Stable control method and system for high-voltage frequency converter under power grid fluctuation

Abstract

The invention discloses a stable control method and system for a high-voltage frequency converter under power grid fluctuation, and relates to the technical field of frequency converters. The method comprises the following steps: collecting data of a target frequency converter, and carrying out fluctuation characteristic analysis to obtain fluctuation characteristic data; performing control bias weight analysis to obtain a plurality of control bias weights; constructing a suppression control function to obtain a suppression reactor parameter library; constructing a cost reduction control function, and obtaining a cost reduction reactor parameter library; and judging whether an intersection exists between the suppression reactor parameter library and the reduction reactor parameter library, if so, sorting the reactor parameters in the intersection, and selecting to obtain an optimal reactor parameter, and if not, selecting to obtain the optimal reactor parameter according to the optimization record data, and performing selective configuration access of the reactor. The technical problem that the high-voltage frequency converter is not stable enough under power grid fluctuation due to different accessed reactor parameters in the prior art is solved, and the technical effect of improving the stability of the high-voltage frequency converter under power grid fluctuation is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of frequency converters, and in particular to a method and system for stabilizing control of a high-voltage frequency converter under power grid fluctuations. Background Art

[0002] With the development of modern industry, the power grid system is facing an increasingly complex operating environment, and power grid fluctuations have become one of the key factors affecting the stable operation of high-voltage inverters. As an important device in the power system, the stability of the performance of high-voltage inverters is directly related to the safe and efficient operation of the power grid system. Therefore, how to effectively control the stable operation of high-voltage inverters under power grid fluctuations has become an important issue that needs to be urgently solved in the field of power technology. In the prior art, reactors are connected to the inverter to improve the power factor of the power grid and reduce harmonic interference, thereby ensuring the safe and stable operation of the high-voltage inverter. However, the effect of reactors with different parameters in suppressing the influence of power grid fluctuations on the inverter varies according to the actual situation, resulting in the technical problem that the high-voltage inverter is not stable enough under power grid fluctuations. Summary of the invention

[0003] The embodiments of the present application provide a method and system for stable control of a high-voltage inverter under grid fluctuations, which solves the technical problem in the prior art that the high-voltage inverter is not stable enough under grid fluctuations due to different parameters of the connected reactor.

[0004] In view of the above problems, an embodiment of the present application provides a method and system for stable control of a high-voltage inverter under power grid fluctuations.

[0005] A first aspect of an embodiment of the present application provides a method for stable control of a high-voltage inverter under power grid fluctuations, the method comprising: Collecting grid operation monitoring data of a target frequency converter connected to a grid, and performing fluctuation characteristic analysis to obtain fluctuation characteristic data, wherein the target frequency converter is a high-voltage frequency converter; Performing control bias analysis according to the fluctuation amplitudes and frequencies of multiple fluctuation categories in the fluctuation characteristic data to obtain multiple control biases; According to multiple control bias and fluctuation characteristic data, a suppression control function is constructed to optimize the reactor parameters of the high-voltage inverter connected to the reactor, the reactor parameters are optimized, and a suppression reactor parameter library is obtained; 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 inverter connected to the reactor, and the reactor parameters are optimized to obtain a cost reduction reactor parameter library; 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 in the intersection, analyze and select to obtain the optimal reactor parameters. If not, analyze and select to obtain the optimal reactor parameters based on the optimization record data, and select and configure the reactor for access.

