A method and apparatus for constructing an electromagnetic equivalent model of a wind farm

Abstract

This invention pertains to the field of power and discloses a method and apparatus for constructing an electromagnetic equivalent model of a wind farm. The method includes: selecting a target electromagnetic transient model for each segment of the collector line from a π-type lumped parameter line model and a distributed parameter line model, based on a preset electromagnetic transient simulation step size and the actual length of the collector lines between wind turbines within the wind farm; selecting a clustering strategy for the wind turbines according to the target frequency band of interest in the electromagnetic transient analysis; for scenarios requiring dynamic clustering, using a K-means clustering algorithm based on an improved bird flocking algorithm to cluster the wind turbines and determine the optimal cluster centers; and using a capacity-weighted algorithm to perform parameter equivalence calculations on the turbines within the same cluster based on the target electromagnetic transient model and the clustering results of the wind turbines, thereby completing the construction of the electromagnetic equivalent model of the wind farm.

Description

Technical Field

[0001] This invention belongs to the field of electric power, and in particular relates to a method and apparatus for constructing an electromagnetic equivalent model of a wind farm. Background Technology

[0002] As the construction of new power systems continues to deepen, the penetration rate of large-scale wind power systems in the power grid will increase, and their impact on the dynamic performance of the power system under small disturbances will become increasingly significant. However, the massive number of power electronic devices in large-scale wind power systems complicates their dynamic interaction with the system, making it multi-layered, multi-timescale, and significantly increasing the difficulty of small disturbance stability analysis and the risk of accidents. Against this backdrop, establishing an electromagnetic transient model that accurately reflects the grid-connected operation characteristics of large-scale wind farms is fundamental to the accurate analysis of power system characteristics.

[0003] In related technologies, renewable energy power plants are typically highly simplified based on similarity assumptions, ignoring to varying degrees the differences between units within the plant and the complex topology of the power collection system. This results in the renewable energy power plant being aggregated into one or more renewable energy units, and the electromagnetic model of these aggregated units is used to replace the electromagnetic model of the power plant itself. However, because the topological characteristics of the power collection system and the characteristics of the line model are not considered during the equivalence process, the broadband small-disturbance characteristics of existing large-scale wind farm electromagnetic equivalent models are usually altered. This leads to a decrease in the accuracy of the aggregated equivalent model at the broadband level, affecting the analysis results of the system's small-disturbance dynamic characteristics. Summary of the Invention

[0004] In view of this, the present invention discloses a method and apparatus for constructing an electromagnetic equivalent model of a wind farm, which can solve the shortcomings of related technologies.

[0005] To achieve the above objectives, the present invention discloses the following technical solution:

[0006] According to a first aspect of the present invention, a method for constructing an electromagnetic equivalent model of a wind farm is proposed, comprising:

[0007] Based on the preset electromagnetic transient simulation step size and the actual length of the collector lines between each wind turbine in the wind farm, the target electromagnetic transient model is selected from the π-type lumped parameter line model and the distributed parameter line model for each section of the collector line.

[0008] Based on the target frequency band of interest in electromagnetic transient analysis, select the clustering strategy for wind turbine units;

[0009] For scenarios requiring dynamic clustering, a K-means clustering algorithm based on an improved bird flocking algorithm is used to cluster wind turbines to determine the optimal cluster centers.

[0010] Based on the target electromagnetic transient model and the clustering results of the wind turbine units, a capacity-weighted algorithm is used to perform parameter equivalence calculations on the units within the same cluster, so as to complete the construction of the electromagnetic equivalence model of the wind farm.

[0011] According to a second aspect of the present invention, a device for constructing an electromagnetic equivalent model of a wind farm is provided, the device comprising:

[0012] Unit selection: Based on the preset electromagnetic transient simulation step size and the actual length of the collector lines between each wind turbine in the wind farm, the target electromagnetic transient model is selected from the π-type lumped parameter line model and the distributed parameter line model for each collector line segment;

[0013] Selecting Units: Based on the target frequency band of interest in the electromagnetic transient analysis, select the clustering strategy for wind turbine units;

[0014] Clustering Unit: For scenarios requiring dynamic clustering, a K-means clustering algorithm based on an improved bird flocking algorithm is used to cluster the wind turbines to determine the optimal cluster centers;

[0015] Calculation unit: Based on the target electromagnetic transient model and the clustering results of the wind turbine units, the capacity weighted algorithm is used to perform parameter equivalent calculations on the units within the same cluster to complete the construction of the electromagnetic equivalent model of the wind farm.

