A method and system for stable control of high-voltage frequency converters under power grid fluctuations

Abstract

This invention discloses a method and system for stable control of a high-voltage frequency converter under power grid fluctuations, relating to the field of frequency converter technology. The method includes: collecting data from the target frequency converter, performing fluctuation characteristic analysis to obtain fluctuation characteristic data; performing control bias analysis to obtain multiple control biases; constructing a suppression control function to obtain a suppression reactor parameter library; constructing a cost reduction control function to obtain a cost reduction reactor parameter library; determining whether the suppression reactor parameter library and the cost reduction reactor parameter library have an intersection; if so, sorting the reactor parameters within the intersection and selecting the optimal reactor parameters; if not, selecting the optimal reactor parameters based on optimization record data and configuring the reactors accordingly. This solves the technical problem in the prior art where different connected reactor parameters lead to insufficient stability of the high-voltage frequency converter under power grid fluctuations, achieving the technical effect of improving the stability of the high-voltage frequency converter under power grid fluctuations.

Description

Technical Field

[0001] This invention relates to the field of frequency converter technology, and specifically to a method and system for stable control of a high-voltage frequency converter under power grid fluctuations. Background Technology

[0002] With the development of modern industry, power grid systems face increasingly complex operating environments, and grid fluctuations have become one of the key factors affecting the stable operation of high-voltage frequency converters (VFDs). As an important piece of equipment in the power system, the stability of high-voltage VFDs directly affects the safe and efficient operation of the power grid. Therefore, how to effectively control the stable operation of high-voltage VFDs under grid fluctuations has become a crucial problem that urgently needs to be solved in the field of power technology. Existing technologies connect reactors to the VFDs to improve the power factor of the power grid and reduce harmonic interference, thereby ensuring the safe and stable operation of the high-voltage VFDs. However, the effectiveness of reactors with different parameters in suppressing the impact of grid fluctuations on the VFDs varies depending on the actual situation, leading to technical problems such as insufficient stability of high-voltage VFDs under grid fluctuations. Summary of the Invention

[0003] This application provides a method and system for the stable control of a high-voltage frequency converter under power grid fluctuations, which solves the technical problem in the prior art that the high-voltage frequency converter is not stable enough under power grid fluctuations due to different parameters of the connected reactor.

[0004] In view of the above problems, this application provides a method and system for stable control of a high-voltage frequency converter under power grid fluctuations.

[0005] A first aspect of this application provides a method for stabilizing a high-voltage frequency converter under power grid fluctuations, the method comprising:

[0006] Collect grid operation monitoring data of the target frequency converter connected to the grid, perform fluctuation characteristic analysis, and obtain fluctuation characteristic data. The target frequency converter is a high-voltage frequency converter.

[0007] Based on the amplitude and frequency of multiple fluctuation categories within the fluctuation characteristic data, a control bias analysis is performed to obtain multiple control biases.

[0008] Based on multiple control bias and fluctuation characteristic data, a suppression control function is constructed to optimize the reactor parameters of the reactor connected to the high-voltage frequency converter. The reactor parameters are optimized to obtain a suppression reactor parameter library.

[0009] Based on multiple control bias and fluctuation characteristic data, a cost-reduction control function is constructed to optimize the reactor parameters of the high-voltage frequency converter connected reactor. The reactor parameters are optimized to obtain a cost-reduction reactor parameter library.

[0010] Determine whether there is an intersection between the suppression reactor parameter library and the cost reduction reactor parameter library. If so, sort the multiple intersection reactor parameters within the intersection, analyze and select the optimal reactor parameters. If not, analyze and select the optimal reactor parameters based on the optimization record data, and then select, configure, and connect the reactor.

[0011] A second aspect of this application provides a stable control system for a high-voltage frequency converter under power grid fluctuations, the system comprising:

[0012] A fluctuation characteristic analysis module is used to collect grid operation monitoring data of the target frequency converter connected to the grid, perform fluctuation characteristic analysis, and obtain fluctuation characteristic data. The target frequency converter is a high-voltage frequency converter.

[0013] A control bias analysis module is used to perform control bias analysis based on the amplitude and frequency of multiple fluctuation categories within the fluctuation characteristic data to obtain multiple control biases.

[0014] The first optimization module is used to construct a suppression control function to optimize the reactor parameters of the high-voltage frequency converter connected reactor based on multiple control bias and fluctuation characteristic data, optimize the reactor parameters, and obtain a suppression reactor parameter library.

[0015] The second optimization module is used to construct a cost-reduction control function to optimize the reactor parameters of the high-voltage frequency converter connected reactor based on multiple control bias and fluctuation characteristic data, optimize the reactor parameters, and obtain a cost-reduction reactor parameter library.

