Adaptive parameter optimization control method and system for frequency converters under voltage sag

Abstract

This invention discloses an adaptive parameter optimization control method and system for frequency converters under voltage sags, relating to the field of frequency converter parameter optimization control technology. It solves the technical problems in existing technologies where it is impossible to simulate voltage sag environments for frequency converters, thus hindering targeted parameter control and increasing the impact of voltage sags. Specifically, it includes a voltage sag prediction unit that predicts voltage sags in the operating environment of the frequency converter; a voltage sag parameter analysis unit that analyzes the impact of voltage sags on frequency converter parameters and sets control strategies for frequency converter parameters based on the analysis; and an optimization identification control unit that simulates voltage sag environments for the frequency converter and infers the impact of voltage sag parameters on the frequency converter during operation based on the simulation.

Description

Technical Field

[0001] This invention relates to the field of inverter parameter optimization control technology, specifically to an adaptive parameter optimization control method and system for inverters under voltage sag. Background Technology

[0002] Inverters are the "speed control core" in industrial automation and energy conservation. Their core value lies in achieving precise speed control by changing the power supply frequency of the motor, while significantly reducing energy consumption. Whether it is motor control in the production line or energy-saving operation of building air conditioning, it is inseparable from its "intelligent regulation". Voltage sag, also known as voltage dip, refers to the phenomenon that the voltage in the power supply system suddenly drops to 10%-90% of the rated value, and the duration is usually between 0.5 cycles (about 10ms) and 1 minute, after which it returns to normal.

[0003] However, in the existing technology, frequency converters cannot predict sags based on environmental analysis during operation, nor can they be targeted for control based on the impact analysis of frequency converter parameters, which reduces the operating efficiency of the frequency converter. In addition, it is impossible to simulate sag environments for the frequency converter and to control parameters in a targeted manner, which increases the impact of sags.

[0004] To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0005] The purpose of this invention is to solve the problems mentioned above by proposing an adaptive parameter optimization control method and system for frequency converters under voltage sag.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] An adaptive parameter optimization control system for a frequency converter under voltage sags, including an optimization control platform, wherein the optimization control platform is connected to:

[0008] The voltage sag prediction unit predicts voltage sags in the operating environment of the frequency converter.

[0009] The voltage sag parameter analysis unit performs an impact analysis on the inverter parameters during voltage sags and sets control strategies for inverter parameters based on the impact analysis.

[0010] The control unit is optimized, and a sag environment simulation is performed on the frequency converter. Based on the sag environment simulation, the impact of sag parameters on the frequency converter during operation is inferred.

[0011] In a preferred embodiment of the present invention, the process of temporarily generating the prediction unit is as follows:

[0012] Monitor the wires within the control range of the frequency converter and the external environment of the wires;

[0013] During the operation of the power line, the time of lightning in the external environment is obtained, and the lightning interference generated at the corresponding time is marked as power interference; at the same time, the force interference generated by the external environment is obtained; according to the monitoring of the sudden supply parameter fluctuation of the equipment connected to the power line, if the sudden supply parameter span is set to a parameter span threshold, the current sudden supply parameter fluctuation is marked as fault interference.

[0014] An operational timeline is constructed based on the operating periods of the power lines, and the times of power interference, force interference, and fault interference are displayed on the operational timeline.

[0015] As a preferred embodiment of the present invention, a temporary dip prediction is performed on the existing runtime timeline:

[0016] The cumulative duration corresponding to the overlapping occurrence of multiple types of interference moments in the running timeline is collected. At the same time, the floating trend of the interval duration of a single occurrence of the same type of interference moment and the floating trend of the interval duration of the synchronous occurrence of multiple types of interference moments are collected.

[0017] Specifically, based on the fluctuation trend of the interval duration of a single occurrence of the same type of interference and the fluctuation trend of the interval duration of simultaneous occurrence of multiple types of interference, the shortening span of the interval duration of a single occurrence and the shortening span of the interval duration of simultaneous occurrence are obtained. Under all operating scenarios, the highest value of the shortening span is used as the collected data.