[0006] A second aspect of the embodiments of the present application provides a stable control system for a high-voltage inverter under power grid fluctuations, the system comprising: A fluctuation characteristic analysis module, the fluctuation characteristic analysis module is used to collect the grid operation monitoring data of the target inverter connected to the grid, and perform fluctuation characteristic analysis to obtain fluctuation characteristic data, the target inverter is a high-voltage inverter; A control bias analysis module, the control bias analysis module is used to perform control bias analysis according to the fluctuation amplitude and frequency of multiple fluctuation categories in the fluctuation characteristic data to obtain multiple control biases; A first optimization module, wherein the first optimization module is used to construct a suppression control function for optimizing the reactor parameters of the high-voltage inverter connected to the reactor according to a plurality of control bias and fluctuation characteristic data, optimize the reactor parameters, and obtain a suppression reactor parameter library; A second optimization module, the second optimization module is used to construct a cost reduction control function for optimizing the reactor parameters of the high-voltage inverter connected to the reactor according to multiple control bias and fluctuation characteristic data, optimize the reactor parameters, and obtain a cost reduction reactor parameter library; A judgment module is used to judge whether there is an intersection between the suppression reactor parameter library and the cost reduction reactor parameter library. If so, multiple intersection reactor parameters in the intersection are sorted, and the optimal reactor parameters are selected and analyzed. If not, the optimal reactor parameters are analyzed and selected according to the optimization record data, and the reactor is selected and configured for access.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: The grid operation monitoring data of the target inverter connected to the grid is collected, and the fluctuation characteristic analysis is performed to obtain the fluctuation characteristic data. The target inverter is a high-voltage inverter. Then, according to the fluctuation amplitude and frequency of multiple fluctuation categories in the fluctuation characteristic data, the control weight analysis is performed to obtain multiple control weights. Then, according to the multiple control weights and fluctuation characteristic data, a suppression control function is constructed to optimize the reactor parameters of the high-voltage inverter connected to the reactor, the reactor parameters are optimized, and a suppression reactor parameter library is obtained. At the same time, according to the multiple control weights and fluctuation characteristic data, a cost reduction control function is constructed to optimize the reactor parameters of the high-voltage inverter connected to the reactor, the reactor parameters are optimized, and a cost reduction reactor parameter library is obtained. Finally, it is determined whether there is an intersection between the suppression reactor parameter library and the cost reduction reactor parameter library. If so, the multiple intersection reactor parameters in the intersection are sorted, and the optimal reactor parameters are selected and analyzed. If not, according to the optimization record data, the optimal reactor parameters are analyzed and selected to select and configure the reactor for access. The invention 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, and achieves the technical effect of improving the stability of the high-voltage frequency converter under power grid fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic flow chart of a stable control method for a high-voltage inverter under power grid fluctuations provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a stable control system for a high-voltage inverter under power grid fluctuations provided in an embodiment of the present application.

[0010] Explanation of the accompanying drawings: fluctuation characteristic analysis module 11, control bias analysis module 12, first optimization module 13, second optimization module 14, judgment module 15. DETAILED DESCRIPTION

[0011] The embodiments of the present application provide a method and system for stable control of a high-voltage inverter under grid fluctuations, thereby solving the technical problem in the prior art that the high-voltage inverter is not stable enough under grid fluctuations due to different parameters of the connected reactor.

[0012] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0013] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. 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 explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.

[0014] Embodiment 1 like Figure 1 As shown, an embodiment of the present application provides a method for stable control of a high-voltage inverter under power grid fluctuations, wherein the method includes: Collecting grid operation monitoring data of a target frequency converter connected to a grid, and performing fluctuation characteristic analysis to obtain fluctuation characteristic data, wherein the target frequency converter is a high-voltage frequency converter; The target inverter is a high-voltage inverter. The grid operation monitoring data after the target inverter is connected to the grid is collected, including voltage, current, power factor, harmonic content and other data that can fully reflect the real-time operation status of the grid. The collected grid operation monitoring data is analyzed for fluctuation characteristics, and the fluctuation characteristic data is obtained by identifying the fluctuation amplitude and frequency of different fluctuation categories in the grid.

[0015] Furthermore, the grid operation monitoring data of the target inverter connected to the grid is collected, and the fluctuation characteristic analysis is performed to obtain the fluctuation characteristic data, including: Collecting grid operation monitoring data of the target inverter connected to the grid, wherein the grid operation monitoring data includes the grid voltage accumulation data of the grid connected to the target inverter; Extracting and obtaining a plurality of fluctuating voltages in the power grid operation monitoring data according to the rated power grid voltage as a plurality of fluctuation categories; The fluctuation frequencies of the multiple fluctuation categories in the power grid operation monitoring data are extracted to obtain multiple fluctuation frequencies, and the fluctuation characteristic data are obtained by combining the multiple fluctuation categories.

[0016] The grid operation monitoring data after the target inverter is connected to the grid is collected. The grid operation monitoring data includes the accumulated grid voltage data of the target inverter connected to the grid. According to the rated grid voltage, multiple fluctuating voltages are extracted from the grid operation monitoring data, and the multiple fluctuating voltages are used as multiple fluctuation categories. The fluctuating voltage represents the deviation of the grid voltage from the rated value, reflecting the fluctuation of the grid. The fluctuation frequency of multiple fluctuation categories in the grid operation monitoring data is further extracted. The fluctuation frequency refers to the number of times or frequency of each fluctuation category, reflecting the frequency of grid fluctuations. The fluctuation characteristic data is obtained by combining the fluctuation category and the fluctuation frequency. The fluctuation characteristic data includes not only the fluctuation amplitude of the grid voltage, but also the frequency of the fluctuation.

