Double-stage compression frequency conversion control method and device for screw air compressor

Abstract

The application discloses a double-stage compression variable frequency control method and device of a screw air compressor and relates to the technical field of intelligent control. The method comprises the following steps: performing demand fluctuation analysis on a gas supply demand sequence to determine a target gas supply demand interval; performing tolerance interval retrieval on K variable frequency control parameters of a target screw air compressor to determine K parameter tolerance intervals; collecting real-time gas supply pressure and K real-time variable frequency control parameters of the target screw air compressor; performing double-target optimization on the K real-time variable frequency control parameters in combination with the target gas supply demand interval to obtain K real-time adjustment variable frequency control parameters; and transmitting the K real-time adjustment variable frequency control parameters to a control module to perform variable frequency control on the target screw air compressor by using the control module. The method solves 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, and achieves the technical effects of improving gas supply stability, reducing energy consumption and optimizing variable frequency control parameter adjustment efficiency.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, specifically to a two-stage compression variable frequency control method and device for screw air compressors. Background Technology

[0002] Currently, screw air compressors are widely used in industrial production and air supply systems, primarily to provide a stable supply of compressed air. However, existing air compressor control technologies often employ fixed control strategies when faced with dynamically changing air supply demands, making it difficult to achieve precise responses to fluctuating requirements. This results in low air supply system efficiency, increased energy consumption, and shortened equipment lifespan. Furthermore, due to the lack of effective parameter optimization and real-time adjustment mechanisms, existing air compressor systems are prone to unstable air supply pressure when air supply demand fluctuates, affecting overall production efficiency and stability. Summary of the Invention

[0003] 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.

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

[0005] The first aspect of this application provides a two-stage compression variable frequency control method for a screw air compressor, the method comprising:

[0006] The system acquires the air supply demand sequence of the target screw air compressor within a preset monitoring window, performs demand fluctuation analysis on the air supply demand sequence, and determines the target air supply demand range. Using the target air supply demand range as an index, it performs tolerance range retrieval on K variable frequency control parameters of the target screw air compressor to determine K parameter tolerance ranges, where K is an integer greater than or equal to 1. It collects the real-time air supply pressure and K real-time variable frequency control parameters of the target screw air compressor, and performs dual-objective optimization on the K real-time variable frequency control parameters in conjunction with the target air supply demand range. During the optimization process, it uses the K parameter tolerance ranges as constraints to obtain K real-time adjustable variable frequency control parameters. The dual-objective optimization aims to reduce the difference between the real-time air supply pressure and the target air supply demand range and reduce the energy consumption of the target screw air compressor. The K real-time adjustable variable frequency control parameters are transmitted to the control module, which is used to perform variable frequency control on the target screw air compressor.

[0007] A second aspect of this application provides a two-stage compression variable frequency control device for a screw air compressor, the device comprising:

[0008] The analysis module acquires the air supply demand sequence of the target screw air compressor within a preset monitoring window, performs demand fluctuation analysis on the air supply demand sequence, and determines the target air supply demand range. The retrieval module uses the target air supply demand range as an index to retrieve the tolerance ranges of K variable frequency control parameters of the target screw air compressor, determining the tolerance ranges of K parameters, where K is an integer greater than or equal to 1. The optimization module collects the real-time air supply pressure and K real-time variable frequency... The control parameters are optimized by combining the target air supply demand range with the K real-time variable frequency control parameters for dual objectives. 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 objectives are 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 variable frequency control parameters to the control module and use the control module to perform variable frequency control on the target screw air compressor.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] First, the air supply demand sequence of the target screw air compressor within a preset monitoring window is obtained. Demand fluctuation analysis is performed on the air supply demand sequence to determine the target air supply demand range. Next, using the target air supply demand range as an index, the tolerance ranges of K variable frequency control parameters of the target screw air compressor are retrieved to determine K parameter tolerance ranges, where K is an integer greater than or equal to 1. Then, the real-time air supply pressure of the target screw air compressor and the K real-time variable frequency control parameters are collected. Combined with the target air supply demand range, a dual-objective optimization is performed on the K real-time variable frequency control parameters. During the optimization process, the K parameter tolerance ranges are used as constraints to obtain K real-time adjustable variable frequency control parameters. The dual-objective optimization aims 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. Finally, the K real-time adjustable variable frequency control parameters are transmitted to the control module, which then performs variable frequency control on the target screw air compressor. This invention solves the technical problem of screw air compressors being difficult to control efficiently and energy-savingly when air supply demand fluctuates, achieving the technical effects of improving air supply stability, reducing energy consumption, and optimizing the adjustment efficiency of variable frequency control parameters. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[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 gas demand sequence is traversed to perform average gas demand analysis to obtain the average gas demand; the gas demand sequence is traversed to perform demand fluctuation analysis to determine the demand fluctuation factor; based on the demand fluctuation factor and the difference between the maximum and minimum gas demand values ​​in the gas demand sequence, the analysis iteration step size is determined; 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.

[0023] Specifically, the gas demand sequence is iterated and accumulated for each data point to calculate the total gas demand for the entire sequence. This sum is then divided by the total number of data points to obtain the average gas demand. The variance of each data point relative to the average gas demand is calculated to obtain the demand fluctuation factor. The maximum and minimum values ​​in the gas demand sequence are determined, and the difference between them is calculated, representing the overall range of gas demand. The analysis iteration step size can be obtained by dividing the difference by an empirical coefficient, which is related to the fluctuation factor, ensuring that the step size is sufficiently fine to effectively capture fluctuation characteristics. The average gas demand is used as the starting point for the analysis. Starting from the average gas demand, the gas demand value is gradually increased or decreased according to the determined analysis iteration step size, while calculating the gas demand frequency or probability corresponding to each value. Based on the distribution of the gas demand frequency or probability, a suitable interval is selected as the target gas demand interval.

[0024] Furthermore, 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:

[0025] 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, where each analysis space point corresponds to the gas supply demand at a given time point. A straight line passing through the starting point and parallel to the horizontal axis of the two-dimensional demand fluctuation analysis space is taken as the starting straight line. The starting straight line is iteratively analyzed according to the analysis iteration step size to determine the target iteration straight line. The analysis space point located above the target iteration straight line and at a distance equal to the analysis iteration step size from the target iteration straight line is taken as the first interval endpoint. The analysis space point located below the target iteration straight line and at a distance equal to the analysis iteration step size from the target iteration straight line is taken as the second interval endpoint. The target gas supply demand interval is constructed based on the first interval endpoint and the second interval endpoint.

[0026] Preferably, a two-dimensional plane is established with time as the horizontal axis and gas demand as the vertical axis to visually represent the changes in gas demand over time. Each data point in the gas demand sequence (including the time point and the corresponding gas demand amount) is input into the two-dimensional demand fluctuation analysis space, with each data point corresponding to an analysis space point. The average gas demand is taken as the starting point, and its position on the vertical axis represents the average gas demand amount. In the two-dimensional demand fluctuation analysis space, a straight line passing through the starting point and parallel to the horizontal axis (time axis) is drawn as the starting line. Starting from the starting line, the analysis is iterated up and down according to the analysis iteration step size. Each iteration will obtain a new straight line until a specific stopping condition is met (such as reaching a preset number of iterations, or the position of the straight line and the distribution characteristics of the gas demand sequence). (Consistent); Based on the iteration results, select the straight line that best represents the fluctuation characteristics of gas supply demand as the target iteration line; Above the target iteration line, find an analysis space point that is equal to the analysis iteration step size at a distance from the target iteration line as the endpoint of the first interval. If there are multiple points that meet the conditions, select the one closest to the target iteration line; Below the target iteration line, similarly find an analysis space point that is equal to the analysis iteration step size at a distance from the target iteration line as the endpoint of the second interval. Similarly, if there are multiple points that meet the conditions, select the one closest to the target iteration line; Based on the found endpoints of the first and second intervals, determine the range of the target gas supply demand interval; The target gas supply demand interval is defined by the positions of the endpoints of the first and second intervals on the vertical axis (i.e., the gas supply demand).