[0018] In a preferred embodiment of the present invention, a threshold comparison is performed on the cumulative duration corresponding to the overlapping occurrence times of multiple types of interference and the highest shortening span within the running timeline:

[0019] If the cumulative duration corresponding to the overlapping occurrence of multiple types of interference exceeds the cumulative duration threshold, or if the highest shortening span within the operating timeline exceeds the highest shortening span threshold, a sag prediction signal is generated and sent to the optimization control platform. The optimization control platform monitors the frequency of interference occurrences within the operating timeline. If the frequency exceeds the red line value in the historical operation process, a sag warning is issued, and the frequency modulator parameters are controlled. If the cumulative duration corresponding to the overlapping occurrence of multiple types of interference does not exceed the cumulative duration threshold, and the highest shortening span within the operating timeline does not exceed the highest shortening span threshold, no sag prediction signal is generated and no sag prediction signal is sent to the optimization control platform.

[0020] In a preferred embodiment of the present invention, the process of the temporary descent parameter analysis unit is as follows:

[0021] The maximum deviation of the peak value of the DC bus voltage drop span before and after the span fluctuates in the fixed span voltage sag scenario is obtained. At the same time, the time when the torque compensation increases the output current and the time when the output current needs to be compensated in the current period are obtained before and after the time fluctuates in the fixed duration voltage sag scenario. Based on the compensation time and the time when compensation is needed, the compensation frequency of the inverter is increased by the span.

[0022] In a preferred embodiment of the present invention, the maximum deviation value of the peak value of the DC bus voltage drop span and the frequency increase span of the inverter are compared with the maximum deviation threshold and the frequency increase span threshold, respectively:

[0023] If the maximum deviation of the peak value of the DC bus voltage drop exceeds the maximum deviation threshold, or the frequency increase span of the inverter exceeds the frequency increase span threshold, a scenario-specific control signal is generated and sent to the optimization control platform; if the maximum deviation of the peak value of the DC bus voltage drop does not exceed the maximum deviation threshold, and the frequency increase span of the inverter does not exceed the frequency increase span threshold, a single-scenario control signal is generated and sent to the optimization control platform.

[0024] As a preferred embodiment of the present invention, the process of optimizing the identification control unit is as follows:

[0025] The magnitude and duration of the voltage sag are labeled as causative data;

[0026] Furthermore, the power transmission floating marker is used as the influencing data, and the load condition is set to simulate the operating intensity of the environment, namely low load, rated load, and overload; low load is represented as 30% below the rated load;

[0027] Ensure proper grounding and no short-circuit hazards. Start the programmable AC power supply, set it to normal voltage output, and start the frequency converter and load to ensure the system operates stably under the set load conditions. Prioritize single-float fluctuations for the causative data, with a uniform fluctuation range.

[0028] In a preferred embodiment of the present invention, when arbitrary fluctuations occur in the causal data, the deviation between the real-time power delivery fluctuation and the set delivery fluctuation is collected and marked as a supply deviation. As the simulation operation time extends, the supply deviations at multiple moments are analyzed by averaging the values, that is, the average supply deviation is compared with the corresponding deviation red line threshold. When the interval value continues to shorten, the corresponding causal data and influence data are set to an inverse proportional relationship. When the influence data fluctuates, the relevant parameters of the power line operation and the relevant parameters of the frequency converter are obtained according to the data characteristics of the influence data. That is, the fluctuation of the values ​​of the relevant parameters of the power line operation and the relevant parameters of the frequency converter can change the value of the influence data, or the change of the influence value can affect the relevant parameters of the power line operation and the relevant parameters of the frequency converter; and these are marked as passive influence parameters and active influence parameters, respectively.

[0029] As a preferred embodiment of the present invention, when the interval value continues to shorten, active influence-related parameters are recorded, and all types of active influence-related parameters are used to construct an influence data set. The influence of the fluctuation of the influence data in the current time period is calculated based on the dispersion of the subsets within the set and the variance. Specifically, it can be divided into a high-influence stage and a low-influence stage.