[0017] Performing control bias analysis according to the fluctuation amplitudes and frequencies of multiple fluctuation categories in the fluctuation characteristic data to obtain multiple control biases; The control bias analysis is performed based on the fluctuation amplitude and frequency of multiple fluctuation categories in the fluctuation characteristic data. The control bias analysis refers to determining which fluctuation categories to prioritize and control based on the actual situation of power grid fluctuations. For fluctuation categories with large fluctuation amplitude and high frequency, a higher control bias should be given, because these fluctuations have a greater impact on the stable operation of the high-voltage inverter.

[0018] Furthermore, according to the fluctuation amplitudes and frequencies of multiple fluctuation categories in the fluctuation characteristic data, a control bias analysis is performed to obtain multiple control biases, including: According to the multiple fluctuation frequencies of the multiple fluctuation categories in the fluctuation characteristic data, control weight allocation is performed to obtain multiple first control weights, wherein the greater the fluctuation frequency, the greater the first control weight; According to the fluctuation voltage amplitudes of the plurality of fluctuation categories, control bias allocation is performed to obtain a plurality of second control biases, wherein the greater the fluctuation voltage amplitude, the greater the second control bias; A plurality of control biases are calculated based on the plurality of first control biases and the plurality of second control biases.

[0019] According to the multiple fluctuation frequencies of multiple fluctuation categories in the fluctuation characteristic data, control weights are allocated to obtain multiple first control weights. Specifically, for each fluctuation category, control weights are allocated according to the magnitude of the fluctuation frequency. The greater the fluctuation frequency, the more times the fluctuation category appears in the power grid, and the greater the impact on the high-voltage inverter, so a greater control weight should be given. Secondly, control weights are allocated according to the fluctuation voltage amplitudes of multiple fluctuation categories to obtain multiple second control weights. The fluctuation voltage amplitude reflects the degree to which the grid voltage deviates from the rated value. The larger the amplitude, the more serious the impact on the high-voltage inverter. Therefore, when allocating control weights, they should be allocated according to the magnitude of the fluctuation voltage amplitude. For fluctuation categories with larger amplitudes, a greater control weight should be given to ensure that the high-voltage inverter can cope with these larger voltage fluctuations. Combining multiple first control weights and multiple second control weights, multiple control weights are obtained by calculation. In the calculation process, weighted average and other methods can be used to organically combine the first control weight and the second control weight to obtain the final control weight.

[0020] According to multiple control bias and fluctuation characteristic data, a suppression control function is constructed to optimize the reactor parameters of the high-voltage inverter connected to the reactor, the reactor parameters are optimized, and a suppression reactor parameter library is obtained; The suppression control function refers to adjusting the parameters of the reactor according to the fluctuation characteristics of the power grid to minimize the impact of the power grid fluctuation on the high-voltage inverter. The suppression control function is constructed based on multiple control biases and fluctuation characteristic data, and the reactor parameters are optimized using the suppression control function to obtain a suppression reactor parameter library. The suppression reactor parameter library contains a combination of reactor parameters that can effectively suppress the high-voltage inverter under different power grid fluctuation conditions.

[0021] Furthermore, based on a plurality of control biases and fluctuation characteristic data, a suppression control function is constructed to optimize the reactor parameters of the high-voltage inverter connected to the reactor, and the reactor parameters are optimized, including: According to multiple control bias and fluctuation characteristic data, a suppression control function is constructed to optimize the reactor parameters of the high-voltage inverter connected to the reactor, as shown in the following formula: ; Among them, RES is the suppression fitness, M is the number of multiple fluctuation categories, is the control weight of the ith volatility category, is the grid fluctuation to which the target inverter is subjected in the ith fluctuation category after the reactor is connected according to the reactor parameters, is the grid fluctuation to which the target inverter is subjected under the i-th fluctuation category; Acquire reactor parameters of multiple reactors, build a reactor parameter library, randomly select a first reactor parameter in the reactor parameter library, and use it as a stage suppression optimization result; Based on the first reactor parameter, combined with the multiple fluctuation categories, simulated fluctuation suppression is performed to obtain multiple first suppressed power grid fluctuation information, and combined with the suppression control function, a first suppression fitness is calculated; Randomly selecting a second reactor parameter that is closest to the first reactor parameter again, and calculating to obtain a second suppression fitness; According to the second inhibition fitness and the first inhibition fitness, a determination is made, and a stage inhibition optimization result is updated; Continue to perform iterative optimization until convergence, output the last updated K stage suppression optimization results, and obtain the suppression reactor parameter library, where K is an integer greater than 1.

