Two-stage compression frequency conversion control method and device of screw air compressor

Abstract

The invention discloses a two-stage compression frequency conversion control method and device for a screw air compressor, and relates to the technical field of intelligent control. The method comprises the following steps: carrying out demand fluctuation analysis on a gas supply demand sequence, and determining a target gas supply demand interval; performing tolerance interval retrieval on the K frequency conversion control parameters of the target screw air compressor to determine K parameter tolerance intervals; the real-time air supply pressure and K real-time frequency conversion control parameters of the target screw air compressor are collected, double-target optimization is conducted on the K real-time frequency conversion control parameters in combination with the target air supply demand interval, and K real-time adjustment frequency conversion control parameters are obtained; and the K real-time adjustment frequency conversion control parameters are transmitted to a control module, and frequency conversion control is conducted on the target screw air compressor through the control module. The technical problem that in the prior art, efficient and energy-saving frequency conversion control is difficult to conduct on the screw air compressor when the air supply requirement fluctuates is solved, and the technical effects of improving the air supply stability, reducing the energy consumption and optimizing the frequency conversion control parameter adjusting efficiency are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control, and particularly relates to a two-stage compression variable frequency control method and device for a screw air compressor. BACKGROUND

[0002] At present, screw air compressors are widely used in industrial production and gas supply systems, mainly for providing stable compressed air. However, the existing air compressor control technology often uses fixed control strategies when facing dynamic changes in gas supply demand, making it difficult to achieve accurate response to variable demand, resulting in low efficiency of the gas supply system, increased energy consumption, and shortened equipment operating life. In addition, due to the lack of effective parameter optimization and real-time adjustment mechanism, the existing air compressor system is prone to instability of the gas supply pressure when the gas supply demand fluctuates, affecting the overall production efficiency and stability. SUMMARY

[0003] The present application provides a two-stage compression variable frequency control method and device for a screw air compressor, solving the technical problem that the screw air compressor in the prior art is difficult to efficiently and energy-efficiently perform variable frequency control when the gas supply demand fluctuates.

[0004] In view of the above problems, the present application provides a two-stage compression variable frequency control method and device for a screw air compressor.

[0005] In a first aspect of the present application, a two-stage compression variable frequency control method for a screw air compressor is provided, the method comprising:

[0006] obtaining a gas supply demand sequence of a target screw air compressor within a preset monitoring window, performing demand fluctuation analysis on the gas supply demand sequence, and determining a target gas supply demand interval; using the target gas supply demand interval as an index, performing a tolerance interval search on K variable frequency control parameters of the target screw air compressor to determine K parameter tolerance intervals, wherein K is an integer greater than or equal to 1; collecting real-time gas supply pressure and K real-time variable frequency control parameters of the target screw air compressor, combining the K real-time variable frequency control parameters with the target gas supply demand interval to perform double-objective optimization, and using the K parameter tolerance intervals as constraints during the optimization process to obtain K real-time adjusted variable frequency control parameters, wherein the double-objective optimization is to reduce the difference between the real-time gas supply pressure and the target gas supply demand interval and to reduce the energy consumption of the target screw air compressor; and transmitting the K real-time adjusted variable frequency control parameters to a control module to perform variable frequency control on the target screw air compressor using the control module.

[0007] In a second aspect of the present application, a two-stage compression variable frequency control device for a screw air compressor is provided, the device comprising:

[0008] The analysis module is used to acquire a gas supply demand sequence of the target screw air compressor in a preset monitoring window, perform demand fluctuation analysis on the gas supply demand sequence, and determine a target gas supply demand interval; the retrieval module is used to take the target gas supply demand interval as an index, perform tolerance interval retrieval on K variable frequency control parameters of the target screw air compressor, and determine K parameter tolerance intervals, wherein K is an integer greater than or equal to 1; the optimization module is used to collect real-time gas supply pressure and K real-time variable frequency control parameters of the target screw air compressor, perform double-target optimization on the K real-time variable frequency control parameters in combination with the target gas supply demand interval, and obtain K real-time adjustment variable frequency control parameters in the optimization process with the K parameter tolerance intervals as constraints, wherein the double-target optimization is to reduce the difference between the real-time gas supply pressure and the target gas supply demand interval and reduce the energy consumption of the target screw air compressor; and the control module is used to transmit the K real-time adjustment variable frequency control parameters to a control module, and perform variable frequency control on the target screw air compressor by using the control module.