[0027] Furthermore, the method of iteratively analyzing the initial straight line according to the stated analysis iteration step size to determine the target iterative straight line includes:

[0028] 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 amount; the starting straight line is moved upward and downward according to the analysis iteration step size to obtain the first upward iteration line and the first downward iteration line; it is determined whether the first upward distribution amount of the first upward iteration line and the first downward distribution amount of the first downward iteration line are both less than or equal to the initial distribution amount. If so, the starting straight line is taken as the target iteration line.

[0029] Preferably, in the two-dimensional demand fluctuation analysis space, the number of all analysis space points whose distance to the starting straight line is equal to the analysis iteration step size is counted. The number of these points is the initial distribution quantity, which is used to evaluate the distribution of gas supply demand on the initial horizontal line. The starting straight line is shifted upward according to the analysis iteration step size to obtain the first upward iteration line. Similarly, the starting straight line is shifted downward according to the analysis iteration step size to obtain the first downward iteration line. The number of analysis space points whose distance to the first upward iteration line and the first downward iteration line is equal to the analysis iteration step size is counted respectively to obtain the first upward distribution quantity and the first downward distribution quantity. It is determined whether the first upward distribution quantity and the first downward distribution quantity are both less than or equal to the initial distribution quantity. If both are less than or equal to the initial distribution quantity, it means that the starting straight line is already in a relatively balanced position, and the distribution of analysis space points on its upper and lower sides is relatively uniform. Therefore, the starting straight line can be used as the target iteration line.

[0030] Furthermore, the methods include:

[0031] 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;

[0032] 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.

[0033] Preferably, if the first upward shift and the first downward shift of the initial straight line are not both less than or equal to the initial distribution, then the two are compared; the line corresponding to the larger distribution (the first upward shift iteration line or the first downward shift iteration line) is taken as the stage iteration line; the iteration direction is determined according to the source of the stage iteration line (the first upward shift iteration line or the first downward shift iteration line); if the stage iteration line comes from the first upward shift iteration line, the iteration direction is upward; if it comes from the first downward shift iteration line, the iteration direction is downward; according to the analysis iteration step size, the stage iteration line is moved in the iteration direction to obtain a new stage iteration line; the distance between the upper and lower sides of the new stage iteration line is the analysis space point of the analysis iteration step size. The quantity, i.e., the new distribution quantity; compare the distribution quantity obtained in the current iteration with the distribution quantity obtained in the previous iteration; if the distribution quantity obtained in the current iteration is less than the distribution quantity obtained in the previous iteration, it means that the iteration direction is correct and close to the target iteration line, and continuing the iteration may cause the distribution quantity to decrease further; if the distribution quantity obtained in the current iteration is not less than the distribution quantity obtained in the previous iteration (i.e., the distribution quantity has not decreased or has increased instead), it means 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 stopping condition is met (i.e., the distribution quantity obtained in the current iteration is less than the distribution quantity obtained in the previous iteration, but it needs to be judged before the next iteration), the iteration stops; take the stage iteration line obtained in the previous iteration as the target iteration line.

[0034] 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.

[0035] The target screw air compressor has K variable frequency control parameters, including supply air pressure, inverter frequency, interstage cooling temperature, exhaust temperature, minimum starting pressure, unloading pressure, intake temperature, and intake humidity. Using the target air supply demand range as an index, the K variable frequency control parameters related to this range are retrieved from the screw air compressor. Based on historical operating data, experimental results, or preset parameter ranges, the K parameters are analyzed to determine the upper and lower limits (i.e., tolerance ranges) of each parameter, ultimately obtaining the tolerance ranges for the K parameters.

