Compressed air energy storage compressor all-condition modeling method

Abstract

The invention relates to the technical field of compressors, in particular to a compressed air energy storage compressor all-working-condition modeling method which comprises the steps that the modeling requirement of a compressor to be modeled is obtained; determining a first rotating speed performance curve of the to-be-modeled compressor, determining at least one state variable according to the modeling demand, building an initial compressor model based on the at least one state variable and a preset constraint relationship, and operating the initial compressor model to obtain a second rotating speed performance curve of the to-be-modeled compressor; a first performance curve is determined according to the first rotating speed performance curve, a second performance curve is determined according to the second rotating speed performance curve, a third performance curve is determined according to the first performance curve and the second performance curve, and the performance curve of the full-working-condition rated rotating speed is obtained through combination. Therefore, the problems of inaccurate modeling, poor universality and low economical efficiency of the compressor are solved, the method is simple and high in economical efficiency, and all-working-condition modeling can be carried out on the compressor with unknown performance.

Description

Technical Field

[0001] The present application relates to the technical field of compressors, and in particular to a method for modeling full-operating conditions of a compressed air energy storage compressor. Background Art

[0002] Since wind and solar power generation have significant volatility and uncertainty, large-scale energy storage technology is particularly important. Among them, compressed air energy storage technology has become a research hotspot due to its advantages such as large capacity, long energy storage cycle and high efficiency.

[0003] When designing and evaluating a compressed air energy storage system, it is crucial to establish an accurate simulation model, especially to model the full operating conditions of the compressor in the compressed air energy storage system to study the compressor startup process and subsequent variable operating conditions.

[0004] The modeling method of the compressor in the related art is to restrict the operating conditions and assume that the compressor only works at a higher speed, such as above 80% of the rated speed. At this time, the compressor can be modeled using a commonly used empirical model. This modeling method is relatively simple and convenient. Under certain circumstances, it can more accurately fit the isentropic efficiency and pressure ratio of the compressor and other parameters. It is suitable for modeling compressors working near the rated point. However, since the empirical model can only better give the characteristics of the compressor at a higher speed, it does not clearly and accurately reflect the characteristics of the compressor at a low speed, so corrections are needed.

[0005] The other is a compressor modeling method based on data fitting. By conducting real machine experiments on actual compressors, recording and obtaining parameters such as speed, mass flow, isentropic efficiency, pressure ratio, inlet and outlet temperatures, and inlet and outlet pressures, various characteristic curves are fitted using functions and other data are obtained using interpolation. The principle of this method is simple and easy to understand, and it can better characterize the characteristics of the compressor under all working conditions. However, due to the unit specificity, units of different manufacturers and models have different compressor characteristics and cannot be universal. In addition, the testing process is time-consuming and labor-intensive, and the experimental economy is very low. Experiments at high speeds also have a series of safety issues.

[0006] In summary, it is urgent to propose a simple and efficient full-operating condition modeling method for compressed air energy storage compressors. Summary of the invention

[0007] The present application provides a method for full-operating-condition modeling of a compressed air energy storage compressor to solve the problems of inaccurate compressor modeling, poor versatility and low economy in experimental modeling in a compressed air energy storage system. The modeling is relatively accurate, simple and highly economical, and can quickly perform full-operating-condition modeling on a compressor in a compressed air energy storage system with unknown performance.

[0008] The first aspect of the present application provides a method for modeling a compressed air energy storage compressor under full working conditions, comprising the following steps:

[0009] Obtain modeling requirements of the compressor to be modeled;

[0010] Determine a first speed performance curve of the compressor to be modeled, determine at least one state variable according to the modeling requirement, build an initial compressor model based on the at least one state variable and a preset constraint relationship, and run the initial compressor model to obtain a second speed performance curve of the compressor to be modeled; and

[0011] A first performance curve is determined according to the first speed performance curve, a second performance curve is determined according to the second speed performance curve, and a third performance curve is determined according to the first performance curve and the second performance curve, and the first performance curve, the third performance curve and the second performance curve are combined to obtain a performance curve of rated speed under all operating conditions, so as to model the compressor based on the performance curve of rated speed under all operating conditions.

