Adaptive dynamic adjustment method applied to wind power generation converter

Abstract

This invention discloses a dynamic adjustment method for the adaptability of wind power converters, relating to the field of wind power grid-connected control technology. The invention obtains an operating parameter set by real-time acquisition of multi-dimensional operating parameters of the converter; analyzes the operating parameter set to pinpoint the time range where the converter exhibits anomalies; constructs a dynamic adaptation unit group to dynamically adapt parameters for converters within the abnormal time range; sets an operating status evaluation window, which uses a parallel status monitoring mechanism to determine whether the converter's operating status is stable; performs feature matching on converters in unstable states to obtain corresponding operating condition details; and executes corresponding parameter adaptation strategies based on the operating condition details of converters in unstable states.

Description

Technical Field

[0001] This invention relates to the field of wind power grid connection control technology, specifically to a dynamic adjustment method for the adaptability of wind power converters. Background Technology

[0002] As wind power generation continues to increase its share in the power system, higher requirements are placed on the operating performance of wind power converters. Wind power generation faces complex operating conditions such as random fluctuations in wind speed, variable turbulence intensity, wide range of grid impedance variations, and thermal cycling stress of power modules. Traditional converter control methods typically employ fixed parameter control strategies, which cannot be adapted and adjusted in real time according to wind conditions, grid conditions, and device thermal status. This results in significant power generation efficiency loss under strong turbulent wind conditions, oscillations and grid disconnection under weak grid conditions, and accelerated aging or even failure of power modules due to overheating during instantaneous overload. Therefore, there is an urgent need for a solution that can dynamically sense multi-dimensional operating status and adaptively adjust converter control parameters to enhance grid connection stability and extend equipment life. To this end, we now provide an adaptive dynamic adjustment method for wind power converters. Summary of the Invention

[0003] The purpose of this invention is to provide a dynamic adjustment method for the adaptability of wind power converters.

[0004] The objective of this invention can be achieved through the following technical solution: a dynamic adjustment method for the adaptability of wind power converters, comprising the following steps: Step S1: Real-time acquisition of multi-dimensional operating parameters of the converter to obtain the operating parameter set; Step S2: Analyze the operating parameter set of the converter to pinpoint the time period in which the converter is abnormal; Step S3: Construct a dynamic adaptation unit group, and use the dynamic adaptation unit group to dynamically adapt the parameters of the converter in the abnormal time period range; Step S4: Use a parallel state monitoring mechanism to determine whether the converter is in a stable state, perform feature matching on the converter in an unstable state, and then obtain the corresponding operating condition details features; Step S5: Execute the corresponding parameter adaptation strategy based on the operating condition details of the converter in an unstable state.

[0005] Furthermore, the process of acquiring multi-dimensional operating parameters of the converter in real time to obtain the operating parameter set includes: The operating period of the converter is divided into light load period, heavy load period and overload period according to the load characteristics. The multi-dimensional operating parameters of the converter in different time periods are collected and the corresponding sub-sampling windows are set. Within each sub-sampling window, multi-dimensional operating parameters of the converter are collected under light load, heavy load, and overload conditions, and labeled with light load identification code, heavy load identification code, and overload identification code respectively. All multi-dimensional operating parameters allocated to the light-load identity code, heavy-load identity code, and overload identity code are integrated and distributed to obtain the first parameter subset, the second parameter subset, and the third parameter subset; By integrating the first parameter subset, the second parameter subset, and the third parameter subset corresponding to each converter, the operating parameter set corresponding to each converter can be obtained.