[0009] The one or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0010] Firstly, the gas supply demand sequence of the target screw air compressor in a preset monitoring window is acquired, demand fluctuation analysis is performed on the gas supply demand sequence, and a target gas supply demand interval is determined. Then, the target gas supply demand interval is taken as an index, tolerance interval retrieval is performed on K variable frequency control parameters of the target screw air compressor, and K parameter tolerance intervals are determined, wherein K is an integer greater than or equal to 1. Then, real-time gas supply pressure and K real-time variable frequency control parameters of the target screw air compressor are collected, double-target optimization is performed on the K real-time variable frequency control parameters in combination with the target gas supply demand interval, and K real-time adjustment variable frequency control parameters are obtained in the optimization process with the K parameter tolerance intervals as constraints, wherein the double-target optimization is to reduce the difference between the real-time gas supply pressure and the target gas supply demand interval and reduce the energy consumption of the target screw air compressor. Finally, the K real-time adjustment variable frequency control parameters are transmitted to a control module, and variable frequency control is performed on the target screw air compressor by using the control module. The technical problem that the screw air compressor in the prior art is difficult to perform variable frequency control efficiently and energy-savingly when the gas supply demand fluctuates is solved, and the technical effects of improving gas supply stability, reducing energy consumption, and optimizing variable frequency control parameter adjustment efficiency are achieved. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0012] Figure 1 This is a schematic flowchart of a two-stage compression variable frequency control method for a screw air compressor provided in an embodiment of this application;

[0013] Figure 2 This is a schematic diagram of the two-stage compression variable frequency control device for a screw air compressor provided in an embodiment of this application.

[0014] Figure labeling: Analysis module 11, retrieval module 12, optimization module 13, control module 14. Detailed Implementation

[0015] This application provides a two-stage compression variable frequency control method and device for screw air compressors, which solves the technical problem in the prior art that screw air compressors are difficult to control efficiently and energy-savingly when the air supply demand fluctuates.

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0017] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to these processes, methods, products, or devices.

[0018] Example 1, as Figure 1 As shown, this application provides a two-stage compression variable frequency control method for a screw air compressor, wherein the method includes:

[0019] Obtain the air supply demand sequence of the target screw air compressor within the preset monitoring window, perform demand fluctuation analysis on the air supply demand sequence, and determine the target air supply demand range.

[0020] Within a preset monitoring window (preset time range), the air supply demand data of the target screw air compressor is collected to form the air supply demand sequence of the target screw air compressor; statistical analysis (such as time series analysis) is performed on the air supply demand sequence of the target screw air compressor to identify the fluctuation pattern and trend of demand, and then the target air supply demand range is determined.

[0021] Furthermore, the gas supply demand sequence of the target screw air compressor within a preset monitoring window is obtained, and demand fluctuation analysis is performed on the gas supply demand sequence to determine the target gas supply demand range, including:

[0022] The average gas supply demand is obtained by traversing the gas supply demand sequence for average gas supply demand analysis; the demand fluctuation factor is determined by traversing the gas supply demand sequence for demand fluctuation analysis; the analysis iteration step is determined based on the demand fluctuation factor and the difference between the maximum value and the minimum value of the gas supply demand sequence; and the target gas supply demand interval is determined by performing demand fluctuation analysis on the gas supply demand sequence according to the analysis iteration step, with the average gas supply demand as the starting point.

[0023] Specifically, each data point in the gas supply demand sequence is traversed and accumulated to calculate the total gas supply demand of the entire sequence, and then the total number of data points is divided to obtain the average gas supply demand; the variance of each data point from the average gas supply demand is calculated to obtain the demand fluctuation factor; the maximum value and the minimum value in the gas supply demand sequence are determined, and the difference between them is calculated, which represents the overall range of gas supply demand; the analysis iteration step can be obtained by dividing the difference by an empirical coefficient, which is related to the proportion of the fluctuation factor, to ensure that the step is fine enough to effectively capture the fluctuation characteristics. The average gas supply demand is used as the starting point of the analysis, and the gas supply demand value is gradually increased or decreased according to the determined analysis iteration step, while the gas supply demand frequency or probability corresponding to each value is calculated; according to the distribution of gas supply demand frequency or probability, a suitable interval is selected as the target gas supply demand interval.

[0024] Further, the average gas supply demand is used as the starting point, and the demand fluctuation analysis is performed on the gas supply demand sequence according to the analysis iteration step to determine the target gas supply demand interval, the method comprising:

[0025] A two-dimensional demand fluctuation analysis space is constructed with time as the horizontal coordinate axis and gas supply demand as the vertical coordinate axis; the gas supply demand sequence is input into the two-dimensional demand fluctuation analysis space to obtain a plurality of analysis space points, wherein each analysis space point corresponds to the gas supply demand at a time point; a straight line that passes through the starting point and is parallel to the horizontal coordinate axis of the two-dimensional demand fluctuation analysis space is used as the starting straight line; the starting straight line is iteratively analyzed according to the analysis iteration step to determine the target iterative straight line; the analysis space points located above the target iterative straight line and having a distance to the target iterative straight line of the analysis iteration step are used as the first interval endpoint; the analysis space points located below the target iterative straight line and having a distance to the target iterative straight line of the analysis iteration step are used as the second interval endpoint; and the target gas supply demand interval is constructed according to the first interval endpoint and the second interval endpoint.