[0012] Optionally, in some embodiments, determining a first speed performance curve of the compressor to be modeled includes:

[0013] Determining whether there is performance data in the modeling requirement;

[0014] If the performance data exists in the modeling requirement, the first speed performance curve is determined based on the performance data; otherwise, the first speed performance curve is obtained according to actual machine test results of the compressor to be modeled.

[0015] Optionally, in some embodiments, determining the first speed performance curve based on the performance data includes:

[0016] Determine a reference point and a surge boundary line corresponding to each speed according to the performance data, and connect the reference points corresponding to each speed to form a baseline;

[0017] The first speed performance curve is determined based on the baseline and the surge boundary line using a general characteristic of a compressor stage.

[0018] Optionally, in some embodiments, determining a first performance curve according to the first speed performance curve, determining a second performance curve according to the second speed performance curve, and determining a third performance curve according to the first performance curve and the second performance curve includes:

[0019] Selecting a first performance curve whose rated speed is less than a first threshold from the first speed performance curve;

[0020] A second performance curve having a rated speed greater than a second threshold and less than or equal to a third threshold is selected from the second speed performance curve, and a third performance curve having a rated speed greater than or equal to the first threshold and less than or equal to the second threshold is determined based on the first performance curve and the second performance curve.

[0021] Optionally, in some embodiments, the initial compressor model comprises:

[0022]

[0023] Among them, ρ out is the gas density at the compressor outlet, τ is the time variable, q m,in is the mass flow rate at the compressor inlet, q m,out is the mass flow rate at the compressor outlet, c out is the velocity of the gas at the compressor outlet, c in is the velocity of the gas at the compressor inlet, p in is the compressor inlet pressure, A in is the compressor inlet cross-sectional area, p out is the compressor outlet pressure, A out is the outlet cross-sectional area of ​​the compressor, h out is the specific enthalpy of the gas at the compressor outlet, c p is the specific heat capacity, Rg is the gas constant, T out is the outlet gas temperature, c is the gas velocity, and h is the specific enthalpy of the gas.

[0024] The second aspect of the present application provides a full-operating-condition modeling device for a compressed air energy storage compressor, comprising:

[0025] An acquisition module, used for acquiring modeling requirements of the compressor to be modeled;

[0026] a determination module, configured to determine a first speed performance curve of the compressor to be modeled, determine at least one state variable according to the modeling requirement, build an initial compressor model based on the at least one state variable and a preset constraint relationship, and run the initial compressor model to obtain a second speed performance curve of the compressor to be modeled; and

[0027] A modeling module is used to determine a first performance curve based on the first speed performance curve, determine a second performance curve based on the second speed performance curve, and determine a third performance curve based on the first performance curve and the second performance curve, combine the first performance curve, the third performance curve and the second performance curve to obtain a performance curve of the rated speed under all operating conditions, so as to model the compressor based on the performance curve of the rated speed under all operating conditions.

[0028] Optionally, in some embodiments, the determining module includes:

[0029] A judging unit, used to judge whether there is performance data in the modeling requirement;

[0030] A generating unit is used to determine the first speed performance curve based on the performance data when the performance data exists in the modeling requirement, otherwise obtain the first speed performance curve according to the actual machine test results of the compressor to be modeled.

[0031] Optionally, in some embodiments, the generating unit comprises:

[0032] A first determination subunit is used to determine a reference point and a surge boundary line corresponding to each speed according to the performance data, and connect the reference points corresponding to each speed to form a baseline;

[0033] The second determination subunit determines the first speed performance curve based on the baseline and the surge boundary line by using the general characteristics of the compressor stage.

[0034] Optionally, in some embodiments, the modeling module includes:

[0035] A first selection unit, configured to select a first performance curve having a rated speed less than a first threshold from the first speed performance curve;

[0036] A second selection unit is used to select a second performance curve whose rated speed is greater than a second threshold and less than or equal to a third threshold from the second speed performance curve, and determine a third performance curve whose rated speed is greater than or equal to the first threshold and less than or equal to the second threshold based on the first performance curve and the second performance curve.