[0006] Furthermore, the process of analyzing the converter's operating parameter set to pinpoint the time period in which the converter exhibits anomalies includes: Set a parameter monitoring period. During the parameter monitoring period, each multi-dimensional operating parameter of the converter corresponding to the first parameter subset is used as the first monitoring sample, and the total number of the first monitoring samples is obtained. Acquire various operating parameters for each first monitoring sample, including operating voltage, operating current, and operating power; The time period range corresponding to the currently parsed multidimensional operational parameter is marked as abnormal if at least one of the following conditions is met: The operating voltage is not within the preset safe voltage range; The operating current is not within the preset safe current range; The operating power is not within the preset safe power range; Then, the time ranges in which the converter has all abnormalities during the light load period are located, and the number of these abnormal time ranges is recorded as the number of light load abnormalities. Similarly, each multi-dimensional operating parameter of the converter corresponding to the second and third parameter subsets is used as the second and third monitoring samples, respectively. The total number of the second and third monitoring samples is obtained, and various operating parameters of each second and third monitoring sample are judged to locate the time range in which the converter has all abnormalities during the heavy load and overload periods. The number of these abnormal time ranges is recorded as the number of heavy load abnormalities and the number of overload abnormalities, respectively.

[0007] Furthermore, the process of constructing dynamic adaptation unit groups includes: Create adaptation correction units. The total number of adaptation correction units created is equal to the sum of the number of light load anomalies, the number of heavy load anomalies, and the number of overload anomalies. Create adaptation monitoring units. The total number of adaptation monitoring units created is equal to the sum of the number of normal time ranges during light load periods, the number of normal time ranges during heavy load periods, and the number of normal time ranges during overload periods. For light load periods, heavy load periods, and overload periods, a corresponding number of adaptation correction units are configured based on the number of light load anomalies, heavy load anomalies, and overload anomalies obtained. At the same time, a corresponding number of adaptation monitoring units are configured based on the number of normal periods within the corresponding time period. These are then combined in sequence to form dynamic adaptation unit groups, and the current dynamic adaptation unit groups are marked as the first unit group, the second unit group, and the third unit group, respectively.

[0008] Furthermore, the process of dynamically adapting parameters for converters within abnormal time periods using dynamic adaptation unit groups includes: The first unit group adapts the operating voltage, operating current and operating power of the converter in the abnormal time range during the light load period, thereby adjusting the operating voltage, operating current and operating power of all the abnormal time ranges in the light load period to their respective safe ranges. The first unit group monitors the time range during which there are no abnormalities in the light load period of the converter. When the fluctuation of operating voltage, operating current and operating power in the time range is found to exceed the preset fluctuation value limit, an adaptation correction unit is created to replace the corresponding adaptation monitoring unit for the corresponding time range. Similarly, using the method described above for handling the abnormal time range of the converter during the light load period, the second and third unit groups respectively adjust the operating voltage, operating current, and operating power during the heavy load and overload periods, respectively.

[0009] Furthermore, the process of using a parallel state monitoring mechanism to determine whether the converter's operating state is in a stable state includes: Set up an operation status evaluation window, which consists of several temporary sliding sub-windows. Each temporary sliding sub-window is used to monitor the operation status of a converter. Set the number of synchronizations for the parallel status monitoring mechanism in the operation status evaluation window, and allocate temporary sliding sub-windows to the corresponding number of converters according to the number of synchronizations. Each temporary sliding sub-window is set with a corresponding sliding step size, status judgment rule base and window duration. Before the window duration ends, the temporary sliding sub-window continues to monitor and judge the operating status of the converter. The operating status of the converter includes stable and unstable states. According to the state judgment rule library corresponding to each converter, the time period state evaluation coefficient of the converter in light load period, heavy load period and overload period is obtained. The status determination rule base records the time period status evaluation coefficients of the corresponding converters and their corresponding determination thresholds. Based on the numerical relationship between the time period status evaluation coefficients and the corresponding determination thresholds, the time period status of the converters during light load periods, heavy load periods, and overload periods is determined. The time period status includes normal time period and abnormal time period. If the converter exhibits an abnormal state during any of the light-load, heavy-load, or overload periods, the corresponding converter is determined to be in an unstable state; otherwise, it is determined to be in a stable state.