[0036] 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, taking the tolerance range of the K parameters as a constraint during the optimization process, to obtain K real-time adjustable variable frequency control parameters. The dual-objective optimization aims 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.

[0037] The system collects real-time air supply pressure data and K variable frequency control parameters from the target screw air compressor. It compares the real-time collected air supply pressure and variable frequency control parameters with the determined target air supply demand range to analyze whether the air supply pressure deviates from the demand range. The core objectives of the dual-objective optimization are to reduce the difference between the real-time air supply pressure and the target air supply demand range, and to reduce the air compressor's energy consumption. A suitable algorithm for multi-objective optimization, such as a multi-objective genetic algorithm or particle swarm optimization algorithm, is selected to find the optimal value under the constraints of the dual objective function and parameter tolerance range. Through iterative calculation, one or more sets of variable frequency control parameter values ​​that satisfy the constraints and can simultaneously optimize both objective functions are found. After optimization, K real-time adjusted variable frequency control parameters are output; these parameters are the optimal values ​​obtained after balancing air supply demand and energy consumption.

[0038] Furthermore, the methods include:

[0039] Based on the deviation between the real-time gas supply pressure and the midpoint of the target gas supply demand range, an optimal adjustment range is determined, wherein the optimal adjustment range is the parameter range of a single adjustment to the K real-time frequency converter control parameters. The K real-time frequency converter control parameters are randomly adjusted according to the optimal adjustment range to obtain K first adjustable frequency converter control parameters. An adjustment fitness analyzer is used to perform fitness analysis on the control effect of the K first adjustable frequency converter control parameters to obtain a first adjustment fitness. The K real-time frequency converter control parameters are randomly adjusted again according to the optimal adjustment range to obtain K second adjustable frequency converter control parameters. An adjustment fitness analyzer is used to perform fitness analysis on the control effect of the K second adjustable frequency converter control parameters to obtain a second adjustment fitness. It is determined whether the first adjustment fitness is less than or equal to the second adjustment fitness. If so, the K second adjustable frequency converter control parameters are used as K stage adjustable frequency converter control parameters. After multiple iterative analyses until a preset number of iterations is met, the K stage adjustable frequency converter control parameters corresponding to the maximum adjustment fitness are used as K real-time adjustable frequency converter control parameters.

[0040] Preferably, the deviation between the real-time gas supply pressure and the midpoint of the target gas supply demand range is calculated. Based on the magnitude of the deviation, the parameter range for adjusting K real-time frequency converter control parameters in a single operation is determined, i.e., the optimal adjustment range. The magnitude of the optimal adjustment range is proportional to the deviation, thereby ensuring the effectiveness and stability of the adjustment. According to the optimal adjustment range, the K real-time frequency converter control parameters are randomly adjusted to obtain K first-adjustment frequency converter control parameters. Using an adjustment fitness analyzer, the control effect of the K first-adjustment frequency converter control parameters is analyzed to obtain the first adjustment fitness. The K real-time frequency converter control parameters are randomly adjusted again according to the optimal adjustment range to obtain K second-adjustment frequency converter control parameters. The fitness of the K second-adjustment frequency converter control parameters is analyzed to obtain the second adjustment fitness. It is then determined whether the first adjustment fitness is less than or equal to the second adjustment fitness. If so, it indicates that the second adjustment was more effective, and adjustments should continue in this direction. If the second adjustment has better fitness, then the K second-adjustment frequency converter control parameters are used as the new K-stage adjustment frequency converter control parameters. Repeat the above steps of random adjustment and fitness analysis for multiple iterations. In each iteration, the new adjustment direction and parameter values ​​are determined based on the previous result. During the iteration process, a preset number of iterations is set as a stopping condition. When the preset number of iterations is reached, the iteration stops, and the K-stage adjustment frequency converter control parameters corresponding to the maximum adjustment fitness value are selected from all iteration results as the final K real-time adjustment frequency converter control parameters.