[0037] Optionally, in some embodiments, the initial compressor model comprises:

[0038]

[0039] Among them, ρ out is the gas density at the compressor outlet, τ is the time variable, q m,in is the mass flow rate at the compressor inlet, q m,out is the mass flow rate at the compressor outlet, c out is the velocity of the gas at the compressor outlet, c un is the velocity of the gas at the compressor inlet, p in is the compressor inlet pressure, A in is the compressor inlet cross-sectional area, p out is the compressor outlet pressure, A out is the outlet cross-sectional area of ​​the compressor, h outis the specific enthalpy of the gas at the compressor outlet, c p is the specific heat capacity, Rg is the gas constant, T out is the outlet gas temperature, c is the gas velocity, and h is the specific enthalpy of the gas.

[0040] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for modeling the full working conditions of the compressed air energy storage compressor as described in the above embodiment.

[0041] The fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the method for modeling the full operating conditions of a compressed air energy storage compressor as described in the above embodiment.

[0042] Thus, by obtaining the modeling requirements of the compressor to be modeled, and determining the first speed performance curve of the compressor to be modeled, and determining at least one state variable according to the modeling requirements, and building an initial compressor model based on at least one state variable and a preset constraint relationship, and running the initial compressor model to obtain the second speed performance curve of the compressor to be modeled, and determining the first performance curve according to the first speed performance curve, and determining the second performance curve according to the second speed performance curve, and determining the third performance curve according to the first performance curve and the second performance curve, the performance curve of the rated speed under all working conditions is obtained by combination. Thus, the problems of inaccurate compressor modeling, poor versatility and low economy of experimental modeling in compressed air energy storage systems are solved, the modeling is more accurate, simple and economical, and the compressor in a compressed air energy storage system with unknown performance can be quickly modeled under all working conditions.

[0043] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0045] Figure 1 A flowchart of a method for modeling a compressed air energy storage compressor under full working conditions provided in accordance with an embodiment of the present application;

[0046] Figure 2 A schematic diagram of a compressor system provided according to an embodiment of the present application;

[0047] Figure 3 A schematic diagram of the principle of a method for modeling a compressed air energy storage compressor under all working conditions provided according to an embodiment of the present application;

[0048] Figure 4 A block diagram of a full-operating-condition modeling device for a compressed air energy storage compressor provided according to an embodiment of the present application;

[0049] Figure 5 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0050] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0051] The following describes the full-condition modeling method of the compressed air energy storage compressor of the embodiment of the present application with reference to the accompanying drawings. In view of the problems of inaccurate compressor modeling, poor versatility of experimental modeling and low economy in the compressed air energy storage system mentioned in the above background technology, the present application provides a full-condition modeling method for a compressed air energy storage compressor, in which the modeling requirements of the compressor to be modeled are obtained, and the first speed performance curve of the compressor to be modeled is determined, and at least one state variable is determined according to the modeling requirements, and an initial compressor model is built based on at least one state variable and a preset constraint relationship, and the initial compressor model is run to obtain the second speed performance curve of the compressor to be modeled, and the first performance curve is determined according to the first speed performance curve, and the second performance curve is determined according to the second speed performance curve, and the third performance curve is determined according to the first performance curve and the second performance curve, and the performance curve of the rated speed of the full working condition is obtained by combining. Thus, the problems of inaccurate compressor modeling, poor versatility of experimental modeling and low economy in the compressed air energy storage system are solved, and the modeling is more accurate, simple and economical, and the compressor in the compressed air energy storage system with unknown performance can be quickly modeled under full working conditions.

[0052] Specifically, Figure 1 A schematic flow chart of a method for modeling all operating conditions of a compressed air energy storage compressor provided in an embodiment of the present application.

[0053] like Figure 1 As shown, the full-operating condition modeling method of the compressed air energy storage compressor includes the following steps:

[0054] In step S101, the modeling requirement of the compressor to be modeled is obtained.