[0022] Construct a suppression control function, where RES is the suppression fitness used to measure the degree of suppression of the impact of grid fluctuations on the high-voltage inverter under given reactor parameters. M is the number of multiple fluctuation categories, is the control weight of the ith volatility category, is the grid fluctuation to which the target inverter is subjected in the ith fluctuation category after the reactor is connected according to the reactor parameters, is the grid fluctuation to which the target inverter is subjected under the i-th fluctuation category. The reactor parameters of multiple reactors are obtained to construct a reactor parameter library. A reactor parameter is randomly selected from the reactor parameter library as the initial first reactor parameter and used as the stage suppression optimization result. Based on the first reactor parameter, simulated fluctuation suppression is performed in combination with multiple fluctuation categories to obtain multiple first suppression grid fluctuation information, and the first suppression fitness is calculated using the suppression control function. The first suppression fitness reflects the suppression effect of grid fluctuation on the high-voltage inverter under the current reactor parameters. From the reactor parameter library, the second reactor parameter closest to the first reactor parameter is selected. The second suppression fitness is obtained by simulating fluctuation suppression and calculating 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 maintained. Repeat the above steps and continuously perform iterative optimization until convergence, for example, the suppression fitness no longer changes significantly within a certain range, or the preset maximum number of iterations is reached, and the iteration is stopped. The last updated K stage suppression optimization results are output to obtain the suppression reactor parameter library, where K is an integer greater than 1.

[0023] Further, the determination is made according to the second inhibition fitness and the first inhibition fitness, including: Determine whether the second suppression fitness is greater than the first suppression fitness, and if so, update the stage suppression optimization result to the second reactor parameter; If not, then obtaining a plurality of second power grid fluctuation suppression information of the second reactor parameter, calculating a plurality of suppression similarities with the plurality of first power grid fluctuation suppression information, and calculating and obtaining the similarity; According to the similarity, the update probability is calculated and allocated, wherein the smaller the similarity, the greater the update probability; According to the update probability, the phase suppression optimization result is updated to the second reactor parameter or not updated.

[0024] It is determined whether the second suppression fitness is greater than the first suppression fitness. If so, it means that the second reactor parameter is better than the first reactor parameter in suppressing power grid fluctuations, so the stage suppression optimization result is updated to the second reactor parameter. If the second suppression fitness is not greater than the first suppression fitness, the suppression similarity between the second plurality of second power grid fluctuation suppression information and the first plurality of first power grid fluctuation suppression information of the second reactor parameter is calculated. The suppression similarity can be calculated by comparing the fluctuation suppression effects of the two under the corresponding fluctuation category, for example, using cosine similarity, Euclidean distance and other methods to measure the similarity. The update probability is allocated according to the calculated similarity. The smaller the similarity, the greater the difference between the second reactor parameter and the first reactor parameter in suppressing power grid fluctuations, and the greater the update probability. On the contrary, if the similarity is large, the update probability should be small to avoid too many invalid updates. It is determined whether to update the stage suppression optimization result to the second reactor parameter according to the calculated update probability.

[0025] 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 inverter connected to the reactor, and the reactor parameters are optimized to obtain a cost reduction reactor parameter library; The cost reduction control function is to optimize the use cost so as to select the reactor with long service life under different fluctuations. The cost reduction control function is constructed based on multiple control biases and fluctuation characteristic data, and the reactor parameters are optimized using the cost reduction control function to obtain the cost reduction reactor parameter library. The cost reduction reactor parameter library contains the reactor parameter combination with the longest service life under different power grid fluctuation conditions.

[0026] Furthermore, based on multiple control biases and fluctuation characteristic data, a cost reduction control function is constructed to optimize the reactor parameters of the high-voltage inverter connected to the reactor, and the reactor parameters are optimized, including: 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 inverter connected to the reactor, as shown in the following formula: ; COS is the cost reduction fitness, M is the number of multiple fluctuation categories, is the control weight of the ith volatility category, is the service life of the reactor parameters under the i-th fluctuation category, The preset service life; According to the cost reduction control function, the reactor parameters are optimized in the reactor parameter library to obtain the cost reduction reactor parameter library.