[0041] The main function of the fitness analyzer is to evaluate the adjusted variable frequency control parameters during the dual-objective optimization process, analyzing whether these parameters can meet the dual objectives of reducing supply pressure differences and reducing energy consumption. The fitness analyzer receives K input adjusted variable frequency control parameters, as well as the collected real-time supply pressure and target supply demand range. It performs fitness evaluation based on supply pressure deviation and energy consumption assessment. Supply pressure deviation refers to the deviation between the calculated supply pressure under the adjusted parameters and the median of the target supply demand range; the fitness score is inversely proportional to the deviation, with a smaller deviation resulting in a higher fitness score. Energy consumption assessment refers to calculating the impact of the adjusted parameters on the screw air compressor's energy consumption; lower energy consumption results in a higher fitness score. The fitness formula is: Fitness = w1(1 / Supply Pressure Deviation) + w2(1 / Energy Consumption), where w1 and w2 are weighting factors for fitness, used to balance the priority of supply pressure and energy consumption optimization.

[0042] Furthermore, 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.

[0043] Random adjustment is an operation performed on a subset of K real-time variable frequency control parameters (i.e., N real-time variable frequency control parameters, where N is less than or equal to K). These parameters are randomly increased or decreased based on the optimization adjustment range. 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 achieved by generating a random number (such as 0 or 1), where 0 indicates decrease and 1 indicates increase.

[0044] 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.

[0045] K real-time adjustable variable frequency control parameters, determined after multiple iterations and optimization analyses, are transmitted to the control module. Upon receiving these parameters, the control module immediately adjusts the parameters of the target screw air compressor's inverter. The control module regulates the air compressor's supply pressure by adjusting parameters (such as frequency and speed) to match the current supply demand range. The control module then applies the parameter adjustment results to the air compressor motor in real time via the inverter, changing the motor's operating state (such as acceleration or deceleration). By transmitting the K real-time adjustable variable frequency control parameters to the control module and implementing variable frequency control, precise adjustment of the screw air compressor's supply pressure and effective optimization of energy consumption are achieved, meeting the high-efficiency operation requirements of industrial applications.

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

[0047] First, the air supply demand sequence of the target screw air compressor within a preset monitoring window is obtained. Demand fluctuation analysis is performed on the air supply demand sequence to determine the target air supply demand range. Next, using the target air supply demand range as an index, the tolerance ranges of K variable frequency control parameters of the target screw air compressor are retrieved to determine K parameter tolerance ranges, where K is an integer greater than or equal to 1. Then, the real-time air supply pressure of the target screw air compressor and the K real-time variable frequency control parameters are collected. Combined with the target air supply demand range, a dual-objective optimization is performed on the K real-time variable frequency control parameters. During the optimization process, the K parameter tolerance ranges are used as constraints to obtain K real-time adjustable variable frequency control parameters. The dual-objective optimization aims 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. Finally, the K real-time adjustable variable frequency control parameters are transmitted to the control module, which then performs variable frequency control on the target screw air compressor. This invention solves the technical problem of screw air compressors being difficult to control efficiently and energy-savingly when air supply demand fluctuates, achieving the technical effects of improving air supply stability, reducing energy consumption, and optimizing the adjustment efficiency of variable frequency control parameters.

[0048] Example 2, based on the same inventive concept as the two-stage compression variable frequency control method for the screw air compressor in the previous examples, such as... Figure 2 As shown, this application provides a two-stage compression variable frequency control device for a screw air compressor, wherein the device includes:

[0049] Analysis module 11 is used to acquire 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; retrieval module 12 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; optimization module 13 is used to collect the real-time air supply pressure and K parameters of the target screw air compressor. The real-time variable frequency control parameters are optimized in a dual-objective manner by combining the target air supply demand range with the K real-time variable frequency control parameters. 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 14 is used to transmit the K real-time adjustable variable frequency control parameters to the control module and use the control module to perform variable frequency control on the target screw air compressor.