[0055] It should be noted that in compressed air energy storage systems, multi-stage compressors are usually used to gradually increase the pressure of the gas. Each compressor is driven by an independent motor, and the compressors are connected in sequence through pipelines to form a continuous compression process. Figure 2To simplify the analysis, the present embodiment can make the following assumptions: first, the gas inside the compressor is considered to be uniformly distributed and the lumped parameter method is used for modeling; second, the compression process is considered to be an adiabatic process without heat exchange; third, the adiabatic compression efficiency of the compressor is set to 0.8; fourth, the force exerted by the compressor impeller on the gas during startup and stable operation remains constant; fifth, since the startup process is transient, it is assumed that the heat exchanger does not work during the startup phase, and therefore the influence of the heat exchanger is not considered. Based on the above assumptions, a full-operating condition modeling method for the compressed air energy storage compressor of the present embodiment is proposed.

[0056] Specifically, the embodiments of the present application need to determine the specific design parameters of the compressor system, including the specifications of a single compressor and its corresponding motor. The compression system is generally a multi-stage design, that is, it contains two or more compressors and motors. It can start with a single level of compressor and motor, and then apply the same method to the design of compressors and motors at other levels.

[0057] Obtain the modeling requirements of the compressor to be modeled, and record the obtained information in the following form: the manufacturer and model of the compressor, the rated mass flow rate of the compressor is q m,0 , the rated speed of the compressor is n0, the rated pressure ratio of the compressor is ε0, the adiabatic efficiency of the compressor is η0, the volume of the compressor is V, and the inlet cross-sectional area of ​​the compressor is A in , the outlet cross-sectional area of ​​the compressor is A out , the compression force of the motor is F. Note that the above are all design parameters, that is, the parameters fixed after the compressor leaves the factory, which are different from the variables mentioned later.

[0058] In step S102, a first speed performance curve of the compressor to be modeled is determined, and at least one state variable is determined according to the modeling requirements, and an initial compressor model is built based on the at least one state variable and a preset constraint relationship, and the initial compressor model is run to obtain a second speed performance curve of the compressor to be modeled.

[0059] Next, how to determine the first speed performance curve at low speed is explained.

[0060] Furthermore, in some embodiments, determining a first speed performance curve of the compressor to be modeled includes: determining whether there is performance data in the modeling requirements; if there is performance data in the modeling requirements, determining the first speed performance curve based on the performance data, otherwise obtaining the first speed performance curve based on actual machine experimental results of the compressor to be modeled.

[0061] Furthermore, in some embodiments, a first speed performance curve is determined based on the performance data, including: determining a reference point and a surge boundary line corresponding to each speed according to the performance data, and connecting the reference points corresponding to each speed to form a baseline; based on the baseline and the surge boundary line, the first speed performance curve is determined using the general characteristics of the compressor stage.

[0062] If there are units of similar models from the same manufacturer or units of the same positioning from different manufacturers whose performance data can be obtained, that is, there are already designed compressor performance data, the low-speed performance of the compressor to be tested can be obtained according to the baseline estimation method.

[0063] First, the highest efficiency point at each speed is obtained, that is, the point with the highest isentropic efficiency at all speeds, and is taken as the reference point. Then the reference points are connected to form a baseline, and the surge boundary line is determined. Finally, the performance value of the entire flow range at the speed is calculated through the general characteristics of the stage at the same speed.

[0064] The formula for calculating the reference point position and surge boundary line is given below:

[0065]

[0066] W c ∝ε (k-1) / k -1;

[0067]

[0068] in, is the efficiency of the surge boundary under any condition i, a is a coefficient, usually taken as 0.7, is the compressor speed under the current working condition i, W c is the power output of the compressor, ε is the pressure ratio, k is the adiabatic index, ε su,i is the pressure ratio under the current working condition i, ε su,o is the reference point pressure ratio, n o is the reference point speed, G su,i is the surge flow rate under the current operating condition i, G su,o is the surge flow at the reference point.

