State assessment method of compressed air energy storage system based on digital twin model

Abstract

The present disclosure is about a state assessment method for a compressed air energy storage system based on a digital twin model. The state assessment method for a compressed air energy storage system based on a digital twin model includes constructing a digital twin model of a compressed air energy storage system based on a three-dimensional model of the compressed air energy storage system, a mechanism model of the compressed air energy storage system, and the operating state data of the compressed air energy storage system during operation; constructing a state assessment system for the compressed air energy storage system based on the evaluation index of the compressed air energy storage system and in combination with the digital twin model; training a neural network model based on the state assessment system of the compressed air energy storage system to obtain a state assessment model for the compressed air energy storage system, and conducting a real-time assessment of the operating state of the compressed air energy storage system, and displaying the assessment results in 3D in the digital twin model, which is conducive to realizing real-time monitoring of the operating state of the compressed air energy storage system to improve the stability of the compressed air energy storage system.

Description

Technical Field

[0001] The present disclosure relates to the field of energy storage technology, and in particular to a compressed air energy storage system state assessment method based on a digital twin model. Background Art

[0002] In recent years, new energy power generation technologies represented by wind power generation and photovoltaic power generation have developed rapidly. New energy will play a vital role in the process of building a low-carbon power system. However, the inherent uncertainties of wind power and photovoltaics have brought unprecedented challenges to the operation of the power system. As the main means of peak regulation of new power systems, energy storage is becoming increasingly important in the energy balance of power systems. Compressed air energy storage technology is an emerging energy storage technology that stores pressure potential energy by compressing air. It has the characteristics of large capacity and long life, and has broad application prospects. It can be used as the main means to support the consumption of new energy and peak shaving and valley filling of power grids. However, during long-term operation, the equipment may be unstable. In order to ensure the normal operation of the compressed air energy storage system, it is necessary to evaluate and monitor the operating status of the compressed air energy storage system. Summary of the invention

[0003] In view of this, an embodiment of the present disclosure hopes to provide a compressed air energy storage system state assessment method based on a digital twin model.

[0004] The technical solution of the present disclosure is achieved as follows:

[0005] The present invention provides a compressed air energy storage system state assessment method based on a digital twin model.

[0006] The state assessment method of a compressed air energy storage system based on a digital twin model provided in an embodiment of the present disclosure includes:

[0007] Based on the structural parameters of the components of the compressed air energy storage system when combined, a three-dimensional model of the compressed air energy storage system is constructed; wherein the three-dimensional model reflects the combined connection relationship of the components of the compressed air energy storage system;

[0008] Constructing a digital twin model of the compressed air energy storage system based on the three-dimensional model of the compressed air energy storage system, the mechanism models corresponding to the components of the compressed air energy storage system, and the operating status data of the compressed air energy storage system during operation;

[0009] Based on the evaluation index of the compressed air energy storage system and in combination with the digital twin model, a state evaluation system of the compressed air energy storage system is constructed;

[0010] Based on the state assessment system of the compressed air energy storage system, a neural network model is trained to obtain a state assessment model of the compressed air energy storage system;

[0011] Based on the state assessment model of the compressed air energy storage system, the operating state of the compressed air energy storage system is assessed in real time, and the assessment results are displayed in 3D in the digital twin model.

[0012] In some embodiments, the evaluation index of the compressed air energy storage system includes at least one of the following:

[0013] The operating life of components of the compressed air energy storage system, fault diagnosis of components of the compressed air energy storage system, and the overall performance of the compressed air energy storage system;

[0014] The evaluation index based on the compressed air energy storage system is combined with the digital twin model to construct a state evaluation system of the compressed air energy storage system, including:

[0015] Based on the evaluation index of the compressed air energy storage system and combined with the operating status data in the digital twin model, a state evaluation system of the compressed air energy storage system is constructed; wherein the state evaluation system contains the corresponding association relationship between the operating status data and the evaluation index.

[0016] In some embodiments, the state assessment model based on the compressed air energy storage system is used to assess the operating state of the compressed air energy storage system in real time, and the assessment result is displayed in 3D in the digital twin model, including:

[0017] Collecting real-time status data of the current operating status of the compressed air energy storage system;

[0018] Inputting the real-time status data into the status assessment model to obtain a status assessment result of the current operating status of the compressed air energy storage system;

[0019] The state assessment result is displayed in 3D in the digital twin model; wherein the state assessment result at least includes

[0020] An evaluation result corresponding to at least one indicator of the operating life of components of the compressed air energy storage system, fault diagnosis of components of the compressed air energy storage system, and overall performance of the compressed air energy storage system.