[0016] The judgment module is used to determine whether there is an intersection between the suppression reactor parameter library and the cost reduction reactor parameter library. If there is, the multiple intersection reactor parameters within the intersection are sorted, and the optimal reactor parameters are obtained through analysis and selection. If not, the optimal reactor parameters are obtained through analysis and selection based on the optimization record data, and the reactor is selected, configured, and connected.

[0017] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0018] The process involves collecting grid operation monitoring data for the target frequency converter connected to the grid and performing fluctuation characteristic analysis to obtain fluctuation characteristic data. The target frequency converter is a high-voltage frequency converter. Then, based on the fluctuation amplitude and frequency of multiple fluctuation categories within the fluctuation characteristic data, control bias analysis is performed to obtain multiple control biases. Next, based on the multiple control biases and fluctuation characteristic data, a suppression control function is constructed to optimize the reactor parameters of the reactor connected to the high-voltage frequency converter, thus obtaining a suppression reactor parameter library. Simultaneously, based on the multiple control biases and fluctuation characteristic data, a cost reduction control function is constructed to optimize the reactor parameters of the reactor connected to the high-voltage frequency converter, thus obtaining a cost reduction reactor parameter library. Finally, it is determined whether the suppression reactor parameter library and the cost reduction reactor parameter library have an intersection. If they do, the multiple intersection reactor parameters are sorted, and the optimal reactor parameters are selected based on the analysis. If not, the optimal reactor parameters are selected based on the optimized data, and the reactor is then selected and configured for connection. This invention solves the technical problem in existing technologies where high-voltage frequency converters are not stable enough under grid fluctuations due to different parameters of the connected reactors, and achieves the technical effect of improving the stability of high-voltage frequency converters under grid fluctuations. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A schematic flowchart of a method for stabilizing a high-voltage frequency converter under power grid fluctuations, provided in an embodiment of this application;

[0021] Figure 2 This is a schematic diagram of a stable control system for a high-voltage frequency converter under power grid fluctuations, provided as an embodiment of this application.

[0022] Explanation of reference numerals in the attached diagram: 11. Fluctuation characteristic analysis module; 12. Control bias analysis module; 13. First optimization module; 14. Second optimization module; 15. Judgment module. Detailed Implementation

[0023] This application provides a method and system for the stable control of a high-voltage frequency converter under power grid fluctuations, which solves the technical problem in the prior art where the high-voltage frequency converter is not stable enough under power grid fluctuations due to different parameters of the connected reactor.

[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0025] It should be noted that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0026] Example 1

[0027] like Figure 1 As shown in the figure, this application provides a method for stable control of a high-voltage frequency converter under power grid fluctuations, wherein the method includes:

[0028] Collect grid operation monitoring data of the target frequency converter connected to the grid, perform fluctuation characteristic analysis, and obtain fluctuation characteristic data. The target frequency converter is a high-voltage frequency converter.

[0029] The target frequency converter is a high-voltage frequency converter. Data on grid operation monitoring after the target frequency converter is connected to the grid is collected, including data such as voltage, current, power factor, and harmonic content, which comprehensively reflect the real-time operating status of the grid. Fluctuation characteristic analysis is performed on the collected grid operation monitoring data. By identifying the amplitude and frequency of different fluctuation categories in the grid, fluctuation characteristic data is obtained.

[0030] Furthermore, power grid operation monitoring data of the target frequency converter connected to the power grid is collected, and fluctuation characteristic analysis is performed to obtain fluctuation characteristic data, including:

[0031] Collect grid operation monitoring data of the target frequency converter connected to the grid, wherein the grid operation monitoring data includes the cumulative grid voltage data of the grid connected to the target frequency converter;

[0032] Based on the rated grid voltage, multiple fluctuating voltages are extracted from the grid operation monitoring data and classified as multiple fluctuation categories.

[0033] The fluctuation frequencies of the multiple fluctuation categories within the power grid operation monitoring data are extracted to obtain multiple fluctuation frequencies. Combined with the multiple fluctuation categories, fluctuation characteristic data is obtained.

[0034] The process involves collecting grid operation monitoring data after the target frequency converter is connected to the grid. This data includes cumulative grid voltage data connected to the target frequency converter. Based on the rated grid voltage, multiple fluctuating voltages are extracted from the grid operation monitoring data and categorized into several fluctuation types. These fluctuating voltages represent the deviation of the grid voltage from its rated value, reflecting the grid's fluctuation characteristics. Further, the fluctuation frequencies of these fluctuation types within the grid operation monitoring data are extracted. Fluctuation frequency refers to the number of times each fluctuation type occurs, reflecting the frequency of grid fluctuations. Combining the fluctuation types and frequencies, fluctuation characteristic data is obtained. This fluctuation characteristic data includes not only the amplitude of grid voltage fluctuations but also their frequency.