[0069] It should be noted that if there is no performance data of the compressor unit available in the embodiment of the present application, a real machine experiment can be conducted on the compressor to be modeled. Since the performance at low speed needs to be obtained, the experimental safety is relatively high. A gas sensor is placed at the inlet and outlet of the compressor, and a speed sensor is connected to the shaft to measure the gas mass flow rate, temperature, speed and speed of the current experiment. According to the performance of the sensor, the measurement is performed at least once every 0.1 seconds, and the data is recorded in the computer. The drawing function of Excel can be used to display the low performance curve of the compressor, and the interpolation function of MATLAB can be used to obtain the working function of the current compressor under the entire low speed operation.

[0070] Next, how to determine the second speed performance curve at high speed is explained.

[0071] Specifically, the embodiment of the present application selects the gas density ρ inside the compressor to be modeled, the outlet gas velocity c out , outlet gas temperature T out As state variables, other variables can be represented by the above state variables. Specifically, there are the following relationships:

[0072]

[0073] Among them, q m represents the mass flow rate of the compressor, p represents the pressure, τ is the time variable, h is the specific enthalpy of the gas, W is the work done by the motor, c p is the specific heat capacity. The in subscript represents the inlet, and the out subscript represents the outlet.

[0074] Where W is given by:

[0075]

[0076]

[0077] Among them, the superscript · represents the converted variable, ε represents the pressure ratio, η represents the adiabatic compression efficiency, G represents the converted mass flow rate, N represents the converted speed, c1, c2, c3, c4 are intermediate parameters, c4 is 0.8, and q and m are 1.8.

[0078] After obtaining all the information of the compression system, you can build a compressor model in Simulink, select the gas density ρ inside the compressor, the outlet gas velocity c out , outlet gas temperature T out As the state variable of the compressor, use the integral module in simulink and other operation symbols to build a compressor system. The specific method is to integrate the first-order differential of the state variable (that is, use the integration module ) to obtain the state variables, and then obtain the constraint relationship from the mass conservation formula, momentum conservation formula and energy conservation formula. The specific formula is as follows:

[0079]

[0080] The meaning of each quantity has been given in step S101 and will not be repeated here.

[0081] After building the model, it is necessary to give initial values ​​to the state variables. Here, the initial values ​​of the key dynamic parameters of the compressor to be modeled are given, that is, the density, speed and temperature are set to 1.184 kg / m 3 , 0m / s and 298.15K. These parameters can be set in the integration module in simulink. Open the integration module Set the above initial values ​​and confirm. The startup process parameter setting is now completed.

[0082] Then run the current model, and use the scope module to monitor or view the internal parameters of the current compressor, such as speed, gas temperature, mass flow rate, etc. When the model is finished running, download all the parameters and variables of the current model, and construct the speed-isentropic efficiency diagram and speed-pressure ratio diagram of the current compressor, etc. All the data and quantitative relationships required to describe the high-speed performance of the compressor.

[0083] In step S103, a first performance curve is determined according to the first speed performance curve, a second performance curve is determined according to the second speed performance curve, and a third performance curve is determined according to the first performance curve and the second performance curve, and the first performance curve, the third performance curve and the second performance curve are combined to obtain a performance curve of the rated speed under all operating conditions, so as to model the compressor based on the performance curve of the rated speed under all operating conditions.

[0084] Optionally, in some embodiments, a first performance curve is determined based on the first speed performance curve, and a second performance curve is determined based on the second speed performance curve, and a third performance curve is determined based on the first performance curve and the second performance curve, including: selecting a first performance curve whose rated speed is less than a first threshold from the first speed performance curve; selecting a second performance curve whose rated speed is greater than the second threshold and less than or equal to the third threshold from the second speed performance curve, and determining a third performance curve whose rated speed is greater than or equal to the first threshold and less than or equal to the second threshold based on the first performance curve and the second performance curve.

[0085] The first threshold may be 40%, the second threshold may be 60%, and the third threshold may be 100%.