[0030] The floating trend statistics of parameters related to passive influence are performed. When the causal data is fluctuating, if it is in a high-impact stage, the current floating trend is set as an unfavorable floating trend, the floating trend of the current parameters related to passive influence is set as an abnormal trend, and the corresponding opposite trend is marked as an optimized control trend.

[0031] If the current fluctuation trend is in a low-impact phase, it is set as a favorable fluctuation trend, and the fluctuation trend of the current passively affected parameters is set as a normal trend. The corresponding opposite trend is marked as an optimized control trend. All types of passively affected and actively affected parameters are sent to the optimization control platform, and the fluctuation trend type is noted according to each type of parameter. That is, when there is no voltage sag, the unfavorable fluctuation trend of each type of actively affected parameter is used as the monitoring direction, and the optimized control trend of the passively affected parameter is set as the numerical control reference standard. When there is a voltage sag, the favorable fluctuation trend of each type of actively affected parameter is used as the monitoring direction, and the optimized control trend of the passively affected parameter is set as the numerical control reference standard.

[0032] As a preferred embodiment of the present invention, the adaptive parameter optimization control method for a frequency converter under voltage sag includes the following steps:

[0033] Step 1: Voltage sag prediction. Predict voltage sags in the operating environment of the frequency converter.

[0034] Step 2: Voltage sag parameter analysis, analyzing the impact of voltage sag on inverter parameters;

[0035] Step 3: Optimize identification and control, simulate sag environment for the frequency converter, and infer the impact of sag parameters on the frequency converter during operation based on the sag environment simulation.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] 1. In this invention, voltage sag prediction is performed on the operating environment of the frequency converter. By analyzing the operating environment of the frequency converter, it is inferred whether there is a risk of voltage sag, so as to optimize the parameters in a timely manner and avoid data regulation start-up delay during parameter optimization control, which would cause voltage sag to affect the operating efficiency of the frequency converter and reduce the operating stability of power operations.

[0038] 2. In this invention, the influence analysis of inverter parameters during voltage sag is performed. Through the influence analysis, targeted prevention and control can be carried out according to the real-time sag type, so as to avoid the inverter parameters not being adjusted in time after different types of voltage sag occur, which would directly affect the operation of the inverter and reduce the stability of the entire power supply.

[0039] 3. In this invention, a sag environment simulation is performed on the frequency converter to infer the impact of sag parameters on the frequency converter during operation based on the sag environment simulation. This allows for targeted parameter control after identifying downtime risks, thereby reducing the impact of sags and improving the operating efficiency of the frequency converter. Attached Figure Description

[0040] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0041] Figure 1 This is a system principle block diagram of the present invention;

[0042] Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0043] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0045] Please see Figure 1 As shown, the adaptive parameter optimization control system for the frequency converter under voltage sag includes an optimization control platform, which is connected to a sag generation prediction unit, a sag parameter analysis unit, and an optimization identification control unit.

[0046] The optimized control platform generates a descent prediction signal and sends it to the descent prediction unit.

[0047] After receiving the voltage sag prediction signal, the voltage sag prediction unit performs voltage sag prediction on the operating environment of the frequency converter. By analyzing the operating environment of the frequency converter, it infers whether there is a risk of voltage sag and performs timely parameter optimization control. This avoids data regulation start-up delay during parameter optimization control, which could cause voltage sag to affect the operating efficiency of the frequency converter and reduce the operational stability of power operations.

[0048] Monitor the wires within the control range of the frequency converter and the external environment of the wires;

[0049] During the operation of the power line, the time of lightning in the external environment is obtained, and the lightning interference generated at the corresponding time is marked as power interference; at the same time, the force interference generated by the external environment is obtained, such as external force interference such as hurricanes and tree contact.

[0050] If the sudden supply parameter fluctuation is monitored based on the equipment connected to the power line, and if the sudden supply parameter span is set to a threshold, then the current sudden supply parameter fluctuation is marked as a fault interference.

