Full working condition modeling method for compressed air energy storage compressor

Abstract

The application relates to the technical field of compressors, in particular to a full-working-condition modeling method for a compressed air energy storage compressor, which comprises the following steps: obtaining modeling requirements of a compressor to be modeled; determining a first rotating speed performance curve of the compressor to be modeled, determining at least one state variable according to the modeling requirements, building an initial compressor model based on the at least one state variable and a preset constraint relationship, and running the initial compressor model to obtain a second rotating speed performance curve of the compressor to be modeled; determining a first performance curve according to the first rotating speed performance curve, determining a second performance curve according to the second rotating speed performance curve, and determining a third performance curve according to the first performance curve and the second performance curve, and combining the performance curves to obtain a performance curve of a full-working-condition rated rotating speed. Therefore, the problems of inaccurate compressor modeling, poor universality and low economy are solved, the method is simple and has high economy, and the full-working-condition modeling can be performed on a compressor with unknown performance.

Description

Technical Field

[0001] This application relates to the field of compressor technology, and in particular to a method for modeling compressed air energy storage compressors under all operating conditions. Background Technology

[0002] Due to the significant volatility and uncertainty of wind and solar power generation, 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 compressed air energy storage systems, it is crucial to establish accurate simulation models, especially to model the compressor in the compressed air energy storage system under all operating conditions in order to study the compressor start-up process and subsequent variable operating condition characteristics.

[0004] One modeling method for compressors in related technologies involves restricting the operating conditions, assuming that the compressor only operates at higher speeds, such as above 80% of its rated speed. In this case, a commonly used empirical model can be used to model the compressor. This modeling method is relatively simple and convenient, and under certain conditions, it can accurately fit parameters such as the compressor's isentropic efficiency and pressure ratio. It is suitable for modeling compressors operating near their rated point. However, since the empirical model can only provide a good picture of the compressor characteristics at higher speeds, it does not reflect the compressor characteristics at low speeds clearly or accurately, so corrections are needed.

[0005] Another approach is compressor modeling based on data fitting. This method involves conducting actual experiments on a real compressor, recording and acquiring parameters such as speed, mass flow rate, isentropic efficiency, pressure ratio, inlet and outlet temperatures, and inlet and outlet pressures. Various characteristic curves are then fitted using functions, and other data are obtained through interpolation. This method is simple to understand and can effectively characterize the compressor's features under all operating conditions. However, due to the specificity of different compressor units, different manufacturers and models have different compressor characteristics, making them incompatible. Furthermore, the testing process is time-consuming and labor-intensive, resulting in low experimental economics. Experiments at high speeds also present a series of safety issues.

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

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

[0008] The first aspect of this application provides a method for full-condition modeling of a compressed air energy storage compressor, including the following steps:

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

[0010] 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. An initial compressor model is built based on the at least one state variable and preset constraints, and the initial compressor model is run to obtain the second speed performance curve of the compressor to be modeled.

[0011] A first performance curve is determined based on the first speed performance curve, 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. The first performance curve, the third performance curve, and the second performance curve are combined to obtain the performance curve at the rated speed under all operating conditions. The compressor is then modeled based on the performance curve at the rated speed under all operating conditions.

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

[0013] Determine whether performance data exists in the modeling requirements;

[0014] If the performance data exists in the modeling requirements, the first speed performance curve is determined based on the performance data; otherwise, the first speed performance curve is obtained based on the actual experimental 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] Based on the performance data, determine the reference point and surge boundary line corresponding to each rotational speed, and connect the reference points corresponding to each rotational speed to form a baseline;

[0017] Based on the baseline and the surge boundary line, the first speed performance curve is determined using the general characteristics of the compressor stage.

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

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

[0020] Select a second performance curve from the second speed performance curves, where the rated speed is greater than the second threshold and less than or equal to the third threshold. Then, determine a third performance curve based on the first performance curve and the second performance curves, where the rated speed is greater than or equal to the first threshold and less than or equal to the second threshold.