[0086] Specifically, after obtaining the low-speed performance and high-speed performance of the compressed air energy storage compressor characteristics to be determined, the embodiment of the present application selects the first performance curve estimated by the low-speed performance of the compressor to be modeled, of which the portion below 40% of the rated speed is used as the low-speed performance curve of the compressor, and the portion above 60% of the rated speed in the second speed performance curve is used as the high-speed performance curve of the compressor to be modeled, and the curve between 40% and 60% of the rated speed is composed of the average value of the curves at both ends, so as to make the curve smoother, and finally the performance curve of the compressor from 0 to 100% of the rated speed is obtained, and the full-condition modeling of the compressor is completed.

[0087] In summary, if Figure 3 As shown, the full-operating condition modeling method of the compressed air energy storage compressor of the embodiment of the present application includes two cases: the characteristics of the same type of units are known and the characteristics of the same type are unknown. When the low-speed characteristics of the same type of units are known, the baseline estimation method is used to obtain the low-speed performance of the compressor to be tested; when the low-speed characteristics of the same type of units are unknown, the actual machine measurement plus interpolation fitting method is used to obtain the low-speed characteristics of the compressor to be tested.

[0088] The full-operating-condition modeling method of the compressed air energy storage compressor of the embodiment of the present application is a full-operating-condition modeling method based on low-speed characteristics and high-speed characteristics. The low-speed characteristics of the compressor are constructed by the baseline estimation method, and the high-speed characteristics of the compressor are constructed by the mechanism model. Finally, the low-speed characteristics below 40% of the rated speed of the obtained low-speed characteristics are used as the low-speed characteristics of the compressor, and the high-speed characteristics of the mechanism model are used as the high-speed characteristics of the compressor. The characteristics between the middle 40% and 60% of the rated speed are obtained by averaging and summing the two, and the two sections of the characteristics are segmented and reasonably combined to obtain a complete compressor full-operating-condition model. Therefore, the embodiment of the present application can quickly model the full-operating conditions of the compressor of the compressed air energy storage system with unknown performance, and can realize the rapid modeling of similar models of the same manufacturer and similar models of different manufacturers. And with the continuous enrichment of the database after large-scale application, the modeling types will continue to expand. The method is simple and economical.

[0089] According to the method for modeling the full-operating conditions of a compressed air energy storage compressor proposed in the embodiment of the present application, the modeling requirements of the compressor to be modeled are obtained, and the first speed performance curve of the compressor to be modeled is determined, and at least one state variable is determined according to the modeling requirements, and an initial compressor model is built based on at least one state variable and a preset constraint relationship, and the initial compressor model is run to obtain the second speed performance curve of the compressor to be modeled, and the first performance curve is determined according to the first speed performance curve, and the second performance curve is determined according to the second speed performance curve, and the third performance curve is determined according to the first performance curve and the second performance curve, and the performance curve of the rated speed under the full operating conditions is obtained by combining. Thus, the problems of inaccurate compressor modeling, poor versatility and low economy of experimental modeling in the compressed air energy storage system are solved, the modeling is relatively accurate, simple and economical, and the compressor in the compressed air energy storage system with unknown performance can be quickly modeled under the full operating conditions.

[0090] Next, a full-operating-condition modeling device for a compressed air energy storage compressor proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0091] Figure 4 It is a block diagram of a full-operating-condition modeling device for a compressed air energy storage compressor according to an embodiment of the present application.

[0092] like Figure 4 As shown, the compressed air energy storage compressor full-operating condition modeling device 10 includes: an acquisition module 100, a determination module 200 and a modeling module 300.

[0093] The acquisition module 100 is used to acquire the modeling requirements of the compressor to be modeled.

[0094] Determination module 200 is used to determine the first speed performance curve of the compressor to be modeled, and determine at least one state variable according to the modeling requirements, and build an initial compressor model based on the at least one state variable and a preset constraint relationship, and run the initial compressor model to obtain the second speed performance curve of the compressor to be modeled.

[0095] The modeling module 300 is used to determine a first performance curve based on the first speed performance curve, determine a second performance curve based on the second speed performance curve, and determine a third performance curve based on the first performance curve and the second performance curve, and combine the first performance curve, the third performance curve and the second performance curve to obtain a performance curve of the rated speed under all operating conditions, so as to model the compressor based on the performance curve of the rated speed under all operating conditions.