[0010] Furthermore, the process of performing feature matching on the converter in an unstable state to obtain the corresponding detailed operating condition features includes: Collect historical operating parameter sets of converter operating records that are determined to be in an unstable state, and construct an operating condition detail information database. The operating condition detail information database is used to store converter detail information in an unstable state, wherein each converter detail information is associated with the corresponding operating condition detail features of the converter. The set of operating parameters corresponding to the converter in an unstable state is input into the operating condition details information database. Feature matching is performed between the set of operating parameters and the converter details information to obtain the similarity between the set of operating parameters and the converter details information. The converter details information with the highest similarity is selected, and the operating condition details feature corresponding to the converter details information at this time is obtained as the operating condition details feature of the converter in the current unstable state.

[0011] Furthermore, the process of executing corresponding parameter adaptation strategies based on the detailed operating characteristics of the converter in an unstable state includes: A parameter adaptation strategy library is constructed, which stores parameter adaptation strategies corresponding to different operating condition details. The operating condition details of the converter in an unstable state are input into the parameter adaptation strategy library, and then the parameter adaptation strategies for fault handling and fault prevention of the converter in an unstable state are matched from the parameter adaptation strategy library. Relevant personnel are then assigned to execute the parameter adaptation strategies.

[0012] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention obtains a set of operating parameters by real-time acquisition of multi-dimensional operating parameters of the converter, achieving comprehensive perception and accurate data acquisition of the converter's operating status, providing a reliable data foundation for subsequent anomaly diagnosis and parameter adaptation; parsing the converter's operating parameter set allows for the location of the time period in which the converter exhibits anomalies, enabling rapid and accurate identification of abnormal operating segments, improving the timeliness and accuracy of fault diagnosis, reducing the duration of abnormal states, and lowering the risk of equipment damage; constructing a dynamic adaptation unit group allows for dynamic parameter adaptation of converters within the abnormal time period, achieving differentiated parameter adjustments for different anomaly types, and improving the targeting and effectiveness of parameter adaptation. Effectiveness is ensured to maintain stable operation of the converter. An operational status assessment window is set up, which uses a parallel status monitoring mechanism to determine whether the converter is in a stable state. For converters in an unstable state, feature matching is performed to obtain corresponding detailed operating condition features. This achieves real-time, parallel monitoring of the converter's operational status, significantly improving the efficiency and accuracy of status judgment. It also provides a scientific basis and reference standard for parameter adaptation, improving the rationality and reliability of parameter adjustments. Based on the detailed operating condition features of converters in an unstable state, corresponding parameter adaptation strategies are executed, realizing intelligent parameter adjustment. This effectively improves the converter's adaptability and operational stability under complex operating conditions, extends equipment lifespan, and reduces maintenance costs. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0014] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0015] like Figure 1 As shown, the adaptive dynamic adjustment method applied to wind power converters includes the following steps: Step S1: Real-time acquisition of multi-dimensional operating parameters of the converter to obtain the operating parameter set; Step S2: Analyze the operating parameter set of the converter to pinpoint the time period in which the converter is abnormal; Step S3: Construct a dynamic adaptation unit group, and use the dynamic adaptation unit group to dynamically adapt the parameters of the converter in the abnormal time period range; Step S4: Use a parallel state monitoring mechanism to determine whether the converter is in a stable state, perform feature matching on the converter in an unstable state, and then obtain the corresponding operating condition details features; Step S5: Execute the corresponding parameter adaptation strategy based on the operating condition details of the converter in an unstable state.

[0016] It should be further explained that, in the specific implementation process, the process of acquiring multi-dimensional operating parameters of the converter in real time to obtain the operating parameter set includes: The operating period of the converter is divided into light load period, heavy load period and overload period according to the load characteristics, and the multi-dimensional operating parameters of the converter are collected in different time periods. Set up sub-sampling windows for light load periods, heavy load periods, and overload periods, and then divide the light load period into several light load sub-sampling windows, the heavy load period into several heavy load sub-sampling windows, and the overload period into several overload sub-sampling windows. Within each sub-sampling window, multi-dimensional operating parameters of the converter are collected under light load, heavy load, and overload conditions, and labeled with light load identification code, heavy load identification code, and overload identification code respectively. Integrate all the multi-dimensional operating parameters allocated to the lightweight identity code to generate the first parameter subset; Integrate all the multi-dimensional operating parameters allocated to the overloaded identity code to generate a second parameter subset; Integrate all multi-dimensional operational parameters allocated to the overloaded identity code to generate a third parameter subset; By integrating the first parameter subset, the second parameter subset, and the third parameter subset corresponding to each converter, the operating parameter set corresponding to each converter can be obtained.