[0027] Construct a cost reduction control function. In the cost reduction control function, COS is the cost reduction fitness, which is used to measure the relative relationship between the service life of the reactor and the preset service life under given reactor parameters, thereby reflecting the effect of cost optimization. M is the number of multiple fluctuation categories, is the control weight of the ith volatility category, is the service life of the reactor parameters under the i-th fluctuation category, The cost reduction control function is used to optimize the reactor parameters in the reactor parameter library to obtain the cost reduction reactor parameter library.

[0028] 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 in the intersection, analyze and select to obtain the optimal reactor parameters. If not, analyze and select to obtain the optimal reactor parameters based on the optimization record data, and select and configure the reactor for access.

[0029] By comparing the parameter combinations in the suppression reactor parameter library and the cost reduction reactor parameter library, it is determined whether there is an intersection between the suppression reactor parameter library and the cost reduction reactor parameter library. If the two parameter libraries have the same reactor parameter combination, then there is an intersection. If it is determined that there is an intersection, it is necessary to sort the multiple intersection reactor parameters in the intersection. After the sorting is completed, the optimal reactor parameters are analyzed and selected. If it is determined that there is no intersection, that is, there is no common reactor parameter combination between the suppression reactor parameter library and the cost reduction reactor parameter library, it is necessary to analyze and select the optimal reactor parameters based on the optimization record data. According to the selected optimal reactor parameters, the reactor is selected, configured and connected.

[0030] Further, it is determined whether the suppression reactor parameter library and the cost reduction reactor parameter library have an intersection. If so, multiple intersection reactor parameters in the intersection are sorted, and the optimal reactor parameters are selected by analysis. If not, the optimal reactor parameters are selected by analysis according to the optimization record data, including: Determine whether the suppression reactor parameter library and the cost reduction reactor parameter library have an intersection; 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 orders and multiple reduction orders, and calculate to obtain multiple order information, and select the reactor parameter with the smallest order information as the optimal reactor parameter; If not, all the multiple reactor parameters in the optimization process are sorted according to the optimization record data to obtain multiple overall suppression orders and multiple overall descending orders, multiple overall order information is calculated, and the reactor parameters with the smallest overall order information are selected as the optimal reactor parameters.

[0031] Compare the parameters in the suppression reactor parameter library and the cost reduction reactor parameter library to determine whether there is an intersection. The intersection means that there is a common combination of reactor parameters that meets the requirements of suppression and cost reduction at the same time. If it is determined that there is an intersection, the reactor parameters in the intersection are sorted from large to small according to the cost reduction fitness and the suppression fitness, respectively, so as to obtain a descending order based on the cost reduction fitness and a suppression order based on the suppression fitness. For each intersection reactor parameter, an order information is calculated in combination with its suppression order and descending order. The order information can be a weighted sum of the suppression order and the descending order. Compare the order information of all intersection reactor parameters, and select the reactor parameter with the smallest order information as the optimal reactor parameter. If it is determined that there is no intersection, that is, there is no common combination of reactor parameters 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 the suppression fitness and cost reduction fitness to obtain the overall suppression order and the overall descending order. For each reactor parameter, the overall order information is calculated by combining its overall suppression order and the overall descending 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.

[0032] In summary, the embodiments of the present application have at least the following technical effects: The grid operation monitoring data of the target inverter connected to the grid is collected, and the fluctuation characteristic analysis is performed to obtain the fluctuation characteristic data. The target inverter is a high-voltage inverter. Then, according to the fluctuation amplitude and frequency of multiple fluctuation categories in the fluctuation characteristic data, the control weight analysis is performed to obtain multiple control weights. Then, according to the multiple control weights and fluctuation characteristic data, a suppression control function is constructed to optimize the reactor parameters of the high-voltage inverter connected to the reactor, the reactor parameters are optimized, and a suppression reactor parameter library is obtained. At the same time, according to the multiple control weights and fluctuation characteristic data, a cost reduction control function is constructed to optimize the reactor parameters of the high-voltage inverter connected to the reactor, the reactor parameters are optimized, and a cost reduction reactor parameter library is obtained. Finally, it is determined whether there is an intersection between the suppression reactor parameter library and the cost reduction reactor parameter library. If so, the multiple intersection reactor parameters in the intersection are sorted, and the optimal reactor parameters are selected and analyzed. If not, according to the optimization record data, the optimal reactor parameters are analyzed and selected to select and configure the reactor for access. The invention 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, and achieves the technical effect of improving the stability of the high-voltage frequency converter under power grid fluctuations.