[0051] An operating timeline is constructed based on the operating periods of the power lines, and the times of power interference, force interference, and fault interference are displayed on the operating timeline.

[0052] Temporary drop prediction based on the existing runtime timeline:

[0053] The cumulative duration corresponding to the overlapping occurrence of multiple types of interference moments in the timeline is collected. At the same time, the floating trend of the interval duration of a single occurrence of the same type of interference moment and the floating trend of the interval duration of the synchronous occurrence of multiple types of interference moments are collected. It should be explained that a single occurrence means the interval duration analysis of a single type of interference moment of the same type, and multiple types of interference moments means the interval duration of synchronous recording after the occurrence of multiple types of interference moments.

[0054] Specifically, based on the fluctuation trend of the interval duration of a single occurrence of the same type of interference and the fluctuation trend of the interval duration of simultaneous occurrence of multiple types of interference, the shortening span of the interval duration of a single occurrence and the shortening span of the interval duration of simultaneous occurrence are obtained. In all operating scenarios, the highest value of the shortening span is used as the collected data.

[0055] Furthermore, threshold comparisons were performed on the cumulative duration corresponding to the overlapping occurrence times of multiple types of interference and the highest shortening span within the runtime timeline.

[0056] If the cumulative duration of multiple types of interference times overlaps and exceeds the cumulative duration threshold, or if the highest shortening span within the running timeline exceeds the highest shortening span threshold, it indicates that there is a risk of temporary sag in the current running timeline. A temporary sag prediction signal is generated and sent to the optimization control platform. The optimization control platform monitors the frequency of interference times within the running timeline. If the frequency exceeds the red line value in the historical running process, a temporary sag warning is issued, and the parameters of the frequency modulator are controlled.

[0057] If the cumulative duration corresponding to the overlapping occurrence of multiple types of interference does not exceed the cumulative duration threshold, and the highest shortening span within the running timeline does not exceed the highest shortening span threshold, it indicates that there is no risk of temporary descent in the current running timeline, and a no-descent prediction signal is generated and no temporary descent prediction signal is sent to the optimization control platform.

[0058] Upon completion, a descent parameter analysis signal is generated and sent to the descent parameter analysis unit;

[0059] After receiving the voltage sag parameter analysis signal, the voltage sag parameter analysis unit performs an impact analysis on the inverter parameters during voltage sags. Through the impact analysis, targeted prevention and control can be carried out according to the real-time sag type, so as to avoid the inverter parameters not being adjusted in time when different types of voltage sags occur, which would directly affect the operation of the inverter and reduce the stability of the entire power supply.

[0060] The maximum deviation of the peak value of the DC bus voltage drop span before and after the span fluctuates in the fixed span voltage sag scenario is obtained. At the same time, the torque compensation increases the output current and the current output current needs to be compensated before and after the time fluctuates in the fixed duration voltage sag scenario. The compensation frequency increases the span of the inverter based on the compensation time and the time when compensation is needed.

[0061] The maximum deviation of the peak value of the DC bus voltage drop span and the frequency increase span of the inverter are compared with the maximum deviation threshold and the frequency increase span threshold, respectively:

[0062] If the maximum deviation of the peak value of the DC bus voltage drop exceeds the maximum deviation threshold, or the increase in the compensation frequency of the inverter exceeds the frequency increase threshold, it is determined that the fixed and non-fixed sag parameters have different effects, and a scenario-specific control signal is generated and sent to the optimization control platform. The optimization control platform receives the scenario-specific control signal and sets different control strategies for the inverter parameters under different scenarios.

[0063] If the maximum deviation of the DC bus voltage drop span peak value does not exceed the maximum deviation threshold, and the frequency increase span of the inverter compensation frequency does not exceed the frequency increase span threshold, then it is determined that the fixed and non-fixed sag parameters have the same impact, a single-scenario control signal is generated and sent to the optimization control platform. The optimization control platform receives the single-scenario control signal, and different scenarios set the same control strategy for the inverter parameter regulation.