[0017] It should be further explained that, in the specific implementation process, the process of analyzing the converter's operating parameter set and then locating the time range in which the converter is experiencing anomalies includes: Set a parameter monitoring period. Within this period, each multi-dimensional operating parameter corresponding to the first parameter subset of the converter is used as the first monitoring sample. The total number of the first monitoring samples is then obtained and denoted as [missing information]. ; Acquire various operating parameters for each first monitoring sample, including operating voltage, operating current, and operating power; The time period range corresponding to the currently parsed multidimensional operational parameter is marked as abnormal if at least one of the following conditions is met: The operating voltage is not within the preset safe voltage range; The operating current is not within the preset safe current range; The operating power is not within the preset safe power range; This allows us to pinpoint the time periods during which the converter exhibits anomalies, and the number of these anomaly time periods is recorded as the number of light-load anomalies. ; Similarly, each multidimensional operating parameter corresponding to the second and third parameter subsets of the converter is used as the second and third monitoring samples, respectively, and the total number of the second and third monitoring samples is obtained, denoted as […]. and The system acquires various operating parameters from each second and third monitoring sample for analysis, thereby pinpointing the time periods during which the converter exhibits anomalies under both heavy load and overload conditions. The number of these abnormal time periods is recorded as the number of heavy load anomalies and the number of overload anomalies, respectively. and .

[0018] It should be further explained that, in the specific implementation process, the process of constructing a dynamic adaptation unit group and using the dynamic adaptation unit group to dynamically adapt the parameters of the converter within the abnormal time period includes: Create adaptation correction units. The total number of adaptation correction units created is equal to the sum of the number of light load anomalies, the number of heavy load anomalies, and the number of overload anomalies. Create adaptation monitoring units. The total number of adaptation monitoring units created is equal to the sum of the number of normal time ranges during light load periods, the number of normal time ranges during heavy load periods, and the number of normal time ranges during overload periods. Wherein, the number of normal time periods within the light load period is equal to the total number of the first monitoring samples minus the number of light load anomalies, the number of normal time periods within the heavy load period is equal to the total number of the second monitoring samples minus the number of heavy load anomalies, and the number of normal time periods within the overload period is equal to the total number of the third monitoring samples minus the number of overload anomalies. For light load periods, a corresponding number of adaptation correction units are configured based on the number of light load anomalies obtained. At the same time, a corresponding number of adaptation monitoring units are configured based on the number of normal periods within the light load period. These units are then combined in sequence to form a dynamic adaptation unit group, and the current dynamic adaptation unit group is marked as the first unit group. For heavy load periods, a corresponding number of adaptation correction units are configured based on the number of heavy load anomalies obtained. At the same time, a corresponding number of adaptation monitoring units are configured based on the number of normal time periods within the heavy load period. These units are then combined in sequence to form a dynamic adaptation unit group, and the current dynamic adaptation unit group is marked as the second unit group. For overload periods, a corresponding number of adaptation correction units are configured based on the number of overload anomalies obtained. At the same time, a corresponding number of adaptation monitoring units are configured based on the number of normal periods within the overload period. These units are then combined in sequence to form a dynamic adaptation unit group, and the current dynamic adaptation unit group is marked as the third unit group. The first unit group adapts the operating voltage, operating current and operating power of the converter in the abnormal time range during the light load period, thereby adjusting the operating voltage, operating current and operating power of all the abnormal time ranges in the light load period to their respective safe ranges. The first unit group monitors the time range during which there are no abnormalities in the light load period of the converter. When the fluctuation of operating voltage, operating current and operating power in the time range is found to exceed the preset fluctuation value limit, an adaptation correction unit is created to replace the corresponding adaptation monitoring unit for the corresponding time range. Similarly, using the method described above for handling the abnormal time range of the converter during the light load period, the second and third unit groups respectively adjust the operating voltage, operating current, and operating power during the heavy load and overload periods, respectively.