[0021] In some embodiments, the 3D display of the state assessment result in the digital twin model includes:

[0022] According to the evaluation results corresponding to the evaluation indicators, the status levels of the component operation statuses corresponding to the evaluation indicators are respectively divided;

[0023] The status level corresponding to each evaluation index is displayed in 3D at each component position in the digital twin model; wherein the status level includes at least four levels: normal, caution, abnormal and severe.

[0024] In some embodiments, the 3D display of the state assessment result in the digital twin model includes:

[0025] Assign different colors to the four levels;

[0026] According to the evaluation results corresponding to the evaluation indicators of each component, the color of the status level corresponding to the evaluation result is mapped to each component area of ​​the digital twin model.

[0027] In some embodiments, the digital twin model of the compressed air energy storage system is constructed based on the three-dimensional model of the compressed air energy storage system, the mechanism models corresponding to the components of the compressed air energy storage system, and the operating status data of the compressed air energy storage system during operation, including:

[0028] The calculation process and logic of the mechanism model are embedded into the corresponding components or objects of the 3D model, and the mechanism model is called for real-time calculation and simulation during the operation of the 3D model;

[0029] The calculation results of the mechanism model are mapped to the corresponding positions of the three-dimensional model, and compared with the operating status data of the compressed air energy storage system during operation, so as to couple the multi-dimensional data and laws of multi-physical fields, realize the real-time symbiosis of the physical entity and virtual entity of the compressed air energy storage system, and obtain the digital twin model.

[0030] In some embodiments, the three-dimensional model of the compressed air energy storage system is constructed based on the structural parameters of the components of the compressed air energy storage system when combined, including:

[0031] Obtain the structural parameters of each component of the compressed air energy storage system, and obtain the relative position and connection method between each component;

[0032] Import the structural parameters of each component into the modeling software and draw the wireframe model of the component;

[0033] Arrange the positions and connections of each component in the modeling software according to the actual layout to form a wireframe model of the entire system;

[0034] The wireframe model of the entire system is adjusted in detail as needed to obtain a three-dimensional model of the compressed air energy storage system.

[0035] In some embodiments, the mechanism models corresponding to the components of the compressed air energy storage system include at least one of the following:

[0036] The mechanism model of the compressor, the mechanism model of the turbine, the mechanism model of the gas storage system and the mechanism model of the heat storage system.

[0037] According to an embodiment of the present disclosure, a compressed air energy storage system state assessment method based on a digital twin model includes constructing a three-dimensional model of the compressed air energy storage system based on the structural parameters of the various components of the compressed air energy storage system when combined; wherein the three-dimensional model reflects the combined connection relationship of the various components of the compressed air energy storage system; constructing a digital twin model of the compressed air energy storage system based on the three-dimensional model of the compressed air energy storage system, the mechanism models corresponding to the various components of the compressed air energy storage system, and the operating state data of the compressed air energy storage system during operation; constructing a state assessment system of the compressed air energy storage system based on the evaluation indicators of the compressed air energy storage system and the digital twin model; training a neural network model based on the state assessment system of the compressed air energy storage system to obtain a state assessment model of the compressed air energy storage system; and based on the state assessment model of the compressed air energy storage system, performing real-time assessment of the operating state of the compressed air energy storage system, and displaying the assessment results in 3D in the digital twin model. In this application, by constructing a digital twin model and a state assessment model, real-time assessment of the operating status of a compressed air energy storage system and 3D display of the assessment results are achieved, which is conducive to real-time monitoring of the operating status of the compressed air energy storage system and three-dimensional stereoscopic image status display, thereby helping to improve the stability of the compressed air energy storage system.

[0038] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a flow chart of a compressed air energy storage system state assessment method based on a digital twin model according to an exemplary embodiment;

[0040] Figure 2 is a schematic diagram of a compressed air energy storage system module structure according to an exemplary embodiment;

[0041] Figure 3 is a flow chart of compressed air energy storage system state evaluation according to an exemplary embodiment;

[0042] Figure 4 It is a schematic structural diagram of a compressed air energy storage system state assessment device based on a digital twin model according to an exemplary embodiment. DETAILED DESCRIPTION

[0043] Embodiments of the present disclosure are described in detail below, examples of which 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 disclosure, and should not be construed as limiting the present disclosure.