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

[0022] ;

[0023] ; ;

[0024] in, The density of the gas exiting the compressor. For time variables, The mass flow rate at the compressor inlet. The mass flow rate at the compressor outlet. The velocity of the gas exiting the compressor. The velocity of the gas at the compressor inlet. This is the compressor inlet pressure. Let be the inlet cross-sectional area of ​​the compressor. This refers to the compressor outlet pressure. Where is the outlet cross-sectional area of ​​the compressor. The specific enthalpy of the gas exiting the compressor. For specific heat capacity, , The temperature of the outlet gas, For the velocity of the gas, is the specific enthalpy of the gas.

[0025] A second aspect of this application provides a full-condition modeling device for a compressed air energy storage compressor, comprising:

[0026] The acquisition module is used to acquire the modeling requirements of the compressor to be modeled;

[0027] A determination module is used to determine the first speed performance curve of the compressor to be modeled, determine at least one state variable according to the modeling requirements, build an initial compressor model based on the at least one state variable and preset constraint relationships, and run the initial compressor model to obtain the second speed performance curve of the compressor to be modeled; and

[0028] The modeling module is used to determine a first performance curve based on the first speed performance curve, a second performance curve based on the second speed performance curve, and a third performance curve based on the first performance curve and the second performance curve. The first performance curve, the third performance curve, and the second performance curve are combined to obtain a performance curve at the rated speed under all operating conditions, so as to model the compressor based on the performance curve at the rated speed under all operating conditions.

[0029] Optionally, in some embodiments, the determining module includes:

[0030] The judgment unit is used to determine whether performance data exists in the modeling requirements;

[0031] The generation unit is used to determine the first speed performance curve based on the performance data when the performance data exists in the modeling requirements; otherwise, it obtains the first speed performance curve based on the actual experimental results of the compressor to be modeled.

[0032] Optionally, in some embodiments, the generation unit includes:

[0033] The first determining subunit is used to determine the reference point and surge boundary line corresponding to each rotational speed based on the performance data, and connect the reference points corresponding to each rotational speed to form a baseline;

[0034] The second determining subunit determines the first speed performance curve based on the baseline and the surge boundary line, utilizing the general characteristics of the compressor stage.

[0035] Optionally, in some embodiments, the modeling module includes:

[0036] 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;

[0037] The second selection unit is used to select a second performance curve from the second speed performance curve whose rated speed is greater than a second threshold and less than or equal to a third threshold, and to determine a third performance curve whose rated speed is greater than or equal to a first threshold and less than or equal to a second threshold based on the first performance curve and the second performance curve.

[0038] Optionally, in some embodiments, the initial compressor model includes:

[0039] ;

[0040] ; ;

[0041] in, The density of the gas exiting the compressor. For time variables, The mass flow rate at the compressor inlet. The mass flow rate at the compressor outlet. The velocity of the gas exiting the compressor. The velocity of the gas at the compressor inlet. This is the compressor inlet pressure. Let be the inlet cross-sectional area of ​​the compressor. This refers to the compressor outlet pressure. Where is the outlet cross-sectional area of ​​the compressor. The specific enthalpy of the gas exiting the compressor. For specific heat capacity, , The temperature of the outlet gas, For the velocity of the gas, is the specific enthalpy of the gas.

[0042] A third aspect of this application provides an electronic device, including: 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 full-condition modeling method for compressed air energy storage compressors as described in the above embodiments.

[0043] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the full-condition modeling method for compressed air energy storage compressors as described in the above embodiments.

[0044] Therefore, by obtaining the modeling requirements of the compressor to be modeled and determining its first speed performance curve, at least one state variable is determined based on the modeling requirements. An initial compressor model is then built based on this initial state variable and preset constraints. Running the initial compressor model yields the second speed performance curve of the compressor. A first performance curve is then determined based on the first speed performance curve, and a second performance curve is determined based on the second speed performance curve. Finally, a third performance curve is determined based on the first and second performance curves, and these are combined to obtain the performance curve at the rated speed under all operating conditions. This solves the problems of inaccurate compressor modeling, poor versatility, and low economy in compressed air energy storage systems. The modeling is more accurate, simple, and economical, enabling rapid full-condition modeling of compressors in compressed air energy storage systems with unknown performance.