[0096] Optionally, in some embodiments, the determination module 200 includes: a judgment unit and a generation unit.

[0097] The judging unit is used to judge whether there is performance data in the modeling requirement.

[0098] The generating unit is used to determine the first speed performance curve based on the performance data when there is performance data in the modeling requirement, otherwise obtain the first speed performance curve according to the actual machine test results of the compressor to be modeled.

[0099] Optionally, in some embodiments, the generating unit includes: a first determining subunit and a second determining subunit.

[0100] The first determination subunit is used to determine the reference point and surge boundary line corresponding to each speed according to the performance data, and connect the reference points corresponding to each speed to form a baseline;

[0101] The second determination subunit determines a first speed performance curve based on the baseline and the surge boundary line by using the general characteristics of the compressor stage.

[0102] Optionally, in some embodiments, the modeling module 300 includes: a first selection unit and a second selection unit.

[0103] Wherein, the first selection unit is used to select a first performance curve whose rated speed is less than a first threshold from the first speed performance curve;

[0104] The second selection unit is used to select a second performance curve whose rated speed is greater than the second threshold and less than or equal to the third threshold from the second speed performance curve, and determine a third performance curve whose rated speed is greater than or equal to the first threshold and less than or equal to the second threshold based on the first performance curve and the second performance curve.

[0105] Optionally, in some embodiments, the initial compressor model includes:

[0106]

[0107] Among them, ρ out is the gas density at the compressor outlet, τ is the time variable, q m,in is the mass flow rate at the compressor inlet, q m,out is the mass flow rate at the compressor outlet, c out is the velocity of the gas at the compressor outlet, c in is the velocity of the gas at the compressor inlet, p in is the compressor inlet pressure, A in is the compressor inlet cross-sectional area, p out is the compressor outlet pressure, A out is the outlet cross-sectional area of ​​the compressor, h out is the specific enthalpy of the gas at the compressor outlet, c p is the specific heat capacity, Rg is the gas constant, T out is the outlet gas temperature, c is the gas velocity, and h is the specific enthalpy of the gas.

[0108] It should be noted that the aforementioned explanation of the embodiment of the method for modeling the compressed air energy storage compressor under full operating conditions is also applicable to the device for modeling the compressed air energy storage compressor under full operating conditions of this embodiment, and will not be repeated here.

[0109] According to the full-operating-condition modeling device for a compressed air energy storage compressor proposed in the embodiment of the present application, the modeling requirements of the compressor to be modeled are obtained, and the first speed performance curve of the compressor to be modeled is determined, and at least one state variable is determined according to the modeling requirements, and an initial compressor model is built based on at least one state variable and a preset constraint relationship, and the initial compressor model is run to obtain the second speed performance curve of the compressor to be modeled, and the first performance curve is determined according to the first speed performance curve, and the second performance curve is determined according to the second speed performance curve, and the third performance curve is determined according to the first performance curve and the second performance curve, and the performance curve of the rated speed under full operating conditions is obtained by combination. Thus, the problems of inaccurate compressor modeling, poor versatility and low economy of experimental modeling in compressed air energy storage systems are solved, and the modeling is relatively accurate, simple and economical, and the compressor in a compressed air energy storage system with unknown performance can be quickly modeled under full operating conditions.

[0110] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0111] A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0112] When the processor 502 executes the program, the full-operating-condition modeling method of the compressed air energy storage compressor provided in the above-mentioned embodiment is implemented.

[0113] Furthermore, the electronic device further comprises:

[0114] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0115] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0116] The memory 501 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0117] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0118] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0119] The processor 502 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0120] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for modeling the full working conditions of a compressed air energy storage compressor.

