating conditions, thereby improving the response accuracy of the system. Finally, by implementing this adaptive adjustment method, the embedded constant admittance equivalent model can automatically optimize its steady-state parameters according to the actual operating conditions, ensuring the voltage and current response accuracy of the system in both the on-state and the off-state. This adjustment mechanism not only enhances the adaptability of the model but also improves the model's fit to the wind turbine under various operating conditions, ensuring better robustness and stability during operation, thereby supporting more efficient wind turbine control and optimization.

[0113] Specifically, the wind turbine dynamics model includes:

[0114] The input power of the fan blade is:

[0115] ;

[0116] in, is the mass density of air, R is the radius of the wind wheel, is the real-time wind speed, is the wind energy utilization coefficient, is the pitch angle.

[0117] As a preferred embodiment of the above, the wind turbine dynamics model can dynamically simulate and analyze the wind input power, accurately reflecting the energy conversion process of the wind turbine under different wind speeds and control strategies. The core content includes the modeling of the wind energy capture process, the expression of wind energy utilization efficiency, and the relationship with wind speed, wind rotor structure and control variables. The basis of the wind turbine dynamics model is the expression modeling of the input power of the wind turbine blades. Wind energy is formed by the kinetic energy carried by the air flow. Therefore, the power that the wind turbine can obtain is jointly affected by factors such as air density, wind speed and wind rotor area. The input power can be expressed by a physical formula as the air density multiplied by the volume of airflow passing through the wind rotor per unit time, and then multiplied by the kinetic energy of the airflow. In this model, the mass density of air is a constant, which usually depends on environmental conditions such as altitude and temperature. The wind rotor radius is used to calculate the swept area of ​​the wind rotor and is a key physical quantity that determines the range of wind energy capture. The real-time wind speed is the most important dynamic variable, which directly affects the size of the wind energy. In order to reflect the wind energy conversion efficiency, the wind energy utilization is introduced into the wind turbine dynamics model. The wind energy utilization coefficient (WEC) is a key parameter. It is an indicator of the efficiency of a wind turbine in converting kinetic energy into mechanical energy. It is usually expressed as a function and depends on two important control parameters: the pitch angle and the tip speed ratio. The pitch angle is the installation angle of the wind turbine blades relative to the airflow direction. Its size has a direct impact on the airflow incidence angle and aerodynamic force. The tip speed ratio is the ratio of the linear velocity at the rotor blade tip to the wind speed and is a key dimension determining the aerodynamic efficiency of the wind turbine. The WEC is a nonlinear function of the pitch angle and the tip speed ratio, and its value is usually obtained through experimental measurement or simulation optimization. The air density, rotor radius, real-time wind speed, and WEC are combined to form the input power expression in the wind turbine dynamic model. This expression fully reflects the conversion path of wind energy from nature into the wind turbine, providing a fundamental driving force for subsequent mechanical power modeling, electrical modeling, and control strategies. By monitoring wind speed in real time and dynamically adjusting the pitch angle in conjunction with the control strategy, the WEC can be effectively improved, thereby increasing the wind turbine input power and maximizing wind energy capture efficiency.

[0118] Specifically, the converter control model includes:

[0119] ;

[0120] Among them, the subscripts d and q are the d-axis and q-axis components respectively. is the DC bus capacitance, 、 、 、 are the grid side voltage, current, equivalent inductance and resistance respectively, is the AC side output voltage, is the AC side output voltage, is the DC bus voltage, represents the synchronous angular velocity, S is the output power, is the grid-side output current.

[0121] As a preferred embodiment of the above, the converter control model is based on the electrical characteristics of the DC side and the AC side. Through the d-axis and q-axis coordinate transformation, the three-phase electrical quantity is converted into two orthogonal components in the static reference system, and a complete dynamic control structure is constructed in combination with the bus voltage, current, grid parameters, etc., the purpose of which is to ensure that the converter can meet the control requirements of the wind turbine output power and can be stably connected to the grid, thereby ensuring the power quality and system safety. The converter control model converts the voltage and current in the three-phase AC system into a synchronous rotating coordinate system to obtain two components, the d-axis and the q-axis. This transformation method simplifies the expression of the AC variable in the control process, making it easier to perform decoupling control. In this coordinate system, the d-axis is usually used to control the active component, and the q-axis is used to control the reactive component or voltage regulation. In terms of DC side modeling, the converter control model introduces a DC bus capacitor, which plays the role of energy storage and bus voltage stabilization. In the wind energy conversion process, the DC voltage output by the wind turbine through the rectifier is collected by the DC bus and converted into a grid-connected AC voltage through the inverter. Therefore, the dynamic change of the bus voltage The variability reflects the system's energy transmission balance. If the wind turbine output power increases but the bus load fails to respond in a timely manner, the voltage will increase; conversely, it will decrease. The converter control model adjusts the system output through the controller to maintain the bus voltage stable near the target value. The AC side modeling includes the grid-side voltage, current, equivalent inductance, and equivalent resistance. These parameters constitute the interface between the inverter and the grid. The AC output voltage is the controlled variable obtained through inverter regulation. Its amplitude and phase determine the direction and magnitude of power exchange during grid connection. By introducing synchronous angular velocity (i.e., the angular velocity of rotation corresponding to the system frequency), the model can synchronously track the grid phase, achieving non-disruptive grid connection control. The output power is determined by the d-axis and q-axis current components and the output voltage. The output current is dynamically affected by load demand. Therefore, the converter control model dynamically links the output power and load current. Through closed-loop control, the q-axis component can be adjusted under different operating conditions to control reactive power and achieve voltage regulation. Simultaneously, the d-axis component is used to control active power, thereby effectively regulating the total output power S.