[0044] In recent years, new energy power generation technologies represented by wind power generation and photovoltaic power generation have developed rapidly. New energy will play a vital role in the process of building a low-carbon power system. However, the inherent uncertainties of wind power and photovoltaics have brought unprecedented challenges to the operation of the power system. As the main means of peak regulation of new power systems, energy storage is becoming increasingly important in the energy balance of power systems. Compressed air energy storage technology is an emerging energy storage technology that stores pressure potential energy by compressing air. It has the characteristics of large capacity and long life, and has broad application prospects. It can be used as the main means to support the consumption of new energy and peak shaving and valley filling of power grids. However, during long-term operation, the equipment may be unstable. In order to ensure the normal operation of the compressed air energy storage system, it is necessary to evaluate and monitor the operating status of the compressed air energy storage system.

[0045] In view of the above situation, the present disclosure provides a compressed air energy storage system state assessment method based on a digital twin model. Figure 1 FIG. 1 is a flow chart of a method for evaluating a compressed air energy storage system state based on a digital twin model according to an exemplary embodiment. Figure 1 As shown, the compressed air energy storage system state assessment method based on the digital twin model includes:

[0046] Step 10: construct a three-dimensional model of the compressed air energy storage system based on the structural parameters of the components of the compressed air energy storage system when combined; wherein the three-dimensional model reflects the combined connection relationship of the components of the compressed air energy storage system;

[0047] Step 11: construct a digital twin model of the compressed air energy storage system based on the three-dimensional model of the compressed air energy storage system, the mechanism models corresponding to the components of the compressed air energy storage system, and the operating status data of the compressed air energy storage system during operation;

[0048] Step 12: Based on the evaluation index of the compressed air energy storage system and in combination with the digital twin model, a state evaluation system of the compressed air energy storage system is constructed;

[0049] Step 13: Based on the state assessment system of the compressed air energy storage system, a neural network model is trained to obtain a state assessment model of the compressed air energy storage system;

[0050] Step 14: Based on the state assessment model of the compressed air energy storage system, the operating state of the compressed air energy storage system is assessed in real time, and the assessment results are displayed in 3D in the digital twin model.

[0051] In this exemplary embodiment, each component includes at least the core components of the compressed air energy storage system, such as a compressor, a turbine, an air storage system, a heat storage system, and the like.

[0052] The combined connection relationship between the various components of the compressed air energy storage system includes the combined connection relationship between the compressor, the turbine, the air storage system, and the heat storage system.

[0053] In this exemplary embodiment, the mechanism model is an abstract description of the operating mechanism or physical law of a physical device. Through this clear mechanism model, the behavior or state of a physical object can be predicted through a virtual body.

[0054] In this application, the three-dimensional model structural parameters and the actual operating parameters of the compressed air energy storage power station can be brought into the mechanism model to construct a digital twin of the compressed air energy storage power station. The multi-time scale iterative simulation of the compressed air energy storage system is realized through the multi-physics field coupling differential equations and partial differential equations in the mechanism model, and the model simulation parameters are corrected by real-time comparison feedback with the field measured data to achieve the symbiosis of the digital twin and the physical entity, ensuring that the digital twin model integrates the real state parameters of the physical entity.

[0055] At the same time, a compressed air energy storage system status assessment model is established and embedded in the digital twin model for visual display. Based on the multi-dimensional heterogeneous status data of each component and the entire system of the compressed air energy storage system, the entropy weight method-approximate ideal point method evaluation algorithm and the neural network evaluation algorithm are integrated to generate a status assessment model for multi-dimensional indicators of the equipment body, connecting pipelines, auxiliary systems, and operating efficiency. Each device and the system as a whole are evaluated, and the corresponding evaluation results are projected on the digital twin model to show the real-time operating status of the compressed air energy storage power station. The evaluation results can be directly used for optimized operation and maintenance work such as fault prediction and warning, performance optimization control, and operating status prediction.