[0033] Embodiment 2 Based on the same inventive concept as the stable control method of a high-voltage inverter under power grid fluctuation in the above-mentioned embodiment, Figure 2 As shown, the present application provides a stable control system for a high-voltage inverter under power grid fluctuations. The system and method embodiments in the present application are based on the same inventive concept. The system includes: A fluctuation characteristic analysis module 11, the fluctuation characteristic analysis module 11 is used to collect the grid operation monitoring data of the target inverter connected to the grid, and perform fluctuation characteristic analysis to obtain fluctuation characteristic data, the target inverter is a high-voltage inverter; A control bias analysis module 12, the control bias analysis module 12 is used to perform control bias analysis according to the fluctuation amplitude and frequency of multiple fluctuation categories in the fluctuation characteristic data to obtain multiple control biases; A first optimization module 13, the first optimization module 13 is used to construct a suppression control function for optimizing the reactor parameters of the high-voltage inverter connected to the reactor according to a plurality of control bias and fluctuation characteristic data, optimize the reactor parameters, and obtain a suppression reactor parameter library; A second optimization module 14, the second optimization module 14 is used to construct a cost reduction control function for optimizing the reactor parameters of the high-voltage inverter connected to the reactor according to a plurality of control bias and fluctuation characteristic data, optimize the reactor parameters, and obtain a cost reduction reactor parameter library; The judgment module 15 is used to judge whether there is an intersection between the suppression reactor parameter library and the cost reduction reactor parameter library. If so, the multiple intersection reactor parameters in the intersection are sorted, and the optimal reactor parameters are analyzed and selected. If not, the optimal reactor parameters are analyzed and selected according to the optimization record data, and the reactor is selected and configured for access.

[0034] Furthermore, the fluctuation characteristic analysis module 11 is used to perform the following method: Collecting grid operation monitoring data of the target inverter connected to the grid, wherein the grid operation monitoring data includes the grid voltage accumulation data of the grid connected to the target inverter; Extracting and obtaining a plurality of fluctuating voltages in the power grid operation monitoring data according to the rated power grid voltage as a plurality of fluctuation categories; The fluctuation frequencies of the multiple fluctuation categories in the power grid operation monitoring data are extracted to obtain multiple fluctuation frequencies, and the fluctuation characteristic data are obtained by combining the multiple fluctuation categories.

[0035] Furthermore, the control bias analysis module 12 is used to perform the following method: According to the multiple fluctuation frequencies of the multiple fluctuation categories in the fluctuation characteristic data, control weight allocation is performed to obtain multiple first control weights, wherein the greater the fluctuation frequency, the greater the first control weight; According to the fluctuation voltage amplitudes of the plurality of fluctuation categories, control bias allocation is performed to obtain a plurality of second control biases, wherein the greater the fluctuation voltage amplitude, the greater the second control bias; A plurality of control biases are calculated based on the plurality of first control biases and the plurality of second control biases.

[0036] Furthermore, the first optimization module 13 is used to execute the following method: According to multiple control bias and fluctuation characteristic data, a suppression control function is constructed to optimize the reactor parameters of the high-voltage inverter connected to the reactor, as shown in the following formula: ; Among them, RES is the suppression fitness, M is the number of multiple fluctuation categories, is the control weight of the ith volatility category, is the grid fluctuation to which the target inverter is subjected in the ith fluctuation category after the reactor is connected according to the reactor parameters, is the grid fluctuation to which the target inverter is subjected under the i-th fluctuation category; Acquire reactor parameters of multiple reactors, build a reactor parameter library, randomly select a first reactor parameter in the reactor parameter library, and use it as a stage suppression optimization result; Based on the first reactor parameter, combined with the multiple fluctuation categories, simulated fluctuation suppression is performed to obtain multiple first suppressed power grid fluctuation information, and combined with the suppression control function, a first suppression fitness is calculated; Randomly selecting a second reactor parameter that is closest to the first reactor parameter again, and calculating to obtain a second suppression fitness; According to the second inhibition fitness and the first inhibition fitness, a determination is made, and a stage inhibition optimization result is updated; Continue to perform iterative optimization until convergence, output the last updated K stage suppression optimization results, and obtain the suppression reactor parameter library, where K is an integer greater than 1.