[0045] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0046] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0047] Figure 1 This is a flowchart of a full-condition modeling method for a compressed air energy storage compressor provided according to an embodiment of this application;

[0048] Figure 2 This is a schematic diagram of a compressor system provided according to an embodiment of this application;

[0049] Figure 3 This is a schematic diagram illustrating the principle of a full-condition modeling method for a compressed air energy storage compressor according to an embodiment of this application;

[0050] Figure 4 This is a block diagram of a compressed air energy storage compressor full-condition modeling device provided according to an embodiment of this application;

[0051] Figure 5 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0052] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0053] The following describes a full-condition modeling method for compressed air energy storage compressors according to embodiments of this application, with reference to the accompanying drawings. Addressing the problems of inaccurate compressor modeling, poor versatility, and low economy in compressed air energy storage systems mentioned in the background art, this application provides a full-condition modeling method for compressed air energy storage compressors. In this method, the modeling requirements of the compressor to be modeled are obtained, and a first speed performance curve of the compressor is determined. At least one state variable is determined based on the modeling requirements, and an initial compressor model is built based on the at least one state variable and preset constraint relationships. The initial compressor model is then run to obtain a second speed performance curve of the compressor to be modeled. 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. A third performance curve is determined based on the first and second performance curves, and these are combined to obtain the performance curve at the rated speed under all operating conditions. This solves the problems of inaccurate compressor modeling, poor versatility, and low economy in compressed air energy storage systems. The modeling is more accurate, simple, and economical, and can quickly perform full-condition modeling of compressors in compressed air energy storage systems with unknown performance.

[0054] Specifically, Figure 1This is a flowchart illustrating a full-condition modeling method for a compressed air energy storage compressor provided in an embodiment of this application.

[0055] like Figure 1 As shown, the full-condition modeling method for compressed air energy storage compressors includes the following steps:

[0056] In step S101, the modeling requirements of the compressor to be modeled are obtained.

[0057] It should be noted that compressed air energy storage systems typically employ multi-stage compressors to gradually increase gas pressure. Each compressor is driven by an independent motor, and the compressors are connected sequentially via pipelines to form a continuous compression process, such as... Figure 2 As shown. To simplify the analysis, the embodiments of this application 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 with no 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 remains constant during startup and stable operation; fifth, since the startup process is transient, it is assumed that the heat exchanger does not function during the startup phase, and therefore the influence of the heat exchanger is not considered. Based on the above assumptions, the full-condition modeling method for the compressed air energy storage compressor of this application embodiment is proposed.

[0058] Specifically, the embodiments of this application need to determine the specific design parameters of the compressor system, including the specifications of a single compressor and its corresponding motor. The compressor system is generally a multi-stage design, that is, it contains two or more compressors and motors. It can start with a single-stage compressor and motor, and then apply the same method to the design of other stages of compressors and motors.

[0059] Obtain the modeling requirements for the compressor to be modeled, and record the obtained information in the following format: compressor manufacturer and model, compressor rated mass flow rate. The rated speed of the compressor is The compressor's rated pressure ratio is The compressor's adiabatic efficiency is The volume of the compressor is The inlet cross-sectional area of ​​the compressor is The compressor's outlet cross-sectional area is The compression force of the electric motor is Note that the above are all design parameters, that is, parameters that are fixed after the compressor leaves the factory, which are different from the variables mentioned later.

[0060] In step S102, 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. An initial compressor model is built based on the at least one state variable and the preset constraint relationship, and the initial compressor model is run to obtain the second speed performance curve of the compressor to be modeled.

[0061] The following section explains how to determine the first speed performance curve at low speeds.

[0062] Furthermore, in some embodiments, determining the first speed performance curve of the compressor to be modeled includes: determining whether performance data exists in the modeling requirements; if performance data exists in the modeling requirements, then determining the first speed performance curve based on the performance data; otherwise, obtaining the first speed performance curve based on the actual experimental results of the compressor to be modeled.

[0063] Furthermore, in some embodiments, determining the first speed performance curve based on performance data includes: determining a reference point and surge boundary line corresponding to each speed according to the performance data, connecting the reference points corresponding to each speed to form a baseline; and determining the first speed performance curve based on the baseline and surge boundary line using the general characteristics of the compressor stage.