[0035] Based on the amplitude and frequency of multiple fluctuation categories within the fluctuation characteristic data, a control bias analysis is performed to obtain multiple control biases.

[0036] Control bias analysis is performed based on the amplitude and frequency of multiple fluctuation categories within the fluctuation characteristic data. This analysis determines which fluctuation categories should be prioritized and controlled based on the actual situation of power grid fluctuations. Fluctuation categories with larger amplitudes and higher frequencies should be given greater control bias, as these fluctuations have a greater impact on the stable operation of high-voltage frequency converters.

[0037] Furthermore, based on the amplitude and frequency of multiple fluctuation categories within the fluctuation characteristic data, control bias analysis is performed to obtain multiple control biases, including:

[0038] Based on multiple fluctuation frequencies of multiple fluctuation categories within the fluctuation feature data, control bias is allocated to obtain multiple first control biases, wherein the larger the fluctuation frequency, the larger the first control bias.

[0039] Based on the fluctuation voltage amplitude of the multiple fluctuation categories, control bias is allocated to obtain multiple second control biases, wherein the larger the fluctuation voltage amplitude, the larger the second control bias.

[0040] Multiple control weights are calculated based on multiple first control weights and multiple second control weights.

[0041] Based on multiple fluctuation frequencies across multiple fluctuation categories within the fluctuation characteristic data, control bias is allocated to obtain multiple first control biases. Specifically, for each fluctuation category, control bias is allocated according to the magnitude of the fluctuation frequency. A higher fluctuation frequency means that the fluctuation category occurs more frequently in the power grid, resulting in a greater impact on the high-voltage frequency converter; therefore, a larger control bias should be assigned. Secondly, control bias is allocated based on the fluctuation voltage amplitude of multiple fluctuation categories to obtain multiple second control biases. The fluctuation voltage amplitude reflects the degree to which the grid voltage deviates from the rated value; a larger amplitude indicates a more severe impact on the high-voltage frequency converter. Therefore, control bias allocation should be based on the magnitude of the fluctuation voltage amplitude. For fluctuation categories with larger amplitudes, a larger control bias should be assigned to ensure that the high-voltage frequency converter can cope with these larger voltage fluctuations. Combining the multiple first and second control biases, multiple control biases are calculated. During the calculation process, methods such as weighted averaging can be used to organically combine the first and second control biases to obtain the final control bias.

[0042] Based on multiple control bias and fluctuation characteristic data, a suppression control function is constructed to optimize the reactor parameters of the reactor connected to the high-voltage frequency converter. The reactor parameters are optimized to obtain a suppression reactor parameter library.

[0043] The suppression control function refers to adjusting the reactor parameters based on the fluctuation characteristics of the power grid to minimize the impact of grid fluctuations on the high-voltage frequency converter. A suppression control function is constructed based on multiple control biases and fluctuation characteristic data. This function is then used to optimize the reactor parameters, resulting in a suppression reactor parameter library. This library contains combinations of reactor parameters that can effectively suppress high-voltage frequency converter fluctuations under different power grid fluctuation conditions.

[0044] Furthermore, based on multiple control bias and fluctuation characteristic data, a suppression control function is constructed to optimize the reactor parameters of the high-voltage frequency converter connected to the reactor. The optimization of the reactor parameters includes:

[0045] Based on multiple control bias and fluctuation characteristic data, a suppression control function is constructed to optimize the reactor parameters of the high-voltage frequency converter connected to the reactor, as shown in the following equation:

[0046] ;

[0047] Where RES represents the inhibition fitness, and M represents the number of fluctuation categories. For the control bias of the i-th fluctuation category, This refers to the grid fluctuation experienced by the target frequency converter under the i-th fluctuation category after the reactor is connected according to the reactor parameters. The grid fluctuation experienced by the target frequency converter under the i-th fluctuation category;

[0048] The reactor parameters of various reactors are obtained, a reactor parameter library is constructed, and the first reactor parameter is randomly selected from the reactor parameter library and used as the result of stage suppression optimization.

[0049] Based on the parameters of the first reactor, and combined with the multiple fluctuation categories, simulated fluctuation suppression is performed to obtain multiple first suppressed grid fluctuation information. Combined with the suppression control function, the first suppression fitness is calculated.

[0050] The second reactor parameter, which is most similar to the parameter of the first reactor, is randomly selected again, and the second suppression fitness is calculated.

[0051] The stage suppression optimization results are updated based on the second inhibition fitness and the first inhibition fitness.

[0052] Continue iterative optimization until convergence, and output the last updated K-stage suppression optimization results to obtain the suppression reactor parameter library, where K is an integer greater than 1.