[0019] It should be further explained that, in the specific implementation process, the process of using a parallel state monitoring mechanism to determine whether the converter's operating state is in a stable state includes: Set up an operation status evaluation window, which consists of several temporary sliding sub-windows. These temporary sliding sub-windows are numbered and denoted as i, where i = 1, 2, 3, ..., n, and n is a natural number greater than 0. Each temporary sliding sub-window is used to monitor the operation status of one converter. The number of synchronizations for the parallel status monitoring mechanism is set by the operation status evaluation window. Temporary sliding sub-windows are allocated to the corresponding number of converters according to the number of synchronizations. Each temporary sliding sub-window is set with a corresponding sliding step size, status judgment rule base and window duration. The sliding step size is used to smooth the set of operating parameters of the monitored converter after dynamic parameter adaptation, so as to reduce the impact of random fluctuations. The sliding step size is inversely proportional to the monitoring frequency. Before the window duration ends, the temporary sliding sub-window continues to monitor and judge the operating status of the converter. The operating status of the converter includes stable and unstable states. According to the state judgment rule library corresponding to each converter, the time period state evaluation coefficient of the converter in light load period, heavy load period and overload period is obtained. The time-condition evaluation coefficient of the converter during the light-load period is denoted as... ,Right now: ; The time-period state evaluation coefficient of the converter during the heavy load period is denoted as... ,Right now: ; The time-period state evaluation coefficient of the converter during the overload period is denoted as... ,Right now: ; The state determination rule base records the time-period state evaluation coefficients of the corresponding converters and their respective determination thresholds, which are denoted as follows: , , ; according to and , and , and The numerical relationship is used to determine the state of the converter in each time period; when If a light-load period is deemed normal, then the period is deemed abnormal. when If the overloaded period is deemed normal, then the period is deemed abnormal. when If the overload period is determined to be normal, then the period is determined to be abnormal. If the converter exhibits an abnormal state during any of the light-load, heavy-load, or overload periods, the corresponding converter is determined to be in an unstable state; otherwise, it is determined to be in a stable state.

[0020] It should be further explained that, in the specific implementation process, the process of performing feature matching on the converter in an unstable state to obtain the corresponding operating condition details includes: Collect historical operating parameter sets of converter operating records that are determined to be in an unstable state, and construct an operating condition detail information database. The operating condition detail information database is used to store converter detail information in an unstable state. Each converter detail information is associated with the corresponding operating condition detail features of the converter. The operating condition detail features are used to describe the various operating parameters corresponding to the converter when it is currently in an unstable state. The various operating parameters include all abnormal fluctuation periods, the fluctuation voltage, fluctuation current and fluctuation power of the converter during each fluctuation period, and the fluctuation duration of each fluctuation voltage, fluctuation current and fluctuation power. The set of operating parameters corresponding to the converter in an unstable state is input into the operating condition details information database. Feature matching is performed between the set of operating parameters and the converter details information to obtain the similarity between the set of operating parameters and the converter details information. The converter details information with the highest similarity is selected, and the operating condition details feature corresponding to the converter details information at this time is obtained as the operating condition details feature of the converter in the current unstable state.