[0056] According to an embodiment of the present disclosure, a compressed air energy storage system state assessment method based on a digital twin model includes constructing a three-dimensional model of the compressed air energy storage system based on the structural parameters of the various components of the compressed air energy storage system when combined; wherein the three-dimensional model reflects the combined connection relationship of the various components of the compressed air energy storage system; constructing a digital twin model of the compressed air energy storage system based on the three-dimensional model of the compressed air energy storage system, the mechanism models corresponding to the various components of the compressed air energy storage system, and the operating state data of the compressed air energy storage system during operation; constructing a state assessment system of the compressed air energy storage system based on the evaluation indicators of the compressed air energy storage system and the digital twin model; training a neural network model based on the state assessment system of the compressed air energy storage system to obtain a state assessment model of the compressed air energy storage system; and based on the state assessment model of the compressed air energy storage system, performing real-time assessment of the operating state of the compressed air energy storage system, and displaying the assessment results in 3D in the digital twin model. In this application, by constructing a digital twin model and a state assessment model, real-time assessment of the operating status of a compressed air energy storage system and 3D display of the assessment results are achieved, which is conducive to real-time monitoring of the operating status of the compressed air energy storage system and three-dimensional stereoscopic image status display, thereby helping to improve the stability of the compressed air energy storage system.

[0057] In some embodiments, the evaluation index of the compressed air energy storage system includes at least one of the following:

[0058] The operating life of components of the compressed air energy storage system, fault diagnosis of components of the compressed air energy storage system, and the overall performance of the compressed air energy storage system;

[0059] The evaluation index based on the compressed air energy storage system is combined with the digital twin model to construct a state evaluation system of the compressed air energy storage system, including:

[0060] Based on the evaluation index of the compressed air energy storage system and combined with the operating status data in the digital twin model, a state evaluation system of the compressed air energy storage system is constructed; wherein the state evaluation system contains the corresponding association relationship between the operating status data and the evaluation index.

[0061] In this exemplary embodiment, the operating status data of the compressed air energy storage system during operation includes:

[0062] Compressor inlet gas temperature, compressor outlet gas temperature, compressor inlet gas pressure, compressor outlet gas pressure, compressor inlet gas mass flow, compressor outlet gas mass flow, compression transmission pipeline loss;

[0063] Expander inlet gas temperature, expander outlet gas temperature, expander inlet gas pressure, expander outlet gas pressure, expander inlet gas mass flow, expander outlet gas mass flow, expansion transmission pipeline loss;

[0064] The frequency and surge point of each mode of shaft system oscillation;

[0065] Internal pressure and leakage rate of gas storage tank;

[0066] Temperature and pressure inside the heat exchanger;

[0067] The degree of rotor wear, operating hours, number of cycles, operating temperature, and internal cleanliness of compressors and expanders;

[0068] The use time (lifespan) of the gas tank material and the number of gas tank filling and discharging cycles;

[0069] Heat exchanger materials, corrosiveness of heat exchanger media, etc.

[0070] In addition to the above operating status data, other operating status data can be supplemented according to actual needs. At the same time, other specific evaluation indicators can also be supplemented. The above multi-dimensional indicators together constitute the compressed air energy storage system status evaluation system.

[0071] In some embodiments, the state assessment model based on the compressed air energy storage system is used to assess the operating state of the compressed air energy storage system in real time, and the assessment result is displayed in 3D in the digital twin model, including:

[0072] Collecting real-time status data of the current operating status of the compressed air energy storage system;

[0073] Inputting the real-time status data into the status assessment model to obtain a status assessment result of the current operating status of the compressed air energy storage system;

[0074] The state assessment result is displayed in 3D in the digital twin model; wherein the state assessment result at least includes

[0075] An evaluation result corresponding to at least one indicator of the operating life of components of the compressed air energy storage system, fault diagnosis of components of the compressed air energy storage system, and overall performance of the compressed air energy storage system.

[0076] In this exemplary embodiment, the 3D display of the state assessment result in the digital twin model includes:

[0077] According to the evaluation results corresponding to the evaluation indicators, the status levels of the component operation statuses corresponding to the evaluation indicators are respectively divided;

[0078] The status level corresponding to each evaluation index is displayed in 3D at each component position in the digital twin model; wherein the status level includes at least four levels: normal, caution, abnormal and severe.