[0064] After completing the descent parameter analysis, an optimized identification control signal is generated and sent to the optimized identification control unit;

[0065] After receiving the optimization identification control signal, the optimization identification control unit performs a sag environment simulation on the frequency converter. This allows for the inference of the impact of sag parameters on the frequency converter during operation based on the sag environment simulation. This enables targeted parameter control after identifying the downtime risk, thereby reducing the impact of sags and improving the operating efficiency of the frequency converter.

[0066] The magnitude and duration of the voltage sag are labeled as causative data;

[0067] Furthermore, the power transmission floating marker is used as the influencing data, and the load condition is set to simulate the operating intensity of the environment, namely low load, rated load, and overload; low load is represented as 30% below the rated load;

[0068] Ensure proper grounding and no short-circuit hazards. Start the programmable AC power supply, set it to normal voltage output, and start the frequency converter and load to ensure the system operates stably under the set load conditions.

[0069] The trigger data is first subjected to a single fluctuation, and the fluctuation range is uniform.

[0070] When any fluctuation occurs in the causal data, the deviation between the real-time power delivery fluctuation and the set delivery fluctuation is collected and marked as the supply deviation. As the simulation operation time increases, the supply deviation at multiple moments is analyzed by averaging the data. That is, the average supply deviation is compared with the corresponding deviation red line threshold. When the interval value continues to shorten, the corresponding causal data and influence data are set to an inverse proportional relationship. When the influence data fluctuates, the relevant parameters of the power line operation and the relevant parameters of the frequency converter are obtained according to the data characteristics of the influence data. That is, the fluctuation of the values ​​of the relevant parameters of the power line operation and the relevant parameters of the frequency converter can change the value of the influence data or the change of the influence value can affect the relevant parameters of the power line operation and the relevant parameters of the frequency converter.

[0071] And these are respectively labeled as parameters related to passive influence and parameters related to active influence;

[0072] As the interval value continues to shorten, active influence-related parameters are recorded, and all types of active influence-related parameters are used to construct an influence data set. The impact on the fluctuation of influence data in the current period is calculated based on the dispersion of subsets within the set and the variance. Specifically, it can be divided into high-impact stage and low-impact stage.

[0073] At this time, the floating trend statistics of the parameters related to passive influence are performed. When the causal data is fluctuating, if it is in the high influence stage, the current floating trend is set as an unfavorable floating trend, the floating trend of the current parameters related to passive influence is set as an abnormal trend, and the corresponding opposite trend is marked as an optimized control trend.

[0074] If it is in a low-impact stage, the current floating trend is set as a favorable floating trend, the floating trend of the current passive impact related parameters is set as a normal trend, and the corresponding opposite trend is marked as an optimized prevention and control trend.

[0075] All types of passively affected parameters and actively affected parameters are sent to the optimization control platform. The floating trend type is noted according to each type of parameter. That is, when there is no voltage sag, the unfavorable floating trend of each type of actively affected parameter is used as the monitoring direction, and the optimization and control trend of passively affected parameters is set as the numerical control reference standard.

[0076] When voltage dips are present, the favorable floating trends of various types of actively affected parameters are used as the monitoring direction, and the optimized control trends of passively affected parameters are set as the numerical control reference standard.

[0077] Please see Figure 2 As shown, the adaptive parameter optimization control method for a frequency converter under voltage sag includes the following steps:

[0078] Step 1: Voltage sag prediction. Predict voltage sags in the operating environment of the frequency converter.

[0079] Step 2: Voltage sag parameter analysis, analyzing the impact of voltage sag on inverter parameters;

[0080] Step 3: Optimize identification and control, simulate sag environment for the frequency converter, and infer the impact of sag parameters on the frequency converter during operation based on the sag environment simulation.

[0081] Thresholds, preset values, preset ranges, etc. are set for result comparison and analysis to determine whether they are good or bad. The value of these thresholds is determined by a combination of large-scale model analysis of sample data and human experience. They can also be adjusted appropriately based on seasonal or common-sense influences.