[0037] Furthermore, the first optimization module 13 is used to execute the following method: Determine whether the second suppression fitness is greater than the first suppression fitness, and if so, update the stage suppression optimization result to the second reactor parameter; If not, then obtaining a plurality of second power grid fluctuation suppression information of the second reactor parameter, calculating a plurality of suppression similarities with the plurality of first power grid fluctuation suppression information, and calculating and obtaining the similarity; According to the similarity, the update probability is calculated and allocated, wherein the smaller the similarity, the greater the update probability; According to the update probability, the phase suppression optimization result is updated to the second reactor parameter or not updated.

[0038] Furthermore, the second optimization module 14 is used to perform the following method: 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 inverter connected to the reactor, as shown in the following formula: ; COS is the cost reduction fitness, M is the number of multiple fluctuation categories, is the control weight of the ith volatility category, is the service life of the reactor parameters under the i-th fluctuation category, The preset service life; According to the cost reduction control function, the reactor parameters are optimized in the reactor parameter library to obtain the cost reduction reactor parameter library.

[0039] Furthermore, the judging module 15 is used to execute the following method: Determine whether the suppression reactor parameter library and the cost reduction reactor parameter library have an intersection; 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 orders and multiple reduction orders, and calculate to obtain multiple order information, and select the reactor parameter with the smallest order information as the optimal reactor parameter; If not, all the multiple reactor parameters in the optimization process are sorted according to the optimization record data to obtain multiple overall suppression orders and multiple overall descending orders, multiple overall order information is calculated, and the reactor parameters with the smallest overall order information are selected as the optimal reactor parameters.

[0040] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0041] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0042] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A stable control method for a high-voltage frequency converter under power grid fluctuations, characterized in that: The method comprises: Collecting grid operation monitoring data of a target frequency converter connected to a grid, and performing fluctuation characteristic analysis to obtain fluctuation characteristic data, wherein the target frequency converter is a high-voltage frequency converter; Performing control bias analysis according to the fluctuation amplitudes and frequencies of multiple fluctuation categories in the fluctuation characteristic data to obtain multiple control biases; According to multiple control bias and fluctuation characteristic data, a suppression control function is constructed to optimize the reactor parameters of the high-voltage inverter connected to the reactor, the reactor parameters are optimized, and a suppression reactor parameter library is obtained; 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 inverter connected to the reactor, and the reactor parameters are optimized to obtain a cost reduction reactor parameter library; 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 in the intersection, analyze and select to obtain the optimal reactor parameters. If not, analyze and select to obtain the optimal reactor parameters based on the optimization record data, and select and configure the reactor for access.

2. The method according to claim 1, characterized in that: Collect the grid operation monitoring data of the target inverter connected to the grid, and perform fluctuation characteristic analysis to obtain fluctuation characteristic data, including: Collecting grid operation monitoring data of the target inverter connected to the grid, wherein the grid operation monitoring data includes the grid voltage accumulation data of the grid connected to the target inverter; Extracting and obtaining a plurality of fluctuating voltages in the power grid operation monitoring data according to the rated power grid voltage as a plurality of fluctuation categories; The fluctuation frequencies of the multiple fluctuation categories in the power grid operation monitoring data are extracted to obtain multiple fluctuation frequencies, and the fluctuation characteristic data are obtained by combining the multiple fluctuation categories.

3. The method according to claim 1, characterized in that According to the fluctuation amplitudes and frequencies of multiple fluctuation categories in the fluctuation characteristic data, a control bias analysis is performed to obtain multiple control biases, including: According to the multiple fluctuation frequencies of the multiple fluctuation categories in the fluctuation characteristic data, control weight allocation is performed to obtain multiple first control weights, wherein the greater the fluctuation frequency, the greater the first control weight; According to the fluctuation voltage amplitudes of the plurality of fluctuation categories, control bias allocation is performed to obtain a plurality of second control biases, wherein the greater the fluctuation voltage amplitude, the greater the second control bias; A plurality of control biases are calculated based on the plurality of first control biases and the plurality of second control biases.