[0026] Preferably, a two-dimensional plane is established with time as the horizontal coordinate axis and gas supply demand as the vertical coordinate axis to visually represent the change of gas supply demand over time; each data point in the gas supply demand sequence (including the time point and the corresponding gas supply demand) is input into the two-dimensional demand fluctuation analysis space, and each data point corresponds to an analysis space point in the space; the average gas supply demand is taken as the starting point, and the position of the value on the vertical coordinate axis represents the average gas supply demand; in the two-dimensional demand fluctuation analysis space, a straight line passing through the starting point and parallel to the horizontal coordinate axis (time axis) is drawn as the starting straight line; starting from the starting straight line, up and down iterations are performed according to the analysis iteration step, and a new straight line is obtained each time the iteration is performed until a specific stop condition is met (such as reaching a preset number of iterations or the position of the straight line coincides with the distribution characteristics of the gas supply demand sequence); according to the iteration result, a straight line that best represents the fluctuation characteristics of the gas supply demand is selected as the target iteration straight line; above the target iteration straight line, an analysis space point that is equal to the analysis iteration step away from the target iteration straight line is found as the first interval endpoint, and if there are multiple points that meet the condition, the one closest to the target iteration straight line can be selected; below the target iteration straight line, an analysis space point that is equal to the analysis iteration step away from the target iteration straight line is also found as the second interval endpoint, and similarly, if there are multiple points that meet the condition, the one closest to the target iteration straight line can be selected; the range of the target gas supply demand interval is determined according to the first interval endpoint and the second interval endpoint; the target gas supply demand interval is defined by the positions of the first interval endpoint and the second interval endpoint on the vertical coordinate axis (i.e., the gas supply demand).

[0027] Further, the method for iteratively analyzing the starting straight line according to the analysis iteration step to determine the target iteration straight line comprises:

[0028] The number of analysis space points in the two-dimensional demand fluctuation analysis space that are equal to the analysis iteration step away from the starting straight line is counted to obtain a starting distribution quantity; the starting straight line is moved upward and downward by the analysis iteration step to obtain a first upward-moving iteration straight line and a first downward-moving iteration straight line; it is judged whether the first upward-moving distribution quantity of the first upward-moving iteration straight line and the first downward-moving distribution quantity of the first downward-moving iteration straight line are both less than or equal to the starting distribution quantity, and if so, the starting straight line is taken as the target iteration straight line.

[0029] Preferably, in the two-dimensional demand fluctuation analysis space, the number of analysis space points with a distance equal to the analysis iteration step length from the starting straight line is counted, and the number of points is the starting distribution quantity, which is used to evaluate the distribution of gas supply demand on the initial horizontal line; the starting straight line is translated upwards by the analysis iteration step length to obtain a first upwardly shifted iteration straight line, and similarly, the starting straight line is translated downwards by the analysis iteration step length to obtain a first downwardly shifted iteration straight line; the number of analysis space points with a distance equal to the analysis iteration step length from the first upwardly shifted iteration straight line and the first downwardly shifted iteration straight line is counted respectively to obtain a first upwardly shifted distribution quantity and a first downwardly shifted distribution quantity; it is judged whether the first upwardly shifted distribution quantity and the first downwardly shifted distribution quantity are both less than or equal to the starting distribution quantity; if both are less than or equal to the starting distribution quantity, it is indicated that the starting straight line is already at a relatively balanced position, and the analysis space points on the upper and lower sides thereof are evenly distributed, and thus the starting straight line can be used as the target iteration straight line.

[0030] Further, the method comprises:

[0031] If not, the first upwardly shifted distribution quantity and the first downwardly shifted distribution quantity are compared in size, the straight line corresponding to the larger distribution quantity is used as the stage iteration straight line, and the moving direction in which the stage iteration straight line is obtained is used as the iteration moving direction;

[0032] The stage iteration straight line is moved in the iteration moving direction by the analysis iteration step length, and after a plurality of moving iterations, when the distribution quantity obtained in the current iteration is less than the distribution quantity obtained in the last iteration, the iteration is stopped, and the stage iteration straight line obtained in the last iteration is used as the target iteration straight line.