[0064] If there are similar models of compressors from the same manufacturer or compressors from different manufacturers with the same positioning, and performance data can be obtained, that is, if there is already designed compressor performance data, then the low-speed performance of the compressor under test can be obtained by using the baseline estimation method.

[0065] First, obtain the highest efficiency point at each speed, that is, the point with the highest isentropic efficiency at all speeds, and take it as the reference point. Then, connect the reference points to form a baseline and determine the surge boundary line. Finally, calculate the performance value of the entire flow range at that speed using the general characteristics of the same speed of the stage.

[0066] The formulas for calculating the reference point location and surge boundary line are given below:

[0067] ;

[0068] ;

[0069] ;

[0070] ;

[0071] in, Let i be the efficiency of the surge boundary under any working condition i. This is a coefficient, usually taken as 0.7. The compressor speed under current operating condition i. This refers to the power output of the compressor. Where k is the pressure ratio and k is the adiabatic index. The pressure ratio under the current operating condition i. As a reference point pressure ratio, The reference rotational speed The surge flow rate under current operating condition i. The surge flow rate is at the reference point.

[0072] It should be noted that if there is no usable compressor unit performance data in this embodiment, a real-world experiment can be conducted on the compressor to be modeled. Since the performance at low speed is required, the experiment is relatively safe. Gas sensors are placed at the compressor inlet and outlet, and a speed sensor is connected to the shaft to measure the gas mass flow rate, temperature, velocity, and compressor speed. Measurements are taken at least every 0.1 seconds based on sensor performance, and the data is recorded on a computer. Excel's plotting function can be used to display the compressor's low-performance curve, and MATLAB's interpolation functions can be used to obtain the compressor's operating condition function under low-speed operation.

[0073] The following section explains how to determine the second speed performance curve at high speeds.

[0074] Specifically, in this embodiment of the application, the gas density inside the compressor to be modeled is selected. The speed of the outlet gas Temperature of the outlet gas As state variables, other variables can be represented by the aforementioned state variables. Specifically, the following relationships exist:

[0075] ;

[0076] ;

[0077] ;

[0078] in, Represents the mass flow rate of the compressor. Represents pressure. It is a time variable. It is the specific enthalpy of a gas. It is the work done by the electric motor. It refers to specific heat capacity, indicated by the subscript. Indicates the entrance, indicated by the subscript. Indicates the exit point.

[0079] in, Given by the following formula:

[0080] ;

[0081] ; ; ; ; ; ;

[0082] ; ; ; ;

[0083] In this context, the superscript · represents the variable after conversion. Represents pressure ratio, Represents adiabatic compression efficiency. Represents the reduced mass flow rate. Represents the equivalent rotational speed. These are intermediate parameters. Take 0.8, and The value is 1.8.

[0084] After obtaining all the information about the compression system, you can build a compressor model in Simulink and select the gas density inside the compressor. The speed of the outlet gas Temperature of the outlet gas As the state variable of the compressor, the built-in integration module in Simulink is used. The compressor system is constructed using other operators. Specifically, this involves integrating the first-order differential of the state variable (i.e., using an integration module). The state variables are obtained, and then the constraint relationships are derived using the mass conservation formula, momentum conservation formula, and energy conservation formula. The specific formulas are as follows:

[0085] ; ; ;

[0086] The meaning of each quantity has been given in step S101 and will not be repeated here.

[0087] After constructing the model, initial values ​​need to be given for the state variables. Here, the initial values ​​for the key dynamic parameters of the compressor to be modeled are given, namely, density, velocity, and temperature are set to 1.184 kg / m³. 3 0 m / s and 298.15 K can all be set in the integration module in Simulink. Open the integration module. Set the initial values ​​as described above and confirm. At this point, the startup process parameter settings are complete.

[0088] Then, run the current model. You can use the scope module to monitor or view the internal parameters of the current compressor, such as speed, gas temperature, and mass flow rate. After the model finishes running, download all parameters and variables of the current model, and construct all the data and quantitative relationships needed to characterize the high-speed performance of the compressor, such as the speed-isentropic efficiency diagram and the speed-pressure ratio diagram.

[0089] In step S103, a first performance curve is determined based on the first speed performance curve, a second performance curve is determined based on the second speed performance curve, and a third performance curve is determined based on the first and second performance curves. The first, third, and second performance curves are combined to obtain the performance curve at the rated speed under all operating conditions, and the compressor is modeled based on the performance curve at the rated speed under all operating conditions.