[0079] In this exemplary embodiment, for example, the operating life of components of the compressed air energy storage system can be divided into four levels: a normal state that is substantially the same as the scheduled life of the equipment when it leaves the factory, a caution state that is a certain amount shorter than the scheduled life of the equipment when it leaves the factory, a caution state that is a predetermined time shorter than the scheduled life of the equipment when it leaves the factory, and a severe state that is a long time shorter than the scheduled life of the equipment when it leaves the factory;

[0080] The component fault diagnosis of the compressed air energy storage system can be divided into four levels: normal state of component operation, attention state of component operation disturbance, abnormal state of component operation, and serious state of component operation serious abnormality;

[0081] The overall performance of the compressed air energy storage system can be divided into four levels: the normal state of the compressed air energy storage system, the warning state where the compressed air energy storage system performance is unstable, the abnormal state where the compressed air energy storage system performance is degraded, and the serious state where the compressed air energy storage system performance is seriously degraded.

[0082] In some embodiments, the 3D display of the state assessment result in the digital twin model includes:

[0083] Assign different colors to the four levels;

[0084] According to the evaluation results corresponding to the evaluation indicators of each component, the color of the status level corresponding to the evaluation result is mapped to each component area of ​​the digital twin model.

[0085] In this exemplary embodiment, based on the evaluation results, each component is divided into different status levels, and the four statuses of the evaluation level (normal, attention, abnormal, and severe) are assigned different colors, for example, green represents the normal state, yellow represents the attention state, orange represents the abnormal state, and red represents the severe state. Based on the actual evaluation results, different colors are mapped to different areas or components in the digital twin model to intuitively display the current status of the compressed air energy storage power station. For example, in the digital twin model, the entire compression side subsystem is decomposed into a combination of several compressors and heat exchangers. Through three-dimensional real-time rendering of different states of each device, the user can observe the three-dimensional stereoscopic image of the device by rotating the model. Based on the visualization result display scheme, the operation and maintenance personnel can quickly and easily understand the real-time operating status of the entire compressed air energy storage power station, which is convenient for subsequent adjustment of equipment parameters and timely formulation of corresponding maintenance plans.

[0086] In some embodiments, the digital twin model of the compressed air energy storage system is constructed based on the three-dimensional model of the compressed air energy storage system, the mechanism models corresponding to the components of the compressed air energy storage system, and the operating status data of the compressed air energy storage system during operation, including:

[0087] The calculation process and logic of the mechanism model are embedded into the corresponding components or objects of the 3D model, and the mechanism model is called for real-time calculation and simulation during the operation of the 3D model;

[0088] The calculation results of the mechanism model are mapped to the corresponding positions of the three-dimensional model, and compared with the operating status data of the compressed air energy storage system during operation, so as to couple the multi-dimensional data and laws of multi-physical fields, realize the real-time symbiosis of the physical entity and virtual entity of the compressed air energy storage system, and obtain the digital twin model.

[0089] In this exemplary embodiment, the digital twin model of the compressed air energy storage system is mainly composed of a three-dimensional model, a mechanism model and a twin data pool, which describes and characterizes the multiple time scales and multiple spatial dimensions of the compressed air energy storage system. The twin data pool contains the operating status data of the compressed air energy storage system during operation. The three-dimensional model, mechanism model and twin data pool are integrated to embed the calculation process and logic of the mechanism model into the corresponding components or objects of the three-dimensional model, and the mechanism model is called for real-time calculation and simulation during the operation of the three-dimensional model. The calculation results of the mechanism model are mapped to the corresponding positions of the three-dimensional model, and compared with the real-time operating status parameters collected by the real sensors in the twin data pool, coupling the multi-dimensional data and laws of multi-physical fields to realize the real-time symbiosis of the physical entity and virtual entity of the compressed air energy storage system.

[0090] Among them, the operating status data of the compressed air energy storage system during operation is the real data collected based on the state assessment requirements. The actual operating data of the compressed air energy storage power station is collected through monitoring equipment and sensor networks at the data acquisition end, including comprehensive perception and collection of electrical quantities, state quantities, environmental quantities, spatial quantities, and behavioral quantities; remote data collection is realized through embedded devices such as MCU (Microcontroller Unit) / FPGA (Field Programmable Gate Array) and wireless communication modules, and data is uploaded to the cloud using communication methods such as Ethernet to achieve separation of data collection and processing. Establish a local database and a suitable data management system for subsequent data access, query and update. Finally, the collected real data is cleaned and preprocessed, including removing outliers, filling missing values, data smoothing and other processing to ensure data quality and consistency.