[0033] Preferably, if the first upward distribution amount and the first downward distribution amount of the starting straight line are not less than the starting distribution amount, the sizes of the two are compared; the straight line corresponding to the larger distribution amount (the first upward iteration straight line or the first downward iteration straight line) is taken as the stage iteration straight line; the iteration moving direction is determined according to the source of the stage iteration straight line (the first upward iteration straight line or the first downward iteration straight line); if the stage iteration straight line comes from the first upward iteration straight line, the iteration moving direction is upward; if it comes from the first downward iteration straight line, the iteration moving direction is downward; the stage iteration straight line is moved in the iteration moving direction according to the analysis iteration step, to obtain a new stage iteration straight line; the number of analysis space points on both sides of the new stage iteration straight line, whose distance is the analysis iteration step, is counted, that is, a new distribution amount is obtained; the distribution amount obtained in the current iteration is compared with the distribution amount obtained in the last iteration; if the distribution amount obtained in the current iteration is less than the distribution amount obtained in the last iteration, it is indicated that the iteration direction is correct and close to the target iteration straight line, and the iteration may cause the distribution amount to further decrease; if the distribution amount obtained in the current iteration is not less than the distribution amount obtained in the last iteration (that is, the distribution amount does not decrease or even increases), it is indicated that the iteration direction may be incorrect or has approached the limit position, and the iteration should be stopped at this time; when the iteration stop condition is met (that is, the distribution amount obtained in the current iteration is less than the distribution amount obtained in the last iteration, but the next iteration needs to be judged), the iteration is stopped; the stage iteration straight line obtained in the last iteration is taken as the target iteration straight line.

[0034] The K variable frequency control parameters of the target screw air compressor are searched in a tolerance interval based on the target gas supply demand interval, to determine K parameter tolerance intervals, wherein K is an integer greater than or equal to 1.

[0035] The K variable frequency control parameters of the target screw air compressor include gas supply pressure, frequency converter frequency, inter-stage cooling temperature, exhaust temperature, minimum starting pressure, unloading pressure, suction temperature, and suction humidity. The K variable frequency control parameters related to the target gas supply demand interval in the screw air compressor are searched based on the target gas supply demand interval; in the found data, the K parameters are analyzed based on historical operation data, experimental results, or preset parameter ranges, to determine the upper and lower limit ranges of each parameter, that is, the tolerance interval, and finally obtain the K parameter tolerance intervals.

[0036] Real-time gas supply pressure and K real-time variable frequency control parameters of the target screw air compressor are collected, the K real-time variable frequency control parameters are double-target optimized based on the target gas supply demand interval, and the K parameter tolerance intervals are used as constraints in the optimization process, to obtain K real-time adjustment variable frequency control parameters, wherein the double-target optimization is to reduce the difference between the real-time gas supply pressure and the target gas supply demand interval and to reduce the energy consumption of the target screw air compressor.

[0037] The air supply pressure data of the target screw air compressor and K frequency conversion control parameters are collected in real time; the real-time collected air supply pressure and frequency conversion control parameters are compared with the determined target air supply demand interval, and whether the air supply pressure deviates from the demand interval is analyzed; reducing the difference between the real-time air supply pressure and the target air supply demand interval and reducing the energy consumption of the air compressor are taken as the core targets of double-target optimization; an algorithm suitable for multi-objective optimization, such as a multi-objective genetic algorithm or a particle swarm optimization algorithm, is selected to perform optimization under the constraints of double-target functions and parameter tolerance intervals; through iterative calculation, one or more sets of frequency conversion control parameter values that meet the constraint conditions and can simultaneously optimize the two target functions are found; after optimization is completed, K real-time adjustment frequency conversion control parameters are output, which are the optimal values obtained after balancing the air supply demand and energy consumption.

[0038] Further, the method comprises:

[0039] According to the deviation value of the real-time air supply pressure from the interval median value of the target air supply demand interval, the optimization adjustment amplitude is determined, wherein the optimization adjustment amplitude is the parameter amplitude of adjusting the K real-time frequency conversion control parameters at a time; the K real-time frequency conversion control parameters are randomly adjusted according to the optimization adjustment amplitude to obtain K first adjustment frequency conversion control parameters; the control effect of the K first adjustment frequency conversion control parameters is analyzed by using an adjustment fitness analyzer to obtain a first adjustment fitness; the K real-time frequency conversion control parameters are randomly adjusted again according to the optimization adjustment amplitude to obtain K second adjustment frequency conversion control parameters; the control effect of the K second adjustment frequency conversion control parameters is analyzed by using an adjustment fitness analyzer to obtain a second adjustment fitness; whether the first adjustment fitness is less than or equal to the second adjustment fitness is judged, and if so, the K second adjustment frequency conversion control parameters are taken as K stage adjustment frequency conversion control parameters; through multiple iterative analyses, when a preset number of iterations is met, the K stage adjustment frequency conversion control parameters corresponding to the maximum adjustment fitness are taken as the K real-time adjustment frequency conversion control parameters.