[0050] Furthermore, the analysis module 11 is used to perform the following methods:

[0051] The gas demand sequence is traversed to perform average gas demand analysis to obtain the average gas demand; the gas demand sequence is traversed to perform demand fluctuation analysis to determine the demand fluctuation factor; based on the demand fluctuation factor and the difference between the maximum and minimum gas demand values ​​in the gas demand sequence, the analysis iteration step size is determined; 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.

[0052] Furthermore, the analysis module 11 is used to perform the following methods:

[0053] 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, where each analysis space point corresponds to the gas supply demand at a given time point. A straight line passing through the starting point and parallel to the horizontal axis of the two-dimensional demand fluctuation analysis space is taken as the starting straight line. The starting straight line is iteratively analyzed according to the analysis iteration step size to determine the target iteration straight line. The analysis space point located above the target iteration straight line and at a distance equal to the analysis iteration step size from the target iteration straight line is taken as the first interval endpoint. The analysis space point located below the target iteration straight line and at a distance equal to the analysis iteration step size from the target iteration straight line is taken as the second interval endpoint. The target gas supply demand interval is constructed based on the first interval endpoint and the second interval endpoint.

[0054] Furthermore, the analysis module 11 is used to perform the following methods:

[0055] 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 amount; the starting straight line is moved upward and downward according to the analysis iteration step size to obtain the first upward iteration line and the first downward iteration line; it is determined whether the first upward distribution amount of the first upward iteration line and the first downward distribution amount of the first downward iteration line are both less than or equal to the initial distribution amount. If so, the starting straight line is taken as the target iteration line.

[0056] Furthermore, the analysis module 11 is used to perform the following methods:

[0057] 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 moving direction of the obtained stage iteration line as the iteration moving direction; move the stage iteration line in the iteration moving direction according to the analysis iteration step size, and after multiple moving iterations, stop the iteration when the distribution obtained in the current iteration is less than the distribution obtained in the previous iteration, and take the stage iteration line obtained in the previous iteration as the target iteration line.

[0058] Furthermore, the optimization module 13 is used to perform the following method:

[0059] Based on the deviation between the real-time gas supply pressure and the midpoint of the target gas supply demand range, an optimal adjustment range is determined, wherein the optimal adjustment range is the parameter range of a single adjustment to the K real-time frequency converter control parameters. The K real-time frequency converter control parameters are randomly adjusted according to the optimal adjustment range to obtain K first adjustable frequency converter control parameters. An adjustment fitness analyzer is used to perform fitness analysis on the control effect of the K first adjustable frequency converter control parameters to obtain a first adjustment fitness. The K real-time frequency converter control parameters are randomly adjusted again according to the optimal adjustment range to obtain K second adjustable frequency converter control parameters. An adjustment fitness analyzer is used to perform fitness analysis on the control effect of the K second adjustable frequency converter control parameters to obtain a second adjustment fitness. It is determined whether the first adjustment fitness is less than or equal to the second adjustment fitness. If so, the K second adjustable frequency converter control parameters are used as K stage adjustable frequency converter control parameters. After multiple iterative analyses until a preset number of iterations is met, the K stage adjustable frequency converter control parameters corresponding to the maximum adjustment fitness are used as K real-time adjustable frequency converter control parameters.

[0060] Furthermore, the optimization module 13 is used to perform the following method:

[0061] 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.

[0062] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0063] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0064] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A 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. 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 supply 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. 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. 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.

2. The two-stage compression variable frequency control method for a screw air compressor as described in claim 1, 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.

3. The two-stage compression variable frequency control method for a screw air compressor as described in claim 2, 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.

4. The two-stage compression variable frequency control method for a screw air compressor as described in claim 3, 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.

5. 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 1-4 includes: 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.

Images