[0016] According to a third aspect of the present invention, an electronic device is provided, comprising:

[0017] processor;

[0018] Memory used to store processor-executable instructions;

[0019] The processor implements the steps of the method as described in the first aspect by running the executable instructions.

[0020] According to a fourth aspect of the invention, a computer-readable storage medium is provided having computer instructions stored thereon that, when executed by a processor, implement the steps of the method as described in the first aspect.

[0021] As can be seen from the above technical solutions, the wind farm electromagnetic equivalent model construction method disclosed in this invention is as follows:

[0022] On the one hand, in constructing the electromagnetic equivalent model of the wind farm, the lumped parameter or distributed parameter model is adaptively selected based on the simulation step size and line length. This ensures the accuracy of high-frequency transient simulation while avoiding unnecessary computational complexity, thus improving model construction efficiency. On the other hand, different clustering methods are selected according to the target frequency band. Dynamic clustering is used to preserve unit differences in the low-frequency band, while single-unit equivalent modeling is used in the high-frequency band to improve computational efficiency, thereby balancing model accuracy and model construction efficiency. In addition, an improved bird flocking algorithm is used to optimize the initial K-means cluster centers, avoiding the problem of traditional K-means easily getting trapped in local optima, improving the accuracy and stability of the clustering results, and thus increasing model accuracy. Attached Figure Description

[0023] Figure 1 This is an exemplary embodiment of an architecture diagram of a large-scale wind farm aggregation system using a trunk-line layout;

[0024] Figure 2 This is a schematic diagram of an exemplary embodiment of a multi-machine equivalent method for large-scale wind farms based on collector wire aggregation;

[0025] Figure 3 This is a flowchart of a method for constructing an electromagnetic equivalent model of a wind farm, provided in an exemplary embodiment;

[0026] Figure 4 This is a flowchart of an exemplary embodiment of a method for electromagnetic equivalence of a large wind farm that takes into account the characteristics of the power collection system;

[0027] Figure 5 This is a schematic structural diagram of a device provided in an exemplary embodiment;

[0028] Figure 6 This is a block diagram of an electromagnetic equivalent model construction device for a wind farm, provided in an exemplary embodiment. Detailed Implementation

[0029] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of one or more embodiments of the present invention as detailed in the appended claims.

[0030] It should be noted that in other embodiments, the corresponding methods are not necessarily performed in the order shown and described in this invention.

[0031] The method comprises steps. In some other embodiments, the method may include more or fewer steps than those described in this invention. Furthermore, a single step described in this invention may be broken down into multiple steps in other embodiments; and multiple steps described in this invention may be combined into a single step in other embodiments.

[0032] Figure 1 This is an exemplary embodiment of an architecture diagram of a large-scale wind farm aggregation system using a trunk-line layout, such as... Figure 1 As shown, large-scale wind farms typically employ a trunk-line power collection system. Therefore, detailed modeling of a large-scale wind farm power collection system should include the wind turbine cluster, the power collection system, the step-up substation, and the plant's auxiliary power system. Because the wind turbine cluster contains a large number of power electronic switches, a detailed model of a large-scale wind farm requires sufficiently powerful computing capabilities to meet simulation requirements, resulting in low simulation efficiency and difficulty in meeting the needs of practical engineering applications. Therefore, large-scale wind farms typically adopt a trunk-line power collection system... Figure 2 The illustrated method for multi-turbine equivalent simulation of large-scale wind farms based on collector line aggregation equates the power of all turbines on each collector line in a large wind farm to simplify the number of equivalent turbine units in the system. However, while this collector line-based multi-turbine equivalent model can reflect the operational differences between different collector lines, its reliance on physical collector line connections as a clustering indicator is too direct and can easily lead to a trade-off between simulation resources and simulation accuracy. In other words, collector line-based multi-turbine equivalent simulation may result in lower simulation accuracy than single-turbine equivalent simulation. This not only results in more equivalent turbine units but also lower simulation accuracy, wasting computational resources and producing unsatisfactory simulation results. Especially for scenarios requiring consideration of small disturbance dynamics, the simulation accuracy of the above model differs even more significantly from the actual detailed model at high frequencies, making it difficult to meet application requirements.