[0040] Preferably, the deviation of the real-time supply pressure from the interval median of the target supply demand interval is calculated; according to the size of the deviation, the parameter amplitude of the single adjustment of the K real-time variable frequency control parameters is determined, that is, the optimization adjustment amplitude, and the size of the optimization adjustment amplitude is proportional to the size of the deviation, thereby ensuring the effectiveness and stability of the adjustment. According to the optimization adjustment amplitude, the K real-time variable frequency control parameters are randomly adjusted to obtain K first adjustment variable frequency control parameters; the control effect of the K first adjustment variable frequency control parameters is analyzed by using the adjustment fitness analyzer to obtain a first adjustment fitness; the K real-time variable frequency control parameters are randomly adjusted again according to the optimization adjustment amplitude to obtain K second adjustment variable frequency control parameters; the K second adjustment variable frequency control parameters are analyzed to obtain a second adjustment fitness. It is judged whether the first adjustment fitness is less than or equal to the second adjustment fitness. If yes, it means that the second adjustment is better, and the adjustment should be continued in this direction; if the second adjustment fitness is better, the K second adjustment variable frequency control parameters are taken as new K stage adjustment variable frequency control parameters; the steps of random adjustment and fitness analysis are repeated for multiple iterations, and in each iteration, the new adjustment direction and parameter value are determined according to the result of the previous iteration. In the iteration process, a preset iteration number is set as a stop condition; when the preset iteration number is reached, the iteration is stopped, and from all the iteration results, the K stage adjustment variable frequency control parameters corresponding to the maximum adjustment fitness are selected as the final K real-time adjustment variable frequency control parameters.

[0041] The main function of the adjustment fitness analyzer is to evaluate the adjusted variable frequency control parameters in the dual-target optimization process and analyze whether these parameters can meet the dual targets of reducing the supply pressure difference and reducing energy consumption. The adjustment fitness analyzer receives the input K adjustment variable frequency control parameters and the collected real-time supply pressure and target supply demand interval; the adjustment fitness analyzer performs fitness evaluation according to the supply pressure deviation and energy consumption evaluation. The supply pressure deviation refers to the deviation of the supply pressure under the adjustment parameters from the median of the target supply demand interval, and the fitness score is inversely proportional to the deviation. The smaller the deviation, the higher the fitness score. The energy consumption evaluation refers to the impact of the adjustment parameters on the energy consumption of the screw air compressor. The lower the energy consumption, the higher the fitness score. The fitness formula is: Fitness = w1(1 / supply pressure deviation) + w2(1 / energy consumption), where w1 and w2 are weight factors of the fitness, used to balance the priority of supply pressure and energy consumption optimization.

[0042] Further, the random adjustment is to increase or decrease N real-time variable frequency control parameters in the K real-time variable frequency control parameters according to the optimization adjustment amplitude, and N is an integer less than or equal to K.

[0043] The random adjustment is an operation on a subset (i.e., N real-time variable frequency control parameters, where N is less than or equal to K) of the K real-time variable frequency control parameters, which are randomly increased or decreased according to the optimization adjustment amplitude. Specifically, N parameters are randomly selected from the K real-time variable frequency control parameters for adjustment; for each selected parameter, it is randomly decided whether to increase or decrease, which can be realized by generating a random number (such as 0 or 1), where 0 represents decrease and 1 represents increase.

[0044] The K real-time adjustment variable frequency control parameters are transmitted to the control module, and the control module is used to perform variable frequency control on the target screw air compressor.

[0045] The K real-time adjustment variable frequency control parameters determined after multiple iterations and optimization analysis are transmitted to the control module; after the control module receives the K adjusted variable frequency control parameters, the parameters of the frequency converter of the target screw air compressor are immediately adjusted; the control module adjusts the gas supply pressure of the air compressor by adjusting the parameters (such as frequency and speed) to match the current gas supply demand interval; the control module applies the parameter adjustment result to the air compressor motor in real time through the frequency converter to change the operating state (such as acceleration and deceleration) of the motor. By transmitting the K real-time adjustment variable frequency control parameters to the control module and performing variable frequency control, the precise adjustment of the gas supply pressure of the screw air compressor and the effective optimization of the energy consumption are realized, which meets the efficient operation demand in industrial applications.