[0122] Embodiment 2;

[0123] Based on the same inventive concept as the method for constructing a multi-type wind farm equivalent simulation model in the aforementioned embodiment, the present invention further provides a multi-type wind farm equivalent simulation model construction system, the system comprising:

[0124] The model building module collects wind turbine information and builds a multi-scale wind farm model, which includes a wind turbine dynamics model and a converter control model.

[0125] The parameter solution module builds an embedded constant admittance equivalent model and collects the control characteristics of the ideal switch. Based on the control characteristics, the control parameters of the embedded constant admittance equivalent model are solved. The parameters are optimized based on the steady-state error and the converter control model is replaced. The control characteristics include steady-state characteristics and transient characteristics.

[0126] The equivalent processing module performs model equivalent processing on several wind turbine types according to the multivariate large-scale wind farm model to obtain a multivariate wind turbine equivalent simulation model.

[0127] The adjustment system in the present invention can effectively implement a method for constructing an equivalent simulation model of a multi-machine wind farm, and the technical effects that can be achieved are as described in the above embodiments, which will not be repeated here.

[0128] Although the present application has been described with reference to specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and drawings are merely illustrative of the present application as defined herein and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, the present application is intended to include such modifications and variations as fall within the scope of the present application and its equivalents.

Claims

1. A method for constructing a multi-type wind farm equivalent simulation model, characterized in that: The method comprises: Collecting wind turbine information and building a multi-scale wind farm model, wherein the multi-scale wind farm model includes a wind turbine dynamics model and a converter control model; Constructing an embedded constant admittance equivalent model and collecting control characteristics of an ideal switch, solving control parameters of the embedded constant admittance equivalent model based on the control characteristics, optimizing the parameters based on steady-state errors, and replacing the converter control model, wherein the control characteristics include steady-state characteristics and transient characteristics; Performing model equivalence processing on several types of wind turbines according to the multivariate large-scale wind farm model to obtain a multivariate wind turbine equivalent simulation model; According to the multivariate scale wind farm model, model equivalence processing is performed on several wind turbine types, including: Collect the real-time wind speed, perform wind speed equalization processing on the real-time wind speed based on the wind turbine information, and obtain the effective wind speed: ； in, K is the wake attenuation coefficient, is the thrust coefficient, is the wake diffusion coefficient, is the effective wind speed, is the real-time wind speed, The distance from the wind turbine to the wind direction x The wake radius, R is the fan blade radius; Obtaining the wind turbine performance parameters of each wind turbine according to the wind turbine group information, performing equivalent processing on a number of the wind turbine performance parameters, and obtaining a multivariate wind turbine equivalent simulation model; Obtaining the wind turbine performance parameters of each wind turbine according to the wind turbine group information, performing equivalent processing on a plurality of the wind turbine performance parameters, and obtaining a multivariate wind turbine equivalent simulation model, including: The wind turbine generator set includes a doubly-fed wind turbine generator and a direct-drive permanent-magnet synchronous wind turbine generator. The doubly-fed wind turbine generator and the direct-drive permanent-magnet synchronous wind turbine generator have the same capacity equivalence: Calculating the proportion of the capacity of each wind turbine in the total capacity of the same type based on the plurality of wind turbine performance parameters, and performing weighted integration based on the respective capacity proportions of the wind turbine performance parameters to obtain a parameter equivalence factor; ； S Indicates the rated capacity and total of all fans, represents the electromagnetic power of the fan, Represents the mechanical power input by the fan, e represents a wind turbine of equivalent value, Representative i The electromagnetic power of the typhoon, Indicates the i Rated capacity of the typhoon, Indicates the i The capacity of the typhoon machine accounts for the proportion of the equivalent value machine, Indicates the rated capacity of the first fan; The doubly-fed wind turbine generator is weighted and equivalently evaluated according to the parameter equivalent factor: ； is the excitation reactance of the equivalent wind turbine, is the stator reactance of the equivalent wind turbine, Indicates the The stator reactance of the typhoon turbine, Indicates the Rated capacity of the typhoon, Indicates the rated capacity of the equivalent fan; For the direct-drive permanent magnet synchronous wind turbine generator, weighted equivalence is performed on the direct-drive permanent magnet synchronous wind turbine generator according to the parameter equivalent factor: ； Indicates the stator winding inductance of the equivalent motor, represents the stator winding resistance of the equivalent motor, is the equivalent motor torque damping coefficient, Indicates the The inductance of the stator winding of a direct-drive permanent magnet wind turbine generator, Indicates the stator winding resistance of a direct-drive permanent magnet wind turbine generator, Indicates the The torque damping coefficient of a direct-drive permanent magnet wind turbine generator is n, and n represents the number of the direct-drive permanent magnet wind turbine generators.