[0053] A suppression control function is constructed, where RES represents the suppression fitness, used to measure the degree of suppression of the impact of grid fluctuations on the high-voltage frequency converter under given reactor parameters. M represents the number of multiple fluctuation categories. For the control bias of the i-th fluctuation category, This refers to the grid fluctuation experienced by the target frequency converter under the i-th fluctuation category after the reactor is connected according to the reactor parameters. Let represent the grid fluctuation experienced by the target frequency converter under the i-th fluctuation category. A reactor parameter library is constructed by acquiring reactor parameters from various reactors. A reactor parameter is randomly selected from the library as the initial first reactor parameter, which is then used as the stage suppression optimization result. Based on the first reactor parameter, fluctuation suppression is simulated using multiple fluctuation categories to obtain multiple first-suppressed grid fluctuation information. The first suppression fitness is calculated using a suppression control function, reflecting the suppression effect of grid fluctuations on the high-voltage frequency converter under the current reactor parameters. A second reactor parameter, closest to the first reactor parameter, is selected from the reactor parameter library. The second suppression fitness is obtained by simulating fluctuation suppression and calculating the suppression fitness. The second suppression fitness is compared with the first suppression fitness. If the second suppression fitness is better (i.e., the suppression effect is better), the stage suppression optimization result is updated to the second reactor parameter; otherwise, the current first reactor parameter is retained. Repeat the above steps for iterative optimization until convergence occurs. For example, the suppression fitness no longer changes significantly within a certain range, or the preset maximum number of iterations is reached, at which point iteration stops. Output the last updated K-stage suppression optimization results to obtain the suppression reactor parameter library, where K is an integer greater than 1.

[0054] Furthermore, the discrimination based on the second inhibitory fitness and the first inhibitory fitness includes:

[0055] Determine whether the second suppression fitness is greater than the first suppression fitness. If so, update the stage suppression optimization result to the second reactor parameter.

[0056] If not, then obtain multiple second suppression grid fluctuation information of the second reactor parameters, calculate multiple suppression similarities with the multiple first suppression grid fluctuation information, and calculate the similarity.

[0057] Based on the similarity, an update probability is calculated, where the smaller the similarity, the greater the update probability.

[0058] According to the update probability, the stage suppression optimization result is updated to the second reactor parameter or no update is performed.

[0059] The algorithm determines whether the second suppression fitness is greater than the first suppression fitness. If so, it indicates that the second reactor parameters perform better than the first reactor parameters in suppressing grid fluctuations, and therefore the stage suppression optimization result is updated to the second reactor parameters. If the second suppression fitness is not greater than the first suppression fitness, the suppression similarity between multiple second suppression grid fluctuation information and multiple first suppression grid fluctuation information of the second reactor parameters is calculated. Suppression similarity can be calculated by comparing the fluctuation suppression effects of the two under the corresponding fluctuation categories, for example, using methods such as cosine similarity or Euclidean distance to measure the degree of similarity. The update probability is assigned based on the calculated similarity. The smaller the similarity, the greater the difference between the second and first reactor parameters in suppressing grid fluctuations, and the higher the update probability. Conversely, if the similarity is large, the update probability should be small to avoid excessive invalid updates. The calculated update probability determines whether to update the stage suppression optimization result to the second reactor parameters.

[0060] Based on multiple control bias and fluctuation characteristic data, a cost-reduction control function is constructed to optimize the reactor parameters of the high-voltage frequency converter connected reactor. The reactor parameters are optimized to obtain a cost-reduction reactor parameter library.

[0061] Cost reduction control functions refer to optimizations made to lower operating costs, enabling the selection of reactors with long service lives under varying power fluctuations. Cost reduction control functions are constructed based on multiple control biases and fluctuation characteristic data. These functions are then used to optimize reactor parameters, resulting in a cost-reduced reactor parameter library. This library contains the reactor parameter combinations with the longest service lives under different power grid fluctuation conditions.

[0062] Furthermore, based on multiple control bias and fluctuation characteristic data, a cost-reduction control function is constructed to optimize the reactor parameters of the high-voltage frequency converter connected reactor. The optimization of the reactor parameters includes:

[0063] Based on multiple control bias and fluctuation characteristic data, a cost-reduction control function is constructed to optimize the reactor parameters of the high-voltage frequency converter connected to the reactor, as shown in the following formula:

[0064] ;

[0065] COS represents the cost reduction fitness, and M represents the number of multiple fluctuation categories. For the control bias of the i-th fluctuation category, Let the reactor parameters represent the service life under the i-th fluctuation category. Preset service life;

[0066] Based on the cost reduction control function, the reactor parameters are optimized in the reactor parameter library to obtain the cost reduction reactor parameter library.

[0067] A cost reduction control function is constructed, where COS represents the cost reduction fitness, used to measure the relative relationship between the reactor's service life and its preset service life under given reactor parameters, thus reflecting the effectiveness of cost optimization. M represents the number of multiple fluctuation categories. For the control bias of the i-th fluctuation category, Let the reactor parameters represent the service life under the i-th fluctuation category. The preset service life is determined by optimizing the reactor parameters in the reactor parameter library using a cost-reduction control function, thus obtaining the cost-reduction reactor parameter library.