[0090] Optionally, in some embodiments, determining a first performance curve based on a first speed performance curve, determining a second performance curve based on a second speed performance curve, and determining a third performance curve based on the first and second performance curves includes: selecting a first performance curve from the first speed performance curves whose rated speed is less than a first threshold; selecting a second performance curve from the second speed performance curves whose rated speed is greater than a second threshold and less than or equal to a third threshold; 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 and second performance curves.

[0091] The first threshold can be 40%, the second threshold can be 60%, and the third threshold can be 100%.

[0092] Specifically, in this embodiment, after obtaining the low-speed and high-speed performance of the compressed air energy storage compressor to be modeled, the portion of the first performance curve obtained from the low-speed performance estimation of the compressor to be modeled that is below 40% of the rated speed is selected as the low-speed performance curve of the compressor. The portion of the second speed performance curve that is above 60% of the rated speed is selected as the high-speed performance curve of the compressor to be modeled. The curve between 40% and 60% of the rated speed is formed by averaging the two curves to make the curve smoother. 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.

[0093] In summary, as Figure 3 As shown, the compressed air energy storage compressor full-condition modeling method of this application includes two cases: known characteristics of the same type of unit and unknown characteristics of the same type of unit. When the low-speed characteristics of the same type of unit are known, the baseline estimation method is used to obtain the low-speed performance of the compressor under test. When the low-speed characteristics of the same type of unit are unknown, the actual measurement plus interpolation fitting method is used to obtain the low-speed characteristics of the compressor under test.

[0094] The compressed air energy storage compressor full-condition modeling method of this application embodiment is based on low-speed and high-speed characteristics. It uses baseline estimation to construct the compressor's low-speed characteristics and a mechanistic model to construct its high-speed characteristics. Finally, it uses the low-speed characteristics below 40% of the rated speed as the compressor's low-speed characteristics and the high-speed characteristics above 60% of the rated speed from the mechanistic model as the compressor's high-speed characteristics. The characteristics between 40% and 60% of the rated speed are obtained by averaging the two. By segmenting and reasonably combining these two characteristic segments, a complete full-condition model of the compressor is obtained. Therefore, this application embodiment can quickly perform full-condition modeling of compressed air energy storage system compressors with unknown performance. It can achieve rapid modeling of similar models from the same manufacturer and models with similar structures from different manufacturers. Furthermore, as the database is continuously enriched with large-scale applications, the modeling types will continue to expand. The method is simple and highly economical.

[0095] The compressed air energy storage compressor full-condition modeling method proposed in this application involves obtaining the modeling requirements of the compressor to be modeled, determining the first speed performance curve of the compressor, determining at least one state variable based on the modeling requirements, building an initial compressor model based on the at least one state variable and preset constraint relationships, running the initial compressor model to obtain the second speed performance curve of the compressor to be modeled, determining the first performance curve based on the first speed performance curve, determining the second performance curve based on the second speed performance curve, and determining the third performance curve based on the first and second performance curves. These are combined to obtain the full-condition rated speed performance curve. This solves the problems of inaccurate compressor modeling, poor experimental modeling versatility, and low economy in compressed air energy storage systems. The modeling is more accurate, simple, and economical, and can quickly perform full-condition modeling of compressors in compressed air energy storage systems with unknown performance.

[0096] Next, referring to the accompanying drawings, a full-condition modeling device for compressed air energy storage compressors according to embodiments of this application is described.

[0097] Figure 4 This is a block diagram of a compressed air energy storage compressor full-condition modeling device according to an embodiment of this application.

[0098] like Figure 4 As shown, the compressed air energy storage compressor full-condition modeling device 10 includes: an acquisition module 100, a determination module 200, and a modeling module 300.

[0099] The acquisition module 100 is used to acquire the modeling requirements of the compressor to be modeled.

[0100] The determination module 200 is used to determine the first speed performance curve of the compressor to be modeled, determine at least one state variable according to the modeling requirements, build an initial compressor model based on at least one state variable and preset constraint relationships, and run the initial compressor model to obtain the second speed performance curve of the compressor to be modeled.