2. The method for constructing a multi-type wind farm equivalent simulation model according to claim 1, characterized in that: Solving the control parameters of the embedded constant admittance equivalent model according to the control characteristics includes: According to the steady-state characteristics of the ideal switch, the final value theorem is used to solve the steady-state characteristics parameters: ； in, is the voltage coefficient of the equivalent current source, is the current coefficient of the equivalent current source, the subscript on represents the on-coefficient, and off represents the off-coefficient.

3. The method for constructing a multi-type wind farm equivalent simulation model according to claim 1, characterized in that: Solving the control parameters of the embedded constant admittance equivalent model according to the control characteristics includes: The discrete-time system is modeled according to the transient characteristics and organized into a matrix form to solve the parameters corresponding to the transient characteristics and the matrix equation that characterizes the relationship between the voltage and current of the switching device: ； Wherein, subscript 1 and subscript 2 represent the upper and lower corresponding switches of the single bridge arm of the switching device in the embedded constant admittance equivalent model, and subscript n 、 n-1 Indicates the n , No. n-1 At this moment, G is the equivalent constant admittance parameter, 、 is the instantaneous DC and AC voltage at both ends of the single bridge arm, ; According to the matrix equation, a mathematical method is used to calculate the zero point of the spectrum radius and obtain the voltage coefficient and current coefficient: Wherein, subscripts 1 and 2 represent the upper and lower corresponding switches of a single bridge arm of the switching device in the embedded constant admittance equivalent model. Indicates voltage coefficient represents the current coefficient, and G is the equivalent constant admittance parameter.

4. The method for constructing a multi-type wind farm equivalent simulation model according to claim 3, characterized in that: Parameter optimization is performed based on steady-state error, including: Construct a steady-state error analysis model: ； Wherein, subscripts 1 and 2 represent the upper and lower corresponding switches of a single bridge arm of the switch device in the embedded constant admittance equivalent model, G is the equivalent constant admittance parameter, 、 is the instantaneous DC and AC voltage at both ends of the single bridge arm, ; Will and Substitute into the steady-state error analysis model respectively: ； when , then select As the conduction steady-state parameter; For shutdown conditions, and Substitute them into the steady-state error analysis model respectively, calculate the steady-state errors respectively, compare them, and select the one with the smallest steady-state error as the shutdown steady-state parameter.

5. The method for constructing a multi-type wind farm equivalent simulation model according to claim 1, characterized in that: The wind turbine dynamics model includes: The input power of the fan blade is: ； in, is the mass density of air, R is the rotor radius, is the real-time wind speed, is the wind energy utilization coefficient, is the pitch angle.

6. The method for constructing a multi-type wind farm equivalent simulation model according to claim 1, characterized in that: The converter control model includes: ； Among them, the subscript d 、 q They are d axis, q Axis component, is the DC bus capacitance, 、 、 、 are the grid side voltage, current, equivalent inductance and resistance respectively, is the AC side output voltage, is the AC side output voltage, is the DC bus voltage, represents the synchronous angular velocity, S is the output power, is the grid-side output current.

7. A system for constructing equivalent simulation models of multiple wind farm types, characterized in that: The method for constructing a multi-machine wind farm equivalent simulation model according to claim 1 is adopted, wherein the system comprises: The model building module collects wind turbine information and builds a multi-scale wind farm model, which includes a wind turbine dynamics model and a converter control model. A parameter solving module, which constructs an embedded constant admittance equivalent model and collects control characteristics of an ideal switch, solves control parameters of the embedded constant admittance equivalent model based on the control characteristics, and replaces the converter control model. The control characteristics include steady-state characteristics and transient characteristics. The equivalent processing module performs model equivalent processing on several wind turbine types according to the multivariate large-scale wind farm model to obtain a multivariate wind turbine equivalent simulation model.

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