[0068] Determine whether there is an intersection between the suppression reactor parameter library and the cost reduction reactor parameter library. If so, sort the multiple intersection reactor parameters within the intersection, analyze and select the optimal reactor parameters. If not, analyze and select the optimal reactor parameters based on the optimization record data, and then select, configure, and connect the reactor.

[0069] By comparing the parameter combinations in the suppression reactor parameter library and the cost reduction reactor parameter library, it is determined whether there is any overlap between them. If the two parameter libraries contain the same reactor parameter combinations, then there is an overlap. If an overlap is found, the multiple overlapping reactor parameters need to be sorted, and the optimal reactor parameters are selected through analysis. If no overlap is found, meaning there are no common reactor parameter combinations between the suppression reactor and cost reduction reactor parameter libraries, then the optimal reactor parameters need to be selected through analysis based on the optimization record data. Based on the selected optimal reactor parameters, the reactor is then selected, configured, and connected.

[0070] Furthermore, it is determined whether there is an intersection between the suppression reactor parameter library and the cost reduction reactor parameter library. If so, the parameters of multiple intersecting reactors within the intersection are sorted, and the optimal reactor parameters are selected through analysis. If not, the optimal reactor parameters are selected based on the optimization record data, including:

[0071] Determine whether there is any overlap between the suppression reactor parameter library and the cost reduction reactor parameter library;

[0072] If so, sort the multiple intersection reactor parameters in the intersection according to the order of cost reduction fitness and suppression fitness from large to small, obtain multiple suppression order and multiple cost reduction order, calculate multiple order information, and select the reactor parameter with the smallest order information as the optimal reactor parameter;

[0073] If not, based on the optimization record data, sort all the multiple reactor parameters during the optimization process to obtain multiple overall suppression orders and multiple overall reduction orders. Calculate multiple overall order information and select the reactor parameter with the smallest overall order information as the optimal reactor parameter.

[0074] The parameters in the suppression reactor parameter library and the cost reduction reactor parameter library are compared to determine if there is an intersection. An intersection means that there are common reactor parameter combinations that simultaneously meet the requirements of suppression and cost reduction. If an intersection is found, the reactor parameters within the intersection are sorted from largest to smallest according to cost reduction fitness and suppression fitness, respectively, to obtain the cost reduction fitness order and the suppression fitness order. For each intersection reactor parameter, a sequence information is calculated by combining its suppression fitness and cost reduction fitness order. The sequence information can be a weighted sum of the suppression fitness and cost reduction fitness orders. The sequence information of all intersection reactor parameters is compared, and the reactor parameter with the smallest sequence information is selected as the optimal reactor parameter. If no intersection is found, that is, there are no common reactor parameter combinations between the suppression reactor parameter library and the cost reduction reactor parameter library, all reactor parameters and their corresponding suppression fitness and cost reduction fitness are extracted from the optimization record data. Similarly, all reactor parameters are sorted according to suppression fitness and cost reduction fitness to obtain the overall suppression order and overall cost reduction order. For each reactor parameter, the overall order information is calculated by combining its overall suppression order and overall cost reduction order. The overall order information of all reactor parameters is compared, and the reactor parameter with the smallest overall order information is selected as the optimal reactor parameter.

[0075] In summary, the embodiments of this application have at least the following technical effects:

[0076] The process involves collecting grid operation monitoring data for the target frequency converter connected to the grid and performing fluctuation characteristic analysis to obtain fluctuation characteristic data. The target frequency converter is a high-voltage frequency converter. Then, based on the fluctuation amplitude and frequency of multiple fluctuation categories within the fluctuation characteristic data, control bias analysis is performed to obtain multiple control biases. Next, based on the multiple control biases and fluctuation characteristic data, a suppression control function is constructed to optimize the reactor parameters of the reactor connected to the high-voltage frequency converter, thus obtaining a suppression reactor parameter library. Simultaneously, based on the multiple control biases and fluctuation characteristic data, a cost reduction control function is constructed to optimize the reactor parameters of the reactor connected to the high-voltage frequency converter, thus obtaining a cost reduction reactor parameter library. Finally, it is determined whether the suppression reactor parameter library and the cost reduction reactor parameter library have an intersection. If they do, the multiple intersection reactor parameters are sorted, and the optimal reactor parameters are selected based on the analysis. If not, the optimal reactor parameters are selected based on the optimized data, and the reactor is then selected and configured for connection. This invention solves the technical problem in existing technologies where high-voltage frequency converters are not stable enough under grid fluctuations due to different parameters of the connected reactors, and achieves the technical effect of improving the stability of high-voltage frequency converters under grid fluctuations.