[0021] It should be further explained that, in the specific implementation process, the process of executing the corresponding parameter adaptation strategy based on the detailed operating conditions of the converter in an unstable state includes: A parameter adaptation strategy library is constructed, which stores parameter adaptation strategies corresponding to different operating condition details. The operating condition details of the converter in an unstable state are input into the parameter adaptation strategy library, and then the parameter adaptation strategies for fault handling and fault prevention of the converter in an unstable state are matched from the parameter adaptation strategy library. Relevant personnel are then assigned to execute the parameter adaptation strategies. By implementing parameter adaptation strategies for unstable converters, faults can be resolved promptly when they occur, and preventative measures can be taken when a fault is imminent, effectively extending the converter's service life and improving its operating efficiency and safety.

[0022] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications or equivalent substitutions made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for dynamic adaptation adjustment of wind power converters, characterized in that, Includes the following steps: Step S1: Real-time acquisition of multi-dimensional operating parameters of the converter to obtain the operating parameter set; Step S2: Analyze the operating parameter set of the converter to pinpoint the time period in which the converter is abnormal; Step S3: Construct a dynamic adaptation unit group, and use the dynamic adaptation unit group to dynamically adapt the parameters of the converter in the abnormal time period range; Step S4: Use a parallel state monitoring mechanism to determine whether the converter is in a stable state, perform feature matching on the converter in an unstable state, and then obtain the corresponding operating condition details features; Step S5: Execute the corresponding parameter adaptation strategy based on the operating condition details of the converter in an unstable state.

2. The adaptive dynamic adjustment method for wind power converters according to claim 1, characterized in that, The process of acquiring multi-dimensional operating parameters of the converter in real time and obtaining the operating parameter set includes: The operating period of the converter is divided into light load period, heavy load period and overload period according to the load characteristics. The multi-dimensional operating parameters of the converter in different time periods are collected and the corresponding sub-sampling windows are set. Within each sub-sampling window, multi-dimensional operating parameters of the converter are collected under light load, heavy load, and overload conditions, and labeled with light load identification code, heavy load identification code, and overload identification code respectively. All multi-dimensional operating parameters allocated to the light-load identity code, heavy-load identity code, and overload identity code are integrated and distributed to obtain the first parameter subset, the second parameter subset, and the third parameter subset; By integrating the first parameter subset, the second parameter subset, and the third parameter subset corresponding to each converter, the operating parameter set corresponding to each converter can be obtained.

3. The adaptive dynamic adjustment method for wind power converters according to claim 2, characterized in that, The process of analyzing the converter's operating parameter set to pinpoint the time period in which the converter is experiencing an anomaly includes: Set a parameter monitoring period. During the parameter monitoring period, each multi-dimensional operating parameter of the converter corresponding to the first parameter subset is used as the first monitoring sample, and the total number of the first monitoring samples is obtained. Acquire various operating parameters for each first monitoring sample, including operating voltage, operating current, and operating power; The time period range corresponding to the currently parsed multidimensional operational parameter is marked as abnormal if at least one of the following conditions is met: The operating voltage is not within the preset safe voltage range; The operating current is not within the preset safe current range; The operating power is not within the preset safe power range; Then, the time ranges in which the converter has all abnormalities during the light load period are located, and the number of these abnormal time ranges is recorded as the number of light load abnormalities. Similarly, each multi-dimensional operating parameter of the converter corresponding to the second and third parameter subsets is used as the second and third monitoring samples, respectively. The total number of the second and third monitoring samples is obtained, and various operating parameters of each second and third monitoring sample are judged to locate the time range in which the converter has all abnormalities during the heavy load and overload periods. The number of these abnormal time ranges is recorded as the number of heavy load abnormalities and the number of overload abnormalities, respectively.

4. The adaptive dynamic adjustment method for wind power converters according to claim 3, characterized in that, The process of building a dynamic adapting unit group includes: Create adaptation correction units. The total number of adaptation correction units created is equal to the sum of the number of light load anomalies, the number of heavy load anomalies, and the number of overload anomalies. Create adaptation monitoring units. The total number of adaptation monitoring units created is equal to the sum of the number of normal time periods during light load periods, the number of normal time periods during heavy load periods, and the number of normal time periods during overload periods. For light load periods, heavy load periods, and overload periods, a corresponding number of adaptation correction units are configured based on the number of light load anomalies, heavy load anomalies, and overload anomalies obtained. At the same time, a corresponding number of adaptation monitoring units are configured based on the number of normal periods within the corresponding time period. These are then combined in sequence to form dynamic adaptation unit groups, and the current dynamic adaptation unit groups are marked as the first unit group, the second unit group, and the third unit group, respectively.