[0033] To address the shortcomings in related technologies, this invention proposes a method for constructing an electromagnetic equivalent model of a wind farm.

[0034] Figure 3 This is a flowchart illustrating an exemplary embodiment of a method for constructing an electromagnetic equivalent model of a wind farm. For example... Figure 3 As shown, the method may include the following steps:

[0035] Step 301: Based on the preset electromagnetic transient simulation step size and the actual length of the collector lines between each wind turbine in the wind farm, select the target electromagnetic transient model for each collector line from the π-type lumped parameter line model and the distributed parameter line model.

[0036] Based on the relationship between the simulation step size and the length of the collector lines between each wind turbine, the electromagnetic transient model of each collector line segment is selected from two electromagnetic models: the π-type lumped parameter line model and the distributed parameter line model. Specifically, the relationship between the relevant simulation step size and the model selection of the collector line parameters is shown in Table 1.

[0037] Table 1

[0038]

[0039] Since the operating characteristics of collector lines (such as capacitance effects) have a significant impact on the dynamic stability of the electromagnetic transient level under small disturbances and on fault ride-through protection strategies, the influence of collector lines must be considered when modeling large wind farms. Conventional aggregated equivalent modeling focuses more on electromechanical transient characteristics, using the same model for all collector segments and neglecting the influence of simulation step size and collector length on the model's dynamic stability under small disturbances. This results in alterations to the dynamic characteristics of the electromagnetic equivalent model.

[0040] In one embodiment, the step of selecting a target electromagnetic transient model for each collector line segment from a π-type lumped parameter line model and a distributed parameter line model includes: when the length of the collector line is greater than or equal to the product of the speed of light and the simulation step size, a distributed parameter line model is selected as the target electromagnetic transient model; otherwise, a π-type lumped parameter line model is selected as the target electromagnetic transient model.

[0041] In electromagnetic transient simulation, two basic methods can be used to represent collector lines: the π-type lumped parameter representation and the distributed parameter representation. The distributed parameter line model is built based on the traveling wave principle, taking into account reflection and refraction of electrical quantities during transmission, i.e., transmission delay. Compared to the π-type model, it offers higher accuracy. However, its use requires consideration of the system simulation step size and line length; specifically, the distributed parameter collector line model can only be used when the line length exceeds the product of the speed of light and the system simulation step size. For example, for the most commonly used 50 microsecond / 20 microsecond electromagnetic transient simulation step size, the collector line length must be over 6 km to use the distributed parameter line model. However, in actual wind farms, the distance between wind turbines is typically in the range of 0.5–4 km, thus limiting the use of the distributed parameter line model. In contrast, the π-type lumped parameter line model accurately reflects the impedance of shorter collector lines, and therefore can be used for high-precision simulation of shorter collector lines. In summary, based on the first principles of electromagnetic modeling, to accurately reflect the broadband dynamic characteristics of large wind farms, the models of each collector line segment should be selected according to the relationship between the product of the line length, the speed of light, and the system simulation step size, so as to correctly reflect the broadband dynamics.

[0042] Step 302: Select a clustering strategy for wind turbines based on the target frequency band of interest in the electromagnetic transient analysis.

[0043] In one embodiment, the step of selecting a wind turbine clustering strategy based on the target frequency band of interest in the electromagnetic transient analysis includes: when the target frequency band is a low frequency band, adopting a dynamic clustering method based on the unit output power; when the target frequency band is a high frequency band, adopting a method of aggregating all units into a single equivalent unit.

[0044] Specifically, in lower frequency bands, due to the relatively large impact of power, it is necessary to group based on power; in higher frequency bands, due to the small impact of power, grouping is no longer necessary, and single-machine aggregation can be used.