[0077] Example 2

[0078] Based on the same inventive concept as the stability control method of a high-voltage frequency converter under power grid fluctuations in the foregoing embodiments, such as Figure 2 As shown, this application provides a stable control system for a high-voltage frequency converter under grid fluctuations. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0079] The fluctuation characteristic analysis module 11 is used to collect grid operation monitoring data of the target frequency converter connected to the grid, and perform fluctuation characteristic analysis to obtain fluctuation characteristic data. The target frequency converter is a high-voltage frequency converter.

[0080] The control bias analysis module 12 is used to perform control bias analysis based on the fluctuation amplitude and frequency of multiple fluctuation categories in the fluctuation characteristic data to obtain multiple control biases.

[0081] The first optimization module 13 is used to construct a suppression control function to optimize the reactor parameters of the high-voltage frequency converter connected reactor based on multiple control bias and fluctuation characteristic data, optimize the reactor parameters, and obtain a suppression reactor parameter library.

[0082] The second optimization module 14 is used to construct a cost-reduction control function to optimize the reactor parameters of the high-voltage frequency converter connected reactor based on multiple control bias and fluctuation characteristic data, optimize the reactor parameters, and obtain a cost-reduction reactor parameter library.

[0083] The judgment module 15 is used to determine whether there is an intersection between the suppression reactor parameter library and the cost reduction reactor parameter library. If there is, the multiple intersection reactor parameters within the intersection are sorted, and the optimal reactor parameter is obtained by analysis and selection. If not, the optimal reactor parameter is obtained by analysis and selection based on the optimization record data, and the reactor is selected, configured, and connected.

[0084] Furthermore, the fluctuation characteristic analysis module 11 is used to perform the following methods:

[0085] Collect grid operation monitoring data of the target frequency converter connected to the grid, wherein the grid operation monitoring data includes the cumulative grid voltage data of the grid connected to the target frequency converter;

[0086] Based on the rated grid voltage, multiple fluctuating voltages are extracted from the grid operation monitoring data and classified as multiple fluctuation categories.

[0087] The fluctuation frequencies of the multiple fluctuation categories within the power grid operation monitoring data are extracted to obtain multiple fluctuation frequencies. Combined with the multiple fluctuation categories, fluctuation characteristic data is obtained.

[0088] Furthermore, the control bias analysis module 12 is used to perform the following method:

[0089] Based on multiple fluctuation frequencies of multiple fluctuation categories within the fluctuation feature data, control bias is allocated to obtain multiple first control biases, wherein the larger the fluctuation frequency, the larger the first control bias.

[0090] Based on the fluctuation voltage amplitude of the multiple fluctuation categories, control bias is allocated to obtain multiple second control biases, wherein the larger the fluctuation voltage amplitude, the larger the second control bias.

[0091] Multiple control weights are calculated based on multiple first control weights and multiple second control weights.

[0092] Furthermore, the first optimization module 13 is used to perform the following method:

[0093] Based on multiple control bias and fluctuation characteristic data, a suppression control function is constructed to optimize the reactor parameters of the high-voltage frequency converter connected to the reactor, as shown in the following equation:

[0094] ;

[0095] Where RES represents the inhibition fitness, and M represents the number of fluctuation categories. For the control bias of the i-th fluctuation category, This refers to the grid fluctuation experienced by the target frequency converter under the i-th fluctuation category after the reactor is connected according to the reactor parameters. The grid fluctuation experienced by the target frequency converter under the i-th fluctuation category;

[0096] The reactor parameters of various reactors are obtained, a reactor parameter library is constructed, and the first reactor parameter is randomly selected from the reactor parameter library and used as the result of stage suppression optimization.

[0097] Based on the parameters of the first reactor, and combined with the multiple fluctuation categories, simulated fluctuation suppression is performed to obtain multiple first suppressed grid fluctuation information. Combined with the suppression control function, the first suppression fitness is calculated.

[0098] The second reactor parameter, which is most similar to the parameter of the first reactor, is randomly selected again, and the second suppression fitness is calculated.

[0099] The stage suppression optimization results are updated based on the second inhibition fitness and the first inhibition fitness.

[0100] Continue iterative optimization until convergence, and output the last updated K-stage suppression optimization results to obtain the suppression reactor parameter library, where K is an integer greater than 1.

[0101] Furthermore, the first optimization module 13 is used to perform the following method:

[0102] Determine whether the second suppression fitness is greater than the first suppression fitness. If so, update the stage suppression optimization result to the second reactor parameter.

[0103] If not, then obtain multiple second suppression grid fluctuation information of the second reactor parameters, calculate multiple suppression similarities with the multiple first suppression grid fluctuation information, and calculate the similarity.

[0104] Based on the similarity, an update probability is calculated, where the smaller the similarity, the greater the update probability.