4. 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 inverter connected to the reactor, and the reactor parameters are optimized, including: According to multiple control bias and fluctuation characteristic data, a suppression control function is constructed to optimize the reactor parameters of the high-voltage inverter connected to the reactor, as shown in the following formula: ； Among them, RES is the suppression fitness, M is the number of multiple fluctuation categories, is the control weight of the ith volatility category, is the grid fluctuation to which the target inverter is subjected in the ith fluctuation category after the reactor is connected according to the reactor parameters, is the grid fluctuation to which the target inverter is subjected under the i-th fluctuation category; Acquire reactor parameters of multiple reactors, build a reactor parameter library, randomly select a first reactor parameter in the reactor parameter library, and use it as a stage suppression optimization result; Based on the first reactor parameter, combined with the multiple fluctuation categories, simulated fluctuation suppression is performed to obtain multiple first suppressed power grid fluctuation information, and combined with the suppression control function, a first suppression fitness is calculated; Randomly selecting a second reactor parameter that is closest to the first reactor parameter again, and calculating to obtain a second suppression fitness; According to the second inhibition fitness and the first inhibition fitness, a determination is made, and a stage inhibition optimization result is updated; Continue to perform iterative optimization until convergence, output the last updated K stage suppression optimization results, and obtain the suppression reactor parameter library, where K is an integer greater than 1.

5. The method according to claim 4, characterized in that The step of determining according to the second inhibition fitness and the first inhibition fitness includes: Determine whether the second suppression fitness is greater than the first suppression fitness, and if so, update the stage suppression optimization result to the second reactor parameter; If not, then obtaining a plurality of second power grid fluctuation suppression information of the second reactor parameter, calculating a plurality of suppression similarities with the plurality of first power grid fluctuation suppression information, and calculating and obtaining the similarity; According to the similarity, the update probability is calculated and allocated, wherein the smaller the similarity, the greater the update probability; According to the update probability, the phase suppression optimization result is updated to the second reactor parameter or not updated.

6. The method according to claim 4, characterized in that Based on multiple control biases and fluctuation characteristic data, a cost reduction control function is constructed to optimize the reactor parameters of the high-voltage inverter connected to the reactor, and the reactor parameters are optimized, including: 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 inverter connected to the reactor, as shown in the following formula: ； COS is the cost reduction fitness, M is the number of multiple fluctuation categories, is the control weight of the ith volatility category, is the service life of the reactor parameters under the i-th fluctuation category, The preset service life; According to the cost reduction control function, the reactor parameters are optimized in the reactor parameter library to obtain the cost reduction reactor parameter library.

7. The method according to claim 6, characterized in that Determine whether the suppression reactor parameter library and the cost reduction reactor parameter library have an intersection. If so, sort the multiple intersection reactor parameters in the intersection, analyze and select to obtain the optimal reactor parameters. If not, analyze and select to obtain the optimal reactor parameters according to the optimization record data, including: Determine whether the suppression reactor parameter library and the cost reduction reactor parameter library have an intersection; 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 orders and multiple reduction orders, and calculate to obtain multiple order information, and select the reactor parameter with the smallest order information as the optimal reactor parameter; If not, all the multiple reactor parameters in the optimization process are sorted according to the optimization record data to obtain multiple overall suppression orders and multiple overall descending orders, multiple overall order information is calculated, and the reactor parameters with the smallest overall order information are selected as the optimal reactor parameters.

8. A stable control system for a high-voltage frequency converter under power grid fluctuations, characterized in that: A method for stabilizing a high-voltage frequency converter under power grid fluctuations according to any one of claims 1 to 7 is implemented, the system comprising: A fluctuation characteristic analysis module, the fluctuation characteristic analysis module is used to collect the grid operation monitoring data of the target inverter connected to the grid, and perform fluctuation characteristic analysis to obtain fluctuation characteristic data, the target inverter is a high-voltage inverter; A control bias analysis module, the control bias analysis module is used to perform control bias analysis according to the fluctuation amplitude and frequency of multiple fluctuation categories in the fluctuation characteristic data to obtain multiple control biases; A first optimization module, wherein the first optimization module is used to construct a suppression control function for optimizing the reactor parameters of the high-voltage inverter connected to the reactor according to a plurality of control bias and fluctuation characteristic data, optimize the reactor parameters, and obtain a suppression reactor parameter library; A second optimization module, the second optimization module is used to construct a cost reduction control function for optimizing the reactor parameters of the high-voltage inverter connected to the reactor according to multiple control bias and fluctuation characteristic data, optimize the reactor parameters, and obtain a cost reduction reactor parameter library; A judgment module is used to judge whether there is an intersection between the suppression reactor parameter library and the cost reduction reactor parameter library. If so, multiple intersection reactor parameters in the intersection are sorted, and the optimal reactor parameters are selected and analyzed. If not, the optimal reactor parameters are analyzed and selected according to the optimization record data, and the reactor is selected and configured for access.

Images