[0082] Furthermore, the settings for weighting ratios, influence factors, etc., are based on the magnitude of each parameter's influence on the results. The specific values ​​are allocated to ultimately reflect the impact on the results. The settings for input and storage are also determined by a combination of large-scale model analysis of sample data and human experience. Appropriate adjustments can also be made based on seasonal or rational influence conditions.

[0083] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An adaptive parameter optimization control system for a frequency converter under voltage sag, characterized in that, This includes an optimized control platform, which connects to: The voltage sag prediction unit predicts voltage sags in the operating environment of the frequency converter. The voltage sag parameter analysis unit performs an impact analysis on the inverter parameters during voltage sags and sets control strategies for inverter parameters based on the impact analysis. Optimize the identification control unit, simulate the sag environment of the frequency converter, and infer the impact of sag parameters on the frequency converter during operation based on the sag environment simulation. The process of generating prediction units during a temporary descent is as follows: Monitor the wires within the control range of the frequency converter and the external environment of the wires; During the operation of the power line, the time of lightning in the external environment is obtained, and the lightning interference generated at the corresponding time is marked as power interference; at the same time, the force interference generated by the external environment is obtained; according to the monitoring of the sudden supply parameter fluctuation of the equipment connected to the power line, if the sudden supply parameter span exceeds the set parameter span threshold, the current sudden supply parameter fluctuation is marked as fault interference. An operating timeline is constructed based on the operating periods of the power lines, and the times of power interference, force interference, and fault interference are displayed on the operating timeline. Temporary drop prediction based on the existing runtime timeline: The cumulative duration corresponding to the overlapping occurrence of multiple types of interference moments in the running timeline is collected. At the same time, the floating trend of the interval duration of a single occurrence of the same type of interference moment and the floating trend of the interval duration of the synchronous occurrence of multiple types of interference moments are collected. Specifically, based on the fluctuation trend of the interval duration of a single occurrence of the same type of interference and the fluctuation trend of the interval duration of simultaneous occurrence of multiple types of interference, the shortening span of the interval duration of a single occurrence and the shortening span of the interval duration of simultaneous occurrence are obtained. In all operating scenarios, the highest value of the shortening span is used as the collected data. Furthermore, threshold comparisons were performed on the cumulative duration corresponding to the overlapping occurrence times of multiple types of interference and the highest shortening span within the runtime timeline. If the cumulative duration of multiple types of interference times overlaps and exceeds the cumulative duration threshold, or if the highest shortening span within the running timeline exceeds the highest shortening span threshold, a sag prediction signal is generated and sent to the optimization control platform. The optimization control platform monitors the frequency of interference times within the running timeline. If the frequency exceeds the red line value in the historical running process, a sag warning is issued, and the parameters of the frequency modulator are controlled. If the cumulative duration corresponding to the overlapping occurrence of multiple types of interference does not exceed the cumulative duration threshold, and the highest shortening span within the running timeline does not exceed the highest shortening span threshold, then no sag prediction signal is generated and no sag prediction signal is sent to the optimization control platform.

2. The adaptive parameter optimization control system for a frequency converter under voltage sag according to claim 1, characterized in that, The process of the transient parameter analysis unit is as follows: The maximum deviation of the peak value of the DC bus voltage drop span before and after the span fluctuates in the fixed span voltage sag scenario is obtained. At the same time, the time when the torque compensation increases the output current and the time when the output current needs to be compensated in the current period are obtained before and after the time fluctuates in the fixed duration voltage sag scenario. Based on the compensation time and the time when compensation is needed, the compensation frequency of the inverter is increased by the span.