[0105] According to the update probability, the stage suppression optimization result is updated to the second reactor parameter or no update is performed.

[0106] Furthermore, the second optimization module 14 is used to perform the following method:

[0107] Based on multiple control bias and fluctuation characteristic data, a cost-reduction control function is constructed to optimize the reactor parameters of the high-voltage frequency converter connected to the reactor, as shown in the following formula:

[0108] ;

[0109] COS represents the cost reduction fitness, and M represents the number of multiple fluctuation categories. For the control bias of the i-th fluctuation category, Let the reactor parameters represent the service life under the i-th fluctuation category. Preset service life;

[0110] Based on the cost reduction control function, the reactor parameters are optimized in the reactor parameter library to obtain the cost reduction reactor parameter library.

[0111] Furthermore, the determination module 15 is used to perform the following method:

[0112] Determine whether there is any overlap between the suppression reactor parameter library and the cost reduction reactor parameter library;

[0113] If so, sort the multiple intersection reactor parameters in the intersection according to the order of cost reduction fitness and suppression fitness from large to small, obtain multiple suppression order and multiple cost reduction order, calculate multiple order information, and select the reactor parameter with the smallest order information as the optimal reactor parameter;

[0114] If not, based on the optimization record data, sort all the multiple reactor parameters during the optimization process to obtain multiple overall suppression orders and multiple overall reduction orders. Calculate multiple overall order information and select the reactor parameter with the smallest overall order information as the optimal reactor parameter.

[0115] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0116] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0117] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for stable control of a high-voltage frequency converter under power grid fluctuations, characterized in that, The method includes: Collect grid operation monitoring data of the target frequency converter connected to the grid, perform fluctuation characteristic analysis, and obtain fluctuation characteristic data. The target frequency converter is a high-voltage frequency converter. Based on the fluctuation amplitude and frequency of multiple fluctuation categories within the fluctuation characteristic data, control bias analysis is performed to obtain multiple control biases, including: Based on multiple fluctuation frequencies of multiple fluctuation categories within the fluctuation feature data, control bias is allocated to obtain multiple first control biases, wherein the larger the fluctuation frequency, the larger the first control bias. Based on the fluctuation voltage amplitude of the multiple fluctuation categories, control bias is allocated to obtain multiple second control biases, wherein the larger the fluctuation voltage amplitude, the larger the second control bias. Multiple control weights are calculated based on multiple first control weights and multiple second control weights. The control bias analysis refers to determining the priority categories of fluctuations to focus on and control based on the actual situation of power grid fluctuations, and giving higher control bias to fluctuation categories with larger fluctuation amplitudes and higher frequencies. Based on multiple control bias and fluctuation characteristic data, a suppression control function is constructed to optimize the reactor parameters of the reactor connected to the high-voltage frequency converter. The reactor parameters are optimized to obtain a suppression reactor parameter library. The suppression control function refers to adjusting the reactor parameters according to the fluctuation characteristics of the power grid to suppress the impact of power grid fluctuations on the high-voltage frequency converter to the greatest extent. Based on multiple control bias and fluctuation characteristic data, a cost-reduction control function is constructed to optimize the reactor parameters of the high-voltage frequency converter connected reactor. The reactor parameters are optimized to obtain a cost-reduction reactor parameter library. The cost-reduction control function refers to optimizing to reduce the use cost so as to select reactors with long service life under different fluctuations. Determine whether there is an intersection between the suppression reactor parameter library and the cost reduction reactor parameter library. If so, sort the multiple intersection reactor parameters within the intersection, analyze and select the optimal reactor parameters. If not, analyze and select the optimal reactor parameters based on the optimization record data, and then select, configure, and connect the reactor.

2. The method according to claim 1, characterized in that, Collect grid operation monitoring data of the target frequency converter connected to the grid, and perform fluctuation characteristic analysis to obtain fluctuation characteristic data, including: Collect grid operation monitoring data of the target frequency converter connected to the grid, wherein the grid operation monitoring data includes the cumulative grid voltage data of the grid connected to the target frequency converter; Based on the rated grid voltage, multiple fluctuating voltages are extracted from the grid operation monitoring data and classified as multiple fluctuation categories. The fluctuation frequencies of the multiple fluctuation categories within the power grid operation monitoring data are extracted to obtain multiple fluctuation frequencies. Combined with the multiple fluctuation categories, fluctuation characteristic data is obtained.