[0046] In summary, the embodiments of the present application have at least the following technical effects:

[0047] First, the gas supply demand sequence of the target screw air compressor in a preset monitoring window is obtained, the demand fluctuation analysis is performed on the gas supply demand sequence, and the target gas supply demand interval is determined. Then, taking the target gas supply demand interval as an index, the K variable frequency control parameters of the target screw air compressor are searched in the tolerance interval, and the K parameter tolerance intervals are determined, where K is an integer greater than or equal to 1. Then, the real-time gas supply pressure and the K real-time variable frequency control parameters of the target screw air compressor are collected, the K real-time variable frequency control parameters are optimized in combination with the target gas supply demand interval, and the K parameter tolerance intervals are used as constraints in the optimization process to obtain the K real-time adjustment variable frequency control parameters, where the double-objective optimization is to reduce the difference between the real-time gas supply pressure and the target gas supply demand interval and to reduce the energy consumption of the target screw air compressor. Finally, the K real-time adjustment variable frequency control parameters are transmitted to the control module, and the control module is used to perform variable frequency control on the target screw air compressor. The technical problem that the screw air compressor in the prior art is difficult to perform variable frequency control efficiently and energy-savingly when the gas supply demand fluctuates is solved, and the technical effects of improving the gas supply stability, reducing the energy consumption, and optimizing the variable frequency control parameter adjustment efficiency are achieved.

[0048] Embodiment two, based on the same inventive concept as the two-stage compression variable frequency control method of the screw air compressor in the preceding embodiment, as shown in Figure 2 The application provides a two-stage compression variable frequency control device of a screw air compressor, wherein the device comprises:

[0049] An analysis module 11 is configured to acquire a supply air demand sequence of a target screw air compressor in a preset monitoring window, perform demand fluctuation analysis on the supply air demand sequence, and determine a target supply air demand interval; a retrieval module 12 is configured to take the target supply air demand interval as an index to perform a tolerance interval retrieval on K variable frequency control parameters of the target screw air compressor, and determine K parameter tolerance intervals, wherein K is an integer greater than or equal to 1; an optimization module 13 is configured to collect real-time supply air pressure and K real-time variable frequency control parameters of the target screw air compressor, perform double-target optimization on the K real-time variable frequency control parameters in combination with the target supply air demand interval, and obtain K real-time adjustment variable frequency control parameters in the optimization process with the K parameter tolerance intervals as constraints, wherein the double-target optimization is to reduce the difference between the real-time supply air pressure and the target supply air demand interval and reduce the energy consumption of the target screw air compressor; and a control module 14 is configured to transmit the K real-time adjustment variable frequency control parameters to a control module, and perform variable frequency control on the target screw air compressor by using the control module.

[0050] Further, the analysis module 11 is configured to perform the following method:

[0051] The analysis module 11 is configured to perform the following method:

[0052] Further, the analysis module 11 is configured to perform the following method:

[0053] A two-dimensional demand fluctuation analysis space is constructed with time as the horizontal coordinate axis and gas supply demand as the vertical coordinate axis; the gas supply demand sequence is input into the two-dimensional demand fluctuation analysis space to obtain a plurality of analysis space points, wherein each analysis space point corresponds to the gas supply demand at a time point; a straight line passing through the starting point and parallel to the horizontal coordinate axis of the two-dimensional demand fluctuation analysis space is used as a starting straight line; the starting straight line is iteratively analyzed according to the analysis iteration step to determine a target iteration straight line; an analysis space point located above the target iteration straight line and having a distance to the target iteration straight line of the analysis iteration step is used as a first interval endpoint; an analysis space point located below the target iteration straight line and having a distance to the target iteration straight line of the analysis iteration step is used as a second interval endpoint; and the target gas supply demand interval is constructed according to the first interval endpoint and the second interval endpoint.

[0054] Further, the analysis module 11 is configured to perform the following method:

[0055] The number of analysis space points in the two-dimensional demand fluctuation analysis space having a distance to the starting straight line of the analysis iteration step is counted to obtain a starting distribution quantity; the starting straight line is moved upward and downward by the analysis iteration step to obtain a first upward moving iteration straight line and a first downward moving iteration straight line; it is determined whether the first upward moving distribution quantity of the first upward moving iteration straight line and the first downward moving distribution quantity of the first downward moving iteration straight line are both less than or equal to the starting distribution quantity; if yes, the starting straight line is used as the target iteration straight line.