[0101] The modeling module 300 is used to determine a first performance curve based on a first speed performance curve, a second performance curve based on a second speed performance curve, and a third performance curve based on the first and second performance curves. The first, third, and second performance curves are combined to obtain a performance curve at the rated speed under all operating conditions, and the compressor is modeled based on the performance curve at the rated speed under all operating conditions.

[0102] Optionally, in some embodiments, the determining module 200 includes a determining unit and a generating unit.

[0103] The judgment unit is used to determine whether performance data exists in the modeling requirements.

[0104] The generation unit is used to determine the first speed performance curve based on the performance data when performance data exists in the modeling requirements; otherwise, it obtains the first speed performance curve based on the actual experimental results of the compressor to be modeled.

[0105] Optionally, in some embodiments, the generating unit includes: a first determining subunit and a second determining subunit.

[0106] The first determining subunit is used to determine the reference point and surge boundary line corresponding to each speed based on the performance data, and connect the reference points corresponding to each speed to form a baseline.

[0107] The second determining sub-unit, based on the baseline and surge boundary line, utilizes the general characteristics of the compressor stage to determine the first speed performance curve.

[0108] Optionally, in some embodiments, the modeling module 300 includes: a first selection unit and a second selection unit.

[0109] 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 curves.

[0110] The second selection unit is used to select a second performance curve from the second speed performance curve where the rated speed is greater than the second threshold and less than or equal to the third threshold, and to determine a third performance curve where the 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.

[0111] Optionally, in some embodiments, the initial compressor model includes:

[0112] ;

[0113] ; ;

[0114] in, The density of the gas exiting the compressor. For time variables, The mass flow rate at the compressor inlet. The mass flow rate at the compressor outlet. The velocity of the gas exiting the compressor. The velocity of the gas at the compressor inlet. This is the compressor inlet pressure. Let be the inlet cross-sectional area of ​​the compressor. This refers to the compressor outlet pressure. Where is the outlet cross-sectional area of ​​the compressor. The specific enthalpy of the gas exiting the compressor. For specific heat capacity, , The temperature of the outlet gas, For the velocity of the gas, is the specific enthalpy of the gas.

[0115] It should be noted that the foregoing explanation of the full-condition modeling method for compressed air energy storage compressors also applies to the full-condition modeling device for compressed air energy storage compressors in this embodiment, and will not be repeated here.

[0116] The compressed air energy storage compressor full-condition modeling device proposed in this application obtains the modeling requirements of the compressor to be modeled, determines the first speed performance curve of the compressor, determines at least one state variable based on the modeling requirements, builds an initial compressor model based on the at least one state variable and preset constraint relationships, runs the initial compressor model to obtain the second speed performance curve of the compressor to be modeled, determines the first performance curve based on the first speed performance curve, determines the second performance curve based on the second speed performance curve, and determines the third performance curve based on the first and second performance curves, combining them to obtain the full-condition rated speed performance curve. This solves the problems of inaccurate compressor modeling, poor experimental modeling versatility, and low economy in compressed air energy storage systems. The modeling is more accurate, simple, and economical, and can quickly perform full-condition modeling of compressors in compressed air energy storage systems with unknown performance.

[0117] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0118] The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0119] When the processor 502 executes the program, it implements the full-condition modeling method for compressed air energy storage compressors provided in the above embodiments.

[0120] Furthermore, electronic devices also include:

[0121] Communication interface 503 is used for communication between memory 501 and processor 502.

[0122] The memory 501 is used to store computer programs that can run on the processor 502.

[0123] The memory 501 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0124] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. 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 address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0125] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0126] The processor 502 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0127] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for modeling the full operating conditions of a compressed air energy storage compressor.