3. The method according to claim 1, characterized in that, Based on multiple control bias and fluctuation characteristic data, a suppression control function is constructed to optimize the reactor parameters of the high-voltage frequency converter connected to the reactor. The optimization of the reactor parameters includes: Based on multiple control bias and fluctuation characteristic data, a suppression control function is constructed to optimize the reactor parameters of the high-voltage frequency converter connected to the reactor, as shown in the following equation: ； Where RES represents the inhibition fitness, and M represents the number of fluctuation categories. For the control bias of the i-th fluctuation category, This refers to the grid fluctuation experienced by the target frequency converter under the i-th fluctuation category after the reactor is connected according to the reactor parameters. The grid fluctuation experienced by the target frequency converter under the i-th fluctuation category; The reactor parameters of various reactors are obtained, a reactor parameter library is constructed, and the first reactor parameter is randomly selected from the reactor parameter library and used as the result of stage suppression optimization. Based on the parameters of the first reactor, and combined with the multiple fluctuation categories, simulated fluctuation suppression is performed to obtain multiple first suppressed grid fluctuation information. Combined with the suppression control function, the first suppression fitness is calculated. The second reactor parameter, which is most similar to the parameter of the first reactor, is randomly selected again, and the second suppression fitness is calculated. The stage suppression optimization results are updated based on the second inhibition fitness and the first inhibition fitness. Continue iterative optimization until convergence, and output the last updated K-stage suppression optimization results to obtain the suppression reactor parameter library, where K is an integer greater than 1.

4. The method according to claim 3, characterized in that, The discrimination is based on the second inhibition fitness and the first inhibition fitness, including: Determine whether the second suppression fitness is greater than the first suppression fitness. If so, update the stage suppression optimization result to the second reactor parameter. If not, then obtain multiple second suppression grid fluctuation information of the second reactor parameters, calculate multiple suppression similarities with the multiple first suppression grid fluctuation information, and calculate the similarity. Based on the similarity, an update probability is calculated, where the smaller the similarity, the greater the update probability. According to the update probability, the stage suppression optimization result is updated to the second reactor parameter or no update is performed.

5. The method according to claim 3, characterized in that, Based on multiple control bias and fluctuation characteristic data, a cost-reduction control function is constructed to optimize the reactor parameters of the high-voltage frequency converter connected to the reactor. The optimization of the reactor parameters includes: Based on multiple control bias and fluctuation characteristic data, a cost-reduction control function is constructed to optimize the reactor parameters of the high-voltage frequency converter connected to the reactor, as shown in the following formula: ； COS represents the cost reduction fitness, and M represents the number of multiple fluctuation categories. For the control bias of the i-th fluctuation category, Let the reactor parameters represent the service life under the i-th fluctuation category. Preset service life; Based on the cost reduction control function, the reactor parameters are optimized in the reactor parameter library to obtain the cost reduction reactor parameter library.

6. The method according to claim 5, characterized in that, Determine whether there is an intersection between the suppression reactor parameter library and the cost reduction reactor parameter library. If so, sort the multiple intersection reactor parameters within the intersection and analyze and select the optimal reactor parameters. If not, analyze and select the optimal reactor parameters based on the optimization record data, including: Determine whether there is any overlap between the suppression reactor parameter library and the cost reduction reactor parameter library; If so, sort the multiple intersection reactor parameters in the intersection according to the order of cost reduction fitness and suppression fitness from large to small, obtain multiple suppression order and multiple cost reduction order, calculate multiple order information, and select the reactor parameter with the smallest order information as the optimal reactor parameter; If not, based on the optimization record data, sort all the multiple reactor parameters during the optimization process to obtain multiple overall suppression orders and multiple overall reduction orders. Calculate multiple overall order information and select the reactor parameter with the smallest overall order information as the optimal reactor parameter.

7. A stable control system for a high-voltage frequency converter under power grid fluctuations, characterized in that, For implementing the stability control method of a high-voltage frequency converter under power grid fluctuations according to any one of claims 1-6, the system comprises: A fluctuation characteristic analysis module is used to collect grid operation monitoring data of the target frequency converter connected to the grid, perform fluctuation characteristic analysis, and obtain fluctuation characteristic data. The target frequency converter is a high-voltage frequency converter. A control bias analysis module is used to perform control bias analysis based on the amplitude and frequency of multiple fluctuation categories within the fluctuation characteristic data to obtain multiple control biases. The first optimization module is used to construct a suppression control function to optimize the reactor parameters of the high-voltage frequency converter connected reactor based on multiple control bias and fluctuation characteristic data, optimize the reactor parameters, and obtain a suppression reactor parameter library. The second optimization module is used to construct a cost-reduction control function to optimize the reactor parameters of the high-voltage frequency converter connected reactor based on multiple control bias and fluctuation characteristic data, optimize the reactor parameters, and obtain a cost-reduction reactor parameter library. The judgment module is used to determine whether there is an intersection between the suppression reactor parameter library and the cost reduction reactor parameter library. If there is, the multiple intersection reactor parameters within the intersection are sorted, and the optimal reactor parameters are obtained through analysis and selection. If not, the optimal reactor parameters are obtained through analysis and selection based on the optimization record data, and the reactor is selected, configured, and connected.

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