[0045] For a direct-drive wind turbine, considering the average value converter model, current control, DC voltage control, and phase-locked loop, the frequency domain admittance matrix can be expressed as:

[0046] ;

[0047] The expression for the standardized matrix elements is as follows:

[0048] ;

[0049] in, The transfer function of the current control loop; This is the transfer function of the DC voltage control loop; Let be the transfer function of the phase-locked loop; This represents the steady-state amplitude of the grid-connected bus voltage. This represents the steady-state amplitude of the output current. This is the steady-state value of the DC voltage; It is a DC capacitor; For filtering inductors; It is the power frequency angular frequency.

[0050] It exhibits a linear relationship with the steady-state current amplitude, i.e., a linear relationship with the power level. Under typical parameters, the two summation terms in its numerator are:

[0051] ;

[0052] It is evident that at lower frequencies, the amplitude of the first term is relatively large, thus its impact on power is relatively significant; while at higher frequencies, the first term is relatively negligible, therefore its impact on power is minimal. Secondly, It is proportional to the steady-state current amplitude, and The amplitude-frequency response curve of the first element is significantly lower than that of the other two matrix elements, therefore The impact can be ignored. Finally, Similarly, it exhibits a linear relationship with the steady-state current amplitude, and above 100Hz, the second summation term in the numerator is:

[0053] ;

[0054] Its amplitude is significantly less than 1, therefore The impact of power on the frequency band is minimal. The above analysis shows that, for direct-drive wind farms, from the perspective of frequency domain impedance analysis, the following approximate equivalent relationship can be obtained:

[0055] ;

[0056] in, For wind speed, This represents steady-state power.

[0057] Therefore, for direct-drive wind turbines, the use of power equivalence and frequency domain admittance equivalence are consistent in the lower frequency band, so a power-based dynamic grouping method is required; while in the high frequency band, since power has little impact on dynamic characteristics, a single-unit equivalence method can be used.

[0058] Step 303: For scenarios requiring dynamic clustering, the K-means clustering algorithm based on the improved bird flocking algorithm is used to cluster the wind turbine units to determine the optimal cluster centers.

[0059] In one embodiment, the step of using a K-means clustering algorithm based on an improved bird flocking algorithm to cluster wind turbine units includes: initializing bird flocking algorithm parameters, including population size and search space dimension; setting the number of iterations, updating the position through the foraging, alert, and flight behaviors of the bird flock, and finding the optimal solution; using the optimal solution obtained by the bird flocking algorithm as the initial cluster center of the K-means clustering algorithm; executing K-means clustering until the cluster centers no longer change, and outputting the final clustering result.

[0060] Specifically, such as Figure 4 As shown, the steps of the K-means clustering algorithm based on the improved bird flocking algorithm are as follows:

[0061] Step 3031: Initialize each parameter, set the total population, and define the spatial dimension of the bird flock search as 2;

[0062] Step 3032: Set the maximum number of iterations for the outer loop;

[0063] Step 3033: Set variable i=1, enter the inner loop, and update the position of the flock of birds in the foraging behavior;

[0064] Step 3034: Let t = t + 1, and check if the current iteration count has reached the maximum. If it has, the birds will be divided into two groups: predators and producers, and the flock positions will be updated accordingly. Otherwise, continue iterating.

[0065] Step 3035: If the maximum number of iterations is reached or the bird's position is not updated in multiple iterations, stop the calculation and obtain the best reference value;

[0066] Step 3036: The optimal cluster centers obtained in step 5 are used as the initial cluster centers for K-means, and the data are assigned to K clusters;

[0067] Step 3037: Recalculate the cluster centers until they no longer change, end the iteration, and then output the clustering results.

[0068] By employing effective clustering indices and a K-means clustering algorithm based on an improved bird flocking algorithm, it is possible to effectively realize multi-machine dynamic equivalent electromagnetic transient modeling of large wind farms considering power state.

[0069] Step 304: Based on the target electromagnetic transient model and the clustering results of the wind turbine units, the capacity weighted algorithm is used to perform parameter equivalent calculations on the units within the same cluster to complete the construction of the electromagnetic equivalent model of the wind farm.

[0070] In one embodiment, the capacity weighting algorithm includes at least the aggregation of generator parameters, transformer parameters, shaft system parameters, collector line parameters, equivalent wind speed, and control parameters.