[0056] Further, the analysis module 11 is configured to perform the following method:

[0057] If no, the first upward moving distribution quantity and the first downward moving distribution quantity are compared to determine which one is larger; the straight line corresponding to the larger distribution quantity is used as a stage iteration straight line, and the moving direction in which the stage iteration straight line is obtained is used as an iteration moving direction; the stage iteration straight line is moved in the iteration moving direction by the analysis iteration step; after a plurality of moving iterations, when the distribution quantity obtained in the current iteration is smaller than the distribution quantity obtained in the last iteration, the iteration is stopped, and the stage iteration straight line obtained in the last iteration is used as the target iteration straight line.

[0058] Further, the optimization module 13 is configured to perform the following method:

[0059] According to the deviation value of the real-time supply pressure from the interval median value of the target supply demand interval, a seeking adjustment range is determined, wherein the seeking adjustment range is a parameter range for adjusting the K real-time variable frequency control parameters at one time; the K real-time variable frequency control parameters are randomly adjusted according to the seeking adjustment range to obtain K first adjustment variable frequency control parameters; the control effect of the K first adjustment variable frequency control parameters is analyzed by using an adjustment fitness analyzer to obtain a first adjustment fitness; the K real-time variable frequency control parameters are randomly adjusted again according to the seeking adjustment range to obtain K second adjustment variable frequency control parameters; the control effect of the K second adjustment variable frequency control parameters is analyzed by using the adjustment fitness analyzer to obtain a second adjustment fitness; whether the first adjustment fitness is less than or equal to the second adjustment fitness is judged, and if yes, the K second adjustment variable frequency control parameters are taken as K stage adjustment variable frequency control parameters; through multiple iterative analysis, when a preset iteration number is met, K stage adjustment variable frequency control parameters corresponding to a maximum adjustment fitness are taken as K real-time adjustment variable frequency control parameters.

[0060] Further, the seeking module 13 is configured to perform the following method:

[0061] The random adjustment is to increase or decrease N real-time variable frequency control parameters in the K real-time variable frequency control parameters according to the seeking adjustment range, and N is an integer less than or equal to K.

[0062] 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. The above-mentioned specific embodiments of the present application are described. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

[0063] The above-mentioned only for the preferred embodiments of the present application, and not to limit the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application, should be included in the protection scope of the present application.

[0064] The specification and drawings are merely exemplary of the present application, and any and all modifications, variations, combinations or equivalents that fall within the scope of the present application are considered to be covered by the present application. Obviously, those skilled in the art can 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 belong to the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A two-stage compression variable frequency control method for a screw air compressor, characterized in that, The method includes: Obtain the air supply demand sequence of the target screw air compressor within the preset monitoring window, perform demand fluctuation analysis on the air supply demand sequence, and determine the target air supply demand range; Using the target gas supply demand range as an index, the tolerance range of K variable frequency control parameters of the target screw air compressor is retrieved to determine the tolerance range of K parameters, where K is an integer greater than or equal to 1; The real-time air supply pressure of the target screw air compressor and K real-time variable frequency control parameters are collected. The K real-time variable frequency control parameters are then optimized in a dual-objective manner in combination with the target air supply demand range. During the optimization process, the tolerance range of the K parameters is used as a constraint to obtain K real-time adjustable variable frequency control parameters. The dual-objective optimization is to reduce the difference between the real-time air supply pressure and the target air supply demand range and to reduce the energy consumption of the target screw air compressor. The K real-time adjustable frequency conversion control parameters are transmitted to the control module, and the control module is used to perform frequency conversion control on the target screw air compressor.

2. The two-stage compression variable frequency control method for a screw air compressor as described in claim 1, characterized in that, Obtain the air supply demand sequence of the target screw air compressor within a preset monitoring window, perform demand fluctuation analysis on the air supply demand sequence, and determine the target air supply demand range, including: The average gas demand is analyzed by traversing the gas demand sequence to obtain the average gas demand. The gas supply and demand sequence is traversed to perform demand fluctuation analysis and determine the demand fluctuation factor; The analysis iteration step size is determined based on the demand fluctuation factor and the difference between the maximum and minimum gas demand values ​​in the gas demand sequence. Starting from the average gas demand, the gas demand sequence is subjected to demand fluctuation analysis according to the analysis iteration step size to determine the target gas demand range.