[0091] In some embodiments, the three-dimensional model of the compressed air energy storage system is constructed based on the structural parameters of the components of the compressed air energy storage system when combined, including:

[0092] Obtain the structural parameters of each component of the compressed air energy storage system, and obtain the relative position and connection method between each component;

[0093] Import the structural parameters of each component into the modeling software and draw the wireframe model of the component;

[0094] Arrange the positions and connections of each component in the modeling software according to the actual layout to form a wireframe model of the entire system;

[0095] The wireframe model of the entire system is adjusted in detail as needed to obtain a three-dimensional model of the compressed air energy storage system.

[0096] In this exemplary embodiment, when constructing a three-dimensional model of a compressed air energy storage system, the real design parameters of each component of the compressed air energy storage power station can be obtained first, and the relative position and connection method between each component can be obtained. Secondly, a modeling software suitable for establishing a refined three-dimensional model is selected, a new project or scene is created in the modeling software, and the design parameters and layout parameters are imported. According to information such as size, shape and working parameters, an accurate component wireframe model is drawn. Again, according to the collected component layout parameters, the position and connection relationship of each component are arranged in the modeling software in the actual layout to form a wireframe model of the whole system. Finally, the model is further adjusted in detail as needed. For example, detailed materials, connecting parts, etc. are added to improve the realism and visualization of the model, and in the modeling process, the model is continuously verified and corrected to ensure the accuracy and consistency of the model, and finally the construction of a refined three-dimensional model of the compressed air energy storage system is completed.

[0097] In some embodiments, the mechanism models corresponding to the components of the compressed air energy storage system include at least one of the following:

[0098] The mechanism model of the compressor, the mechanism model of the turbine, the mechanism model of the gas storage system and the mechanism model of the heat storage system.

[0099] In some embodiments, the mechanism model of the compressor is as follows:

[0100] The outlet air temperature of the i-th stage compressor It can be expressed as

[0101] in, represents the compressor inlet air temperature, represents the compressor pressure ratio, κ represents the air adiabatic index, η c,irepresents the isentropic efficiency of the compressor;

[0102] Input power W of the i-th compressor c,i And the total compression energy W c,tol They are

[0103]

[0104]

[0105] Among them, m a Indicates the quality of compressed air, c p represents the specific heat capacity of air at constant pressure, N com Indicates the total number of compression stages.

[0106] In some embodiments, the mechanism model of the turbine is as follows:

[0107] The outlet air temperature of the i-th stage expander It can be expressed as

[0108]

[0109] in, represents the input air temperature of the expander, represents the expansion ratio, η e,i represents the isentropic efficiency of the expander;

[0110] The output power of the i-th stage expander W e,i and the total expansion power generation W e,tol for

[0111]

[0112]

[0113] Among them, N exp represents the total expansion order and κ represents the air adiabatic index.

[0114] In this exemplary embodiment,

[0115] The mechanism model of the gas storage system is as follows:

[0116] The relationship between the quality of compressed air and the pressure of the air storage chamber container is as follows:

[0117]

[0118] Among them, V st Indicates the volume of the air storage chamber, m a Indicates the quality of compressed air, R g is the universal gas constant, pr as is the gas pressure in the gas storage container, and T0 is the ambient temperature.

[0119] The mechanism model of the heat storage system is as follows:

[0120] Generalized efficiency of the pressure side and turbine side heat exchanger ε i satisfy

[0121]

[0122]

[0123] in, represents the inlet / outlet air temperature of the i-th stage heat exchanger, represents the inlet / outlet heat transfer fluid temperature of the i-th stage heat exchanger, m f,i represents the mass of heat transfer fluid flowing through the i-th stage heat exchanger, c F represents the specific heat capacity of the heat transfer fluid, m a Indicates the quality of compressed air, c p It represents the specific heat of air at constant pressure.