[0071] Assume there are n wind turbine units of identical model and parameters within the same cluster, divided into m categories. Each category of wind turbine units is equivalent to one unit. The parameter equivalence method used is as follows. The parameter aggregation of the generator units is as follows:

[0072] ;

[0073] in, For the equivalent number of units, This refers to the rated capacity of a single generator. For the magnetizing reactance, and These are the reactance and resistance of the stator, respectively. and These are rotor reactance and resistance, respectively, with subscripts. This represents the variable after it has been equivalent.

[0074] The transformer parameters are aggregated as follows:

[0075] ;

[0076] in, It is the rated capacity of the transformer. It is the impedance of the transformer.

[0077] The aggregated parameters of the shaft system are as follows:

[0078] ;

[0079] in, and , respectively, are the rotor inertia time constants of the asynchronous generator and the wind turbine. This is the shaft stiffness coefficient.

[0080] The cable parameters are aggregated as follows:

[0081] For trunk-type cabling, the equivalent cable impedance is:

[0082] ;

[0083] in, It is the first The active power output of the doubly fed wind turbine unit. For the first The impedance of the cable.

[0084] For a radial arrangement, the equivalent cable impedance is:

[0085] ;

[0086] In the equivalent wind speed section, the relationship curve between wind speed and power of the corresponding wind turbine model can be used to obtain the active power at the corresponding wind speed, take its average value, and finally obtain the equivalent wind speed by reversing the wind speed and power curve.

[0087] In the control parameter section, all control parameters remain unchanged except for the power control module. The equivalent base capacity of the active and reactive power control modules is:

[0088] ;

[0089] The parameter values ​​for reactive power control are:

[0090] ;

[0091] in, These are the parameter values ​​for reactive power control.

[0092] In this embodiment, on the one hand, during the construction of the electromagnetic equivalent model of the wind farm, the lumped parameter or distributed parameter model is adaptively selected based on the simulation step size and line length. This ensures the accuracy of high-frequency transient simulation while avoiding unnecessary computational complexity, thus improving model construction efficiency. On the other hand, different clustering methods are selected according to the target frequency band. Dynamic clustering is used to preserve unit differences in the low-frequency band, while single-unit equivalents are used in the high-frequency band to improve computational efficiency, thereby balancing model accuracy and model construction efficiency. Furthermore, an improved bird flocking algorithm is used to optimize the initial K-means cluster centers, avoiding the problem of traditional K-means easily getting trapped in local optima, improving the accuracy and stability of the clustering results, and thus increasing model accuracy.

[0093] In one embodiment, the method further includes: verifying the accuracy of the constructed equivalent model by comparing the simulation results of the equivalent model and the detailed wind farm model under multiple operating conditions.

[0094] After establishing the equivalent model, its accuracy needs to be assessed. This is quantified by the proportion of the error in the output curve values ​​between the equivalent model and the detailed model to the latter. Based on this, error indices for active and reactive power are defined. and ,like:

[0095] ;

[0096] ;

[0097] Where P and Q represent the active and reactive power of the detailed model, P eq and Q eq Let t1 and t2 represent the active and reactive power of the equivalent model, and t1 and t2 represent the start and end times of the error calculation, respectively.

[0098] Figure 5 This is a schematic structural diagram of a device provided in an exemplary embodiment. Please refer to... Figure 5 At the hardware level, the device includes a processor 502, an internal bus 504, a network interface 506, memory 508, and non-volatile memory 510, and may also include other hardware required for its functions. One or more embodiments of the present invention can be implemented in software, for example, the processor 502 reads the corresponding computer program from the non-volatile memory 510 into memory 508 and then runs it. Of course, in addition to software implementation, one or more embodiments of the present invention do not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0099] Please refer to Figure 6A device for constructing electromagnetic equivalent models of wind farms can be applied to, for example... Figure 6 The device shown, in order to implement the technical solution of the present invention, includes:

[0100] Unit 601 is selected to select a target electromagnetic transient model from the π-type lumped parameter line model and the distributed parameter line model for each section of the collection line based on the preset electromagnetic transient simulation step size and the actual length of the collection line between each wind turbine in the wind farm.