5. The adaptive dynamic adjustment method for wind power converters according to claim 4, characterized in that, The process of dynamically adapting parameters for converters within an abnormal time period using dynamic adaptation unit groups includes: The first unit group adapts the operating voltage, operating current and operating power of the converter in the abnormal time range during the light load period, thereby adjusting the operating voltage, operating current and operating power of all the abnormal time ranges in the light load period to their respective safe ranges. The first unit group monitors the time range during which there are no abnormalities in the light load period of the converter. When the fluctuation of operating voltage, operating current and operating power in the time range is found to exceed the preset fluctuation value limit, an adaptation correction unit is created to replace the corresponding adaptation monitoring unit for the corresponding time range. Similarly, using the method described above for handling the abnormal time range of the converter during the light load period, the second and third unit groups respectively adjust the operating voltage, operating current, and operating power during the heavy load and overload periods, respectively.

6. The adaptive dynamic adjustment method for wind power converters according to claim 5, characterized in that, The process of determining whether the converter's operating state is in a stable state using a parallel state monitoring mechanism includes: Set up an operation status evaluation window, which consists of several temporary sliding sub-windows. Each temporary sliding sub-window is used to monitor the operation status of a converter. Set the number of synchronizations for the parallel status monitoring mechanism in the operation status evaluation window, and allocate temporary sliding sub-windows to the corresponding number of converters according to the number of synchronizations. Each temporary sliding sub-window is set with a corresponding sliding step size, status judgment rule base and window duration. Before the window duration ends, the temporary sliding sub-window continues to monitor and judge the operating status of the converter. The operating status of the converter includes stable and unstable states. According to the state judgment rule library corresponding to each converter, the time period state evaluation coefficient of the converter in light load period, heavy load period and overload period is obtained. The status determination rule base records the time period status evaluation coefficients of the corresponding converters and their corresponding determination thresholds. Based on the numerical relationship between the time period status evaluation coefficients and the corresponding determination thresholds, the time period status of the converters during light load periods, heavy load periods, and overload periods is determined. The time period status includes normal time period and abnormal time period. If the converter exhibits an abnormal state during any of the light-load, heavy-load, or overload periods, the corresponding converter is determined to be in an unstable state; otherwise, it is determined to be in a stable state.

7. The adaptive dynamic adjustment method for wind power converters according to claim 6, characterized in that, The process of performing feature matching on a converter in an unstable state to obtain corresponding detailed operating condition features includes: Collect historical operating parameter sets of converter operating records that are determined to be in an unstable state, and construct an operating condition detail information database. The operating condition detail information database is used to store converter detail information in an unstable state, wherein each converter detail information is associated with the corresponding operating condition detail features of the converter. The set of operating parameters corresponding to the converter in an unstable state is input into the operating condition details information database. Feature matching is performed between the set of operating parameters and the converter details information to obtain the similarity between the set of operating parameters and the converter details information. The converter details information with the highest similarity is selected, and the operating condition details feature corresponding to the converter details information at this time is obtained as the operating condition details feature of the converter in the current unstable state.

8. The adaptive dynamic adjustment method for wind power converters according to claim 7, characterized in that, The process of executing the corresponding parameter adaptation strategy based on the detailed operating conditions of the converter in an unstable state includes: A parameter adaptation strategy library is constructed, which stores parameter adaptation strategies corresponding to different operating condition details. The operating condition details of the converter in an unstable state are input into the parameter adaptation strategy library, and then the parameter adaptation strategies for fault handling and fault prevention of the converter in an unstable state are matched from the parameter adaptation strategy library. Relevant personnel are then assigned to execute the parameter adaptation strategies.

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