3. The two-stage compression variable frequency control method for a screw air compressor as described in claim 2, characterized in that, Starting with the average gas demand, the gas demand sequence is subjected to demand fluctuation analysis according to the analysis iteration step size to determine the target gas demand range. The method includes: A two-dimensional demand fluctuation analysis space is constructed with time as the horizontal axis and gas supply demand as the vertical axis; The gas supply demand sequence is input into the two-dimensional demand fluctuation analysis space to obtain multiple analysis space points, wherein each analysis space point corresponds to the gas supply demand at a certain time point. The starting line is a straight line that passes through the starting point and is parallel to the horizontal axis of the two-dimensional demand fluctuation analysis space. The initial straight line is iteratively analyzed according to the analysis iteration step size to determine the target iterative straight line; The analysis space point located above the target iteration line and at a distance equal to the analysis iteration step size from the target iteration line is designated as the endpoint of the first interval. The analysis space point located below the target iteration line and whose distance from the target iteration line is the analysis iteration step size is taken as the endpoint of the second interval; The target gas supply demand range is constructed based on the endpoints of the first and second intervals.

4. The two-stage compression variable frequency control method for a screw air compressor as described in claim 3, characterized in that, The method involves iteratively analyzing the initial straight line according to the stated analysis iteration step size to determine the target iterative straight line, the method comprising: The number of analysis space points in the two-dimensional demand fluctuation analysis space whose distance to the starting straight line is equal to the analysis iteration step size is counted to obtain the initial distribution quantity; According to the analysis iteration step size, the starting straight line is moved upward and downward respectively to obtain the first upward iteration line and the first downward iteration line; Determine whether the first upward shift distribution of the first upward shifting iteration line and the first downward shift distribution of the first downward shifting iteration line are both less than or equal to the initial distribution. If so, then the initial line is taken as the target iteration line.

5. The two-stage compression variable frequency control method for a screw air compressor as described in claim 4, characterized in that, The method includes: If not, compare the magnitudes of the first upward shift distribution and the first downward shift distribution, take the line corresponding to the larger distribution as the stage iteration line, and take the direction of movement of the obtained stage iteration line as the iteration movement direction; The stage iteration line is moved in the iteration direction according to the analysis iteration step size. After multiple iterations, the iteration stops when the distribution obtained in the current iteration is less than the distribution obtained in the previous iteration, and the stage iteration line obtained in the previous iteration is taken as the target iteration line.

6. The two-stage compression variable frequency control method for a screw air compressor as described in claim 1, characterized in that, The method includes: The optimization adjustment range is determined based on the deviation between the real-time gas supply pressure and the midpoint of the target gas supply demand range, wherein the optimization adjustment range is the parameter range of a single adjustment to the K real-time frequency conversion control parameters. The K real-time frequency converter control parameters are randomly adjusted according to the optimization adjustment range to obtain K first adjusted frequency converter control parameters; The first adjustment fitness is obtained by using an adjustment fitness analyzer to analyze the control effect of the K first adjustment frequency converter control parameters. The K real-time frequency converter control parameters are randomly adjusted again according to the optimization adjustment range to obtain K second adjusted frequency converter control parameters; The fitness analyzer is used to perform fitness analysis on the control effect of the K second adjustable frequency converter control parameters to obtain the second adjustment fitness. Determine whether the first adjustment fitness is less than or equal to the second adjustment fitness; if so, use the K second adjustment frequency converter control parameters as K stage adjustment frequency converter control parameters. After multiple iterative analyses, until the preset number of iterations is met, the K stage frequency converter control parameters corresponding to the maximum fitness value are adjusted as the K real-time frequency converter control parameters.

7. The two-stage compression variable frequency control method for a screw air compressor as described in claim 6, characterized in that, The random adjustment involves increasing or decreasing N of the K real-time frequency conversion control parameters according to the optimization adjustment range, where N is an integer less than or equal to K.

8. A two-stage compression variable frequency control device for a screw air compressor, characterized in that, The apparatus for implementing the two-stage compression variable frequency control method for the screw air compressor according to any one of claims 1-7 comprises: The analysis module is used to obtain the air supply demand sequence of the target screw air compressor within a preset monitoring window, perform demand fluctuation analysis on the air supply demand sequence, and determine the target air supply demand range. The retrieval module is used to retrieve the tolerance range of K variable frequency control parameters of the target screw air compressor using the target air supply demand range as an index, and determine the tolerance range of K parameters, where K is an integer greater than or equal to 1; The optimization module is used to collect the real-time air supply pressure and K real-time variable frequency control parameters of the target screw air compressor, and perform dual-objective optimization on the K real-time variable frequency control parameters in combination with the target air supply demand range. During the optimization process, the tolerance range of the K parameters is used as a constraint to obtain K real-time adjustable variable frequency control parameters. The dual-objective optimization is to reduce the difference between the real-time air supply pressure and the target air supply demand range and to reduce the energy consumption of the target screw air compressor. The control module is used to transmit the K real-time adjustable frequency conversion control parameters to the control module, and use the control module to perform frequency conversion control on the target screw air compressor.

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