[0121] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0122] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0123] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0124] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0125] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0126] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for modeling a compressed air energy storage compressor under full working conditions, characterized in that: The following steps are involved: Obtain modeling requirements of the compressor to be modeled; Determine a first speed performance curve of the compressor to be modeled, determine at least one state variable according to the modeling requirement, build an initial compressor model based on the at least one state variable and a preset constraint relationship, and run the initial compressor model to obtain a second speed performance curve of the compressor to be modeled; as well as A first performance curve is determined according to the first speed performance curve, a second performance curve is determined according to the second speed performance curve, and a third performance curve is determined according to the first performance curve and the second performance curve, and the first performance curve, the third performance curve and the second performance curve are combined to obtain a performance curve of rated speed under all operating conditions, so as to model the compressor based on the performance curve of rated speed under all operating conditions.

2. The method according to claim 1, characterized in that The step of determining a first speed performance curve of the compressor to be modeled comprises: Determining whether there is performance data in the modeling requirement; If the performance data exists in the modeling requirement, the first speed performance curve is determined based on the performance data; otherwise, the first speed performance curve is obtained according to actual machine test results of the compressor to be modeled.

3. The method according to claim 2, characterized in that The determining the first speed performance curve based on the performance data includes: Determine a reference point and a surge boundary line corresponding to each speed according to the performance data, and connect the reference points corresponding to each speed to form a baseline; The first speed performance curve is determined based on the baseline and the surge boundary line using a general characteristic of a compressor stage.

4. The method according to claim 1, characterized in that: The determining of a first performance curve according to the first speed performance curve, determining a second performance curve according to the second speed performance curve, and determining a third performance curve according to the first performance curve and the second performance curve includes: Selecting a first performance curve whose rated speed is less than a first threshold from the first speed performance curve; A second performance curve having a rated speed greater than a second threshold and less than or equal to a third threshold is selected from the second speed performance curve, and a third performance curve having a rated speed greater than or equal to the first threshold and less than or equal to the second threshold is determined based on the first performance curve and the second performance curve.

5. The method according to claim 1, characterized in that The initial compressor model comprises: Among them, ρ out is the gas density at the compressor outlet, τ is the time variable, q m，in is the mass flow rate at the compressor inlet, q m，out is the mass flow rate at the compressor outlet, c out is the velocity of the gas at the compressor outlet, c in is the velocity of the gas at the compressor inlet, p in is the compressor inlet pressure, A in is the compressor inlet cross-sectional area, p out is the compressor outlet pressure, A out is the outlet cross-sectional area of ​​the compressor, h out is the specific enthalpy of the gas at the compressor outlet, c p is the specific heat capacity, Rg is the gas constant, T out is the outlet gas temperature, c is the gas velocity, and h is the specific enthalpy of the gas.

6. A full-operating-condition modeling device for a compressed air energy storage compressor, characterized in that: include: An acquisition module, used for acquiring modeling requirements of the compressor to be modeled; A determination module, used to determine a first speed performance curve of the compressor to be modeled, determine at least one state variable according to the modeling requirement, build an initial compressor model based on the at least one state variable and a preset constraint relationship, and run the initial compressor model to obtain a second speed performance curve of the compressor to be modeled; as well as A modeling module is used to determine a first performance curve based on the first speed performance curve, determine a second performance curve based on the second speed performance curve, and determine a third performance curve based on the first performance curve and the second performance curve, combine the first performance curve, the third performance curve and the second performance curve to obtain a performance curve of the rated speed under all operating conditions, so as to model the compressor based on the performance curve of the rated speed under all operating conditions.

7. The device according to claim 6, characterized in that The determining module comprises: A judging unit, used to judge whether there is performance data in the modeling requirement; A generating unit is used to determine the first speed performance curve based on the performance data when the performance data exists in the modeling requirement, otherwise obtain the first speed performance curve according to the actual machine test results of the compressor to be modeled.

8. The device according to claim 7, characterized in that The generating unit comprises: A first determination subunit is used to determine a reference point and a surge boundary line corresponding to each speed according to the performance data, and connect the reference points corresponding to each speed to form a baseline; The second determination subunit determines the first speed performance curve based on the baseline and the surge boundary line by using the general characteristics of the compressor stage.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for modeling the full working conditions of a compressed air energy storage compressor as described in any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the full-operating condition modeling method for a compressed air energy storage compressor as described in any one of claims 1 to 5.

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