[0124] In this exemplary embodiment, based on the state assessment model of the compressed air energy storage system, when the operating state of the compressed air energy storage system is evaluated in real time, the life of the compressor can be predicted through parameters such as the compressor's pressure ratio, flow rate, speed, operating time, load, historical failure rate, rotor wear degree, number of cycles, operating temperature, internal cleanliness, etc.;

[0125] The life of the gas storage device can be predicted based on parameters such as pressure, temperature, leakage rate, material, number of cycles, medium corrosiveness, ambient temperature, humidity, etc.

[0126] The operating status and performance of the compressor, as well as possible faults of components such as valves, pistons, and bearings, can be predicted through the compressor's pressure, temperature, flow, power, speed, vibration and other parameters.

[0127] The gas storage capacity and sealing performance of the gas storage chamber, as well as possible faults in the gas storage device / heat storage tank wall, pipelines, valves and other components can be reflected through the parameters of the gas storage device, such as pressure, temperature, volume, and leakage rate.

[0128] The overall performance of the compressed air energy storage system can be predicted through parameters such as compressor compression ratio, expansion ratio and efficiency, gas storage device capacity, pressure, temperature, volume and leakage rate, heat exchange efficiency of heat exchange medium, control system accuracy, response time, etc.

[0129] Figure 2 FIG. 1 is a schematic diagram of a compressed air energy storage system module structure according to an exemplary embodiment. Figure 2As shown in the figure, the physical entity of the compressed air energy storage system includes equipment, data acquisition terminal, multi-data integrated sensor system, communication interface, etc. The equipment includes compressor, expander, heat exchanger, air storage chamber, transmission pipeline, etc. The data acquisition terminal includes sensor network, video monitoring network, infrared monitoring network, etc. The communication interface includes Ethernet RS232, etc. Remote data acquisition is achieved through embedded devices such as MCU / Zynq / FPGA and wireless communication modules.

[0130] The virtual entity of the compressed air energy storage system includes twin models, evaluation algorithms, etc. The evaluation algorithms include entropy weight method, grey correlation analysis method, and radial basis function neural network.

[0131] Entropy is a measure of the disorder of a system. For an indicator, the greater its information entropy, the greater the degree of dispersion of the indicator, the greater the impact of the indicator on the comprehensive rating, and the weight of this item should be increased. Therefore, the entropy weight method can appropriately set appropriate weight coefficients for each indicator. Grey correlation analysis is an algorithm that measures the degree of correlation between factors based on the similarity or difference in the development trends between factors. However, the simple statistical analysis and evaluation algorithm is prone to the influence of subjective factors, so a neural network can be further added for evaluation to obtain a comprehensive evaluation result. As a variant of the BP neural network, the radial basis function neural network is superior to the BP neural network in terms of approximation ability, classification ability and learning speed. It has a simple structure, concise training and fast learning convergence speed, so it is suitable for use as a state evaluation algorithm. In summary, the entropy weight method combined with the approximate ideal point method can be used to construct a conventional statistical state assessment model, and the neural network evaluation algorithm combined with massive data can be used to construct an AI model. Combined with the demand target indicators in the above-mentioned indicator evaluation system, the corresponding multi-model fusion compressed air energy storage state assessment model containing the required evaluation indicators (such as equipment life prediction, equipment fault diagnosis, system performance analysis, etc.) is trained to achieve the evaluation of each state of compressed air energy storage to reflect the true state of the compressed air energy storage system.

[0132] The evaluation algorithm can be used to train the state evaluation model, realize equipment life prediction, equipment fault diagnosis and system performance analysis, and real-time evaluation of operating efficiency and response time. The state evaluation analysis results are then mapped to a three-dimensional model (twin model) for visual display of the results.

[0133] Figure 3 FIG. 1 is a flow chart of compressed air energy storage system status evaluation according to an exemplary embodiment. Figure 3 As shown, the process includes:

[0134] Step 30, obtaining the mechanism model of each component of compressed air energy storage;

[0135] Step 31, obtaining the design layout parameters of the compressed air energy storage power station;

[0136] Step 32, constructing a full system mechanism model of compressed air energy storage;

[0137] Step 33: construct a three-dimensional model of the compressed air energy storage system based on the design and structural parameters;

[0138] Step 34: construct a digital twin model of a compressed air energy storage power station based on the three-dimensional model, mechanism model and operation status data of the compressed air energy storage system;

[0139] Step 35: Set multi-dimensional evaluation indicators, combine the grey correlation-TOPSIS algorithm and the radial basis neural network algorithm to calculate the indicator weights, calculate the evaluation results used to characterize the compressed air energy storage power station, and display the evaluation results in the corresponding parts of the digital twin model.