[0128] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0129] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0130] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0131] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0132] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0133] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for full-condition modeling of a compressed air energy storage compressor, characterized in that, Includes the following steps: Obtain the modeling requirements for the compressor to be modeled; 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. An initial compressor model is built based on the at least one state variable and the preset constraint relationship, and the initial compressor model is run to obtain the second speed performance curve of the compressor to be modeled. as well as A first performance curve is determined based on the first speed performance curve, a second performance curve is determined based on the second speed performance curve, and a third performance curve is determined based on the first and second performance curves. The first, third, and second performance curves are combined to obtain a performance curve at the rated speed under all operating conditions. Compressor modeling is then performed based on this performance curve at the rated speed under all operating conditions. The step of determining a first performance curve based on the first speed performance curve, determining a second performance curve based on the second speed performance curve, and determining a third performance curve based on the first performance curve and the second performance curve includes: selecting a first performance curve from the first speed performance curve whose rated speed is less than a first threshold; selecting a second performance curve from the second speed performance curve whose rated speed is greater than a second threshold and less than or equal to a third threshold; 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. Based on the modeling requirements, the type of compressor to be modeled is determined. The first speed performance curve of a known compressor of the same type is obtained using a preset baseline estimation method. The first speed performance of an unknown compressor of the same type is obtained using a combination of actual machine measurement and interpolation fitting.

2. The method according to claim 1, characterized in that, Determining the first speed performance curve of the compressor to be modeled includes: Determine whether performance data exists in the modeling requirements; If the performance data exists in the modeling requirements, the first speed performance curve is determined based on the performance data; otherwise, the first speed performance curve is obtained based on the actual experimental results of the compressor to be modeled.

3. The method according to claim 2, characterized in that, Determining the first speed performance curve based on the performance data includes: Based on the performance data, determine the reference point and surge boundary line corresponding to each rotational speed, and connect the reference points corresponding to each rotational 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.

4. The method according to claim 1, characterized in that, The initial compressor model includes: ； ； ； in, The density of the gas exiting the compressor. For time variables, The mass flow rate at the compressor inlet. The mass flow rate at the compressor outlet. The velocity of the gas exiting the compressor. The velocity of the gas at the compressor inlet. This is the compressor inlet pressure. Let be the inlet cross-sectional area of ​​the compressor. This refers to the compressor outlet pressure. Where is the outlet cross-sectional area of ​​the compressor. The specific enthalpy of the gas exiting the compressor. For specific heat capacity, The gas constant is... The temperature of the outlet gas, For the velocity of the gas, is the specific enthalpy of the gas.

5. A full-condition modeling device for compressed air energy storage compressors, characterized in that, include: The acquisition module is used to acquire the modeling requirements of the compressor to be modeled; The determination module is used to determine the first speed performance curve of the compressor to be modeled, determine at least one state variable according to the modeling requirements, 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. as well as The modeling module is used to determine a first performance curve based on the first speed performance curve, a second performance curve based on the second speed performance curve, and a third performance curve based on the first and second performance curves. It then combines the first, third, and second performance curves to obtain a performance curve at the rated speed under all operating conditions, and uses this performance curve to model the compressor. The step of determining a first performance curve based on the first speed performance curve, determining a second performance curve based on the second speed performance curve, and determining a third performance curve based on the first performance curve and the second performance curve includes: selecting a first performance curve from the first speed performance curve whose rated speed is less than a first threshold; selecting a second performance curve from the second speed performance curve whose rated speed is greater than a second threshold and less than or equal to a third threshold; 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. Based on the modeling requirements, the type of compressor to be modeled is determined. The first speed performance curve of a known compressor of the same type is obtained using a preset baseline estimation method. The first speed performance of an unknown compressor of the same type is obtained using a combination of actual machine measurement and interpolation fitting.

6. The apparatus according to claim 5, characterized in that, The determining module includes: The judgment unit is used to determine whether performance data exists in the modeling requirements; The generation unit is used to determine the first speed performance curve based on the performance data when the performance data exists in the modeling requirements; otherwise, it obtains the first speed performance curve based on the actual experimental results of the compressor to be modeled.

7. The apparatus according to claim 6, characterized in that, The generation unit includes: The first determining subunit is used to determine the reference point and surge boundary line corresponding to each rotational speed based on the performance data, and connect the reference points corresponding to each rotational speed to form a baseline; The second determining subunit determines the first speed performance curve based on the baseline and the surge boundary line, utilizing the general characteristics of the compressor stage.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the full-condition modeling method for compressed air energy storage compressors as described in any one of claims 1-4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the full-condition modeling method for compressed air energy storage compressors as described in any one of claims 1-4.

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