3. The adaptive parameter optimization control system for frequency converters under voltage sags according to claim 2, characterized in that, The maximum deviation of the peak value of the DC bus voltage drop span and the frequency increase span of the inverter are compared with the maximum deviation threshold and the frequency increase span threshold, respectively: If the maximum deviation of the peak value of the DC bus voltage drop exceeds the maximum deviation threshold, or the frequency increase span of the inverter exceeds the frequency increase span threshold, a scenario-specific control signal is generated and sent to the optimization control platform; if the maximum deviation of the peak value of the DC bus voltage drop does not exceed the maximum deviation threshold, and the frequency increase span of the inverter does not exceed the frequency increase span threshold, a single-scenario control signal is generated and sent to the optimization control platform.

4. The adaptive parameter optimization control system for a frequency converter under voltage sag according to claim 1, characterized in that, The process of optimizing the identification control unit is as follows: The magnitude and duration of the voltage sag are labeled as causative data; Furthermore, the power transmission floating marker is used as the influencing data, and the load condition is set to simulate the operating intensity of the environment, namely low load, rated load, and overload; low load is represented as 30% below the rated load; Ensure proper grounding and no short-circuit hazards. Start the programmable AC power supply, set it to normal voltage output, and start the frequency converter and load to ensure the system operates stably under the set load conditions. Prioritize single-float fluctuations for the causative data, with a uniform fluctuation range.

5. The adaptive parameter optimization control system for a frequency converter under voltage sag according to claim 4, characterized in that, When arbitrary fluctuations occur in the causal data, the deviation between the real-time power delivery fluctuation and the set delivery fluctuation is collected and marked as a supply deviation. As the simulation operation time extends, the supply deviations at multiple moments are analyzed by averaging the data. That is, the average supply deviation is compared with the corresponding deviation red line threshold. When the interval between the average supply deviation and the corresponding deviation red line threshold continues to shorten, the corresponding causal data and influence data are set to an inverse proportional relationship. When the influence data fluctuates, the relevant parameters of the power line operation and the frequency converter are obtained according to the data characteristics of the influence data. That is, the fluctuation of the values ​​of the relevant parameters of the power line operation and the frequency converter can change the value of the influence data, or the change of the influence value can affect the relevant parameters of the power line operation and the frequency converter. These are marked as passive influence parameters and active influence parameters, respectively.

6. The adaptive parameter optimization control system for a frequency converter under voltage sag according to claim 5, characterized in that, As the interval value continues to shorten, active influence-related parameters are recorded, and all types of active influence-related parameters are used to construct an influence data set. The impact on the fluctuation of influence data in the current period is calculated based on the dispersion of subsets within the set and the variance. Specifically, it is divided into high-impact stage and low-impact stage. The floating trend statistics of parameters related to passive influence are performed. When the causal data is fluctuating, if it is in a high-impact stage, the current floating trend is set as an unfavorable floating trend, the floating trend of the current parameters related to passive influence is set as an abnormal trend, and the corresponding opposite trend is marked as an optimized control trend. If it is in a low-impact stage, the current floating trend is set as a favorable floating trend, the floating trend of the current passive impact related parameters is set as a normal trend, and the corresponding opposite trend is marked as an optimized prevention and control trend. All types of passively affected parameters and actively affected parameters are sent to the optimization control platform. The floating trend type is noted for each type of parameter. That is, when there is no voltage sag, the unfavorable floating trend of each type of actively affected parameter is used as the monitoring direction, and the optimization and control trend of the passively affected parameter is set as the numerical control reference standard. When there is a voltage sag, the favorable floating trend of each type of actively affected parameter is used as the monitoring direction, and the optimization and control trend of the passively affected parameter is set as the numerical control reference standard.

7. An adaptive parameter optimization control method for a frequency converter under voltage sag, characterized in that, The adaptive parameter optimization control system for the frequency converter under voltage sag, as described in any one of claims 1-6, comprises the following steps: Step 1: Voltage sag prediction. Predict voltage sags in the operating environment of the frequency converter. Step 2: Voltage sag parameter analysis, analyzing the impact of voltage sag on inverter parameters; Step 3: Optimize identification and control, simulate sag environment for the frequency converter, and infer the impact of sag parameters on the frequency converter during operation based on the sag environment simulation.

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