[0101] Selection unit 602 is used to select the grouping strategy of wind turbine units based on the target frequency band of interest in electromagnetic transient analysis.

[0102] Clustering unit 603 is used to cluster wind turbines using a K-means clustering algorithm based on an improved bird flocking algorithm to determine the optimal cluster center for scenarios that require dynamic clustering.

[0103] The calculation unit 604 is used to perform parameter equivalence calculations on the units within the same cluster based on the target electromagnetic transient model and the clustering results of the wind turbine units, using a capacity weighting algorithm, in order to complete the construction of the electromagnetic equivalence model of the wind farm.

[0104] Optionally, the selection unit 601 is specifically used for:

[0105] When the length of the collector line is greater than or equal to the product of the speed of light and the simulation step size, the distributed parameter line model is selected as the target electromagnetic transient model; otherwise, the π-type lumped parameter line model is selected as the target electromagnetic transient model.

[0106] Optionally, the selection unit 602 is specifically used for:

[0107] When the target frequency band is a low frequency band, a dynamic grouping method based on the unit output power is adopted;

[0108] When the target frequency band is a high-frequency band, the method of aggregating all units into a single equivalent unit is adopted.

[0109] Optionally, the clustering unit 603 is specifically used for:

[0110] Initialize the bird flocking algorithm parameters, including population size and search space dimension;

[0111] The number of iterations is set, and the position is updated based on the foraging, alertness, and flight behavior of the flock to find the optimal solution;

[0112] The optimal solution obtained by the bird flocking algorithm is used as the initial cluster center for the K-means clustering algorithm;

[0113] Perform K-means clustering until the cluster centers no longer change, and output the final clustering results.

[0114] Optionally, the capacity weighting algorithm includes at least the aggregation of generator parameters, transformer parameters, shaft system parameters, collector line parameters, equivalent wind speed, and control parameters; wherein, for a trunk-type collector system, the formula for calculating the equivalent cable impedance Zeq1 is:

[0115] ;

[0116] For a radially arranged current collector system, the formula for calculating the equivalent cable impedance Zeq1 is:

[0117] ;

[0118] Where n is the number of units in the cluster, P j Z represents the active power of the j-th generating unit. l Let be the impedance of the l-th cable.

[0119] Optionally, the device further includes:

[0120] The comparison unit 605 is used to verify the accuracy of the constructed equivalent model by comparing the simulation results of the equivalent model and the detailed wind farm model under multiple operating conditions.

[0121] Furthermore, the comparison unit 605 is specifically used for:

[0122] Using the active power error index E p and reactive power error index E q The formulas for quantifying model accuracy are as follows:

[0123] ;

[0124] ;

[0125] Where P and Q represent the active and reactive power of the detailed model, P eq and Q eq Let t1 and t2 represent the active and reactive power of the equivalent model, and t1 and t2 represent the start and end times of the error calculation, respectively.

[0126] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which can take the form of a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email sending and receiving device, game console, tablet computer, wearable device, or any combination of these devices.

[0127] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0128] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0129] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0130] For any computer-readable medium (or computer-readable storage medium) as described above or otherwise, computer instructions may be stored thereon, which, when executed by a processor, implement one or more of the above embodiments, thereby realizing the technical solution of the present invention.

[0131] The present invention also proposes a computer program that, when executed by a processor, implements one or more of the embodiments described above, thereby realizing the technical solution of the present invention. This computer program may be specifically recorded on the above-described or other computer-readable media, and the present invention does not impose any limitations on this.

[0132] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0133] The foregoing has described specific embodiments of the invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0134] The terminology used in one or more embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” used in one or more embodiments of the invention and in the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0135] It should be understood that although the terms first, second, third, etc., may be used to describe various information in one or more embodiments of the present invention, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of one or more embodiments of the present invention, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0136] The above description is merely a preferred embodiment of one or more embodiments of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of the present invention should be included within the protection scope of one or more embodiments of the present invention.