[0140] The present invention provides a compressed air energy storage system state assessment device based on a digital twin model. Figure 4 FIG. 1 is a schematic diagram of a compressed air energy storage system state assessment device based on a digital twin model according to an exemplary embodiment. Figure 4 As shown, the compressed air energy storage system state assessment device based on the digital twin model includes:

[0141] A first model building module 40 is used to build a three-dimensional model of the compressed air energy storage system based on the structural parameters of the components of the compressed air energy storage system when combined; wherein the three-dimensional model reflects the combined connection relationship of the components of the compressed air energy storage system;

[0142] A second model building module 41 is used to build a digital twin model of the compressed air energy storage system based on the three-dimensional model of the compressed air energy storage system, the mechanism models corresponding to the components of the compressed air energy storage system, and the operating status data of the compressed air energy storage system during operation;

[0143] An evaluation system construction module 42 is used to construct a state evaluation system of the compressed air energy storage system based on the evaluation index of the compressed air energy storage system and in combination with the digital twin model;

[0144] A model training module 43 is used to perform neural network model training based on the state assessment system of the compressed air energy storage system to obtain a state assessment model of the compressed air energy storage system;

[0145] The state assessment module 44 is used to perform real-time assessment of the operating state of the compressed air energy storage system based on the state assessment model of the compressed air energy storage system, and to display the assessment results in 3D in the digital twin model.

[0146] In some embodiments, the evaluation index of the compressed air energy storage system includes at least one of the following:

[0147] The operating life of components of the compressed air energy storage system, fault diagnosis of components of the compressed air energy storage system, and the overall performance of the compressed air energy storage system;

[0148] The evaluation system building blocks are used to

[0149] Based on the evaluation index of the compressed air energy storage system and combined with the operating status data in the digital twin model, a state evaluation system of the compressed air energy storage system is constructed; wherein the state evaluation system contains the corresponding association relationship between the operating status data and the evaluation index.

[0150] In some embodiments, the status assessment module is used to

[0151] Collecting real-time status data of the current operating status of the compressed air energy storage system;

[0152] Inputting the real-time status data into the status assessment model to obtain a status assessment result of the current operating status of the compressed air energy storage system;

[0153] The state assessment result is displayed in 3D in the digital twin model; wherein the state assessment result at least includes

[0154] An evaluation result corresponding to at least one indicator of the operating life of components of the compressed air energy storage system, fault diagnosis of components of the compressed air energy storage system, and overall performance of the compressed air energy storage system.

[0155] In some embodiments, the 3D display of the state assessment result in the digital twin model includes:

[0156] According to the evaluation results corresponding to the evaluation indicators, the status levels of the component operation statuses corresponding to the evaluation indicators are respectively divided;

[0157] The status level corresponding to each evaluation index is displayed in 3D at each component position in the digital twin model; wherein the status level includes at least four levels: normal, caution, abnormal and severe.

[0158] In some embodiments, the 3D display of the state assessment result in the digital twin model includes:

[0159] Assign different colors to the four levels;

[0160] According to the evaluation results corresponding to the evaluation indicators of each component, the color of the status level corresponding to the evaluation result is mapped to each component area of ​​the digital twin model.

[0161] In some embodiments, the second model building module is used to

[0162] The calculation process and logic of the mechanism model are embedded into the corresponding components or objects of the 3D model, and the mechanism model is called for real-time calculation and simulation during the operation of the 3D model;

[0163] The calculation results of the mechanism model are mapped to the corresponding positions of the three-dimensional model, and compared with the operating status data of the compressed air energy storage system during operation, so as to couple the multi-dimensional data and laws of multi-physical fields, realize the real-time symbiosis of the physical entity and virtual entity of the compressed air energy storage system, and obtain the digital twin model.

[0164] In some embodiments, the first model building module is used to

[0165] Obtain the structural parameters of each component of the compressed air energy storage system, and obtain the relative position and connection method between each component;

[0166] Import the structural parameters of each component into the modeling software and draw the wireframe model of the component;

[0167] Arrange the positions and connections of each component in the modeling software according to the actual layout to form a wireframe model of the entire system;

[0168] The wireframe model of the entire