Claims

1. A method for constructing an electromagnetic equivalent model of a wind farm, characterized in that, include: Based on the preset electromagnetic transient simulation step size and the actual length of the collector lines between each wind turbine in the wind farm, a target electromagnetic transient model is selected for each collector line segment from the π-type lumped parameter line model and the distributed parameter line model. This includes: when the length of the collector line is greater than or equal to the product of the speed of light and the simulation step size, the distributed parameter line model is selected as the target electromagnetic transient model; otherwise, the π-type lumped parameter line model is selected as the target electromagnetic transient model. When the target frequency band of interest in the electromagnetic transient analysis is the low frequency band, a dynamic grouping method based on the unit output power is adopted as the grouping strategy for wind turbine units; when the target frequency band is the high frequency band, a method of aggregating all units into a single equivalent unit is adopted as the grouping strategy for wind turbine units. For scenarios requiring dynamic clustering, a K-means clustering algorithm based on an improved bird flocking algorithm is used to cluster wind turbines to determine the optimal cluster centers. Based on the target electromagnetic transient model and the clustering results of the wind turbine units, a capacity-weighted algorithm is used to perform parameter equivalence calculations on the units within the same cluster, so as to complete the construction of the electromagnetic equivalence model of the wind farm.

2. The method according to claim 1, characterized in that, The method of using a K-means clustering algorithm based on an improved bird flocking algorithm to cluster wind turbine units includes: Initialize the bird flocking algorithm parameters, including population size and search space dimension; The number of iterations is set, and the position is updated based on the foraging, alertness, and flight behavior of the flock to find the optimal solution; The optimal solution obtained by the bird flocking algorithm is used as the initial cluster center for the K-means clustering algorithm; Perform K-means clustering until the cluster centers no longer change, and output the final clustering results.

3. The method according to claim 1, characterized in that, The capacity-weighted algorithm includes at least the aggregation of generator parameters, transformer parameters, shaft system parameters, collector line parameters, equivalent wind speed, and control parameters; wherein, for a trunk-type collector system, the formula for calculating the equivalent cable impedance Zeq1 is: ； For a radially arranged current collector system, the equivalent cable impedance Z eq1 The calculation formula is: ； Where n is the number of units in the cluster, P j Z represents the active power of the j-th generating unit. l Let be the impedance of the l-th cable.

4. The method according to claim 1, characterized in that, The method further includes: The accuracy of the constructed equivalent model is verified by comparing the simulation results of the equivalent model and the detailed wind farm model under multiple operating conditions.

5. The method according to claim 4, characterized in that, The accuracy of the constructed equivalent model is verified by comparing the simulation results of the equivalent model and the detailed wind farm model under multiple operating conditions, including: Using the active power error index E p and reactive power error index E q The formulas for quantifying model accuracy are as follows: ； ； Where P and Q represent the active and reactive power of the detailed model, P eq and Q eq Let t1 and t2 represent the active and reactive power of the equivalent model, and t1 and t2 represent the start and end times of the error calculation, respectively.

6. A device for constructing an electromagnetic equivalent model of a wind farm, characterized in that, The device includes: Selection Unit: Based on the preset electromagnetic transient simulation step size and the actual length of the collector lines between each wind turbine in the wind farm, a target electromagnetic transient model is selected for each collector line segment from the π-type lumped parameter line model and the distributed parameter line model. This includes: when the length of the collector line is greater than or equal to the product of the speed of light and the simulation step size, the distributed parameter line model is selected as the target electromagnetic transient model; otherwise, the π-type lumped parameter line model is selected as the target electromagnetic transient model. Selecting a unit: When the target frequency band of interest in the electromagnetic transient analysis is the low frequency band, a dynamic grouping method based on the unit output power is adopted as the grouping strategy for wind turbine units; when the target frequency band is the high frequency band, a method of aggregating all units into a single equivalent unit is adopted as the grouping strategy for wind turbine units. Clustering Unit: For scenarios requiring dynamic clustering, a K-means clustering algorithm based on an improved bird flocking algorithm is used to cluster the wind turbines to determine the optimal cluster centers; Calculation unit: Based on the target electromagnetic transient model and the clustering results of the wind turbine units, the capacity weighted algorithm is used to perform parameter equivalent calculations on the units within the same cluster to complete the construction of the electromagnetic equivalent model of the wind farm.

7. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor implements the steps of the method as described in any one of claims 1-5 by running the executable instructions.

8. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, this instruction implements the steps of the method as described in any one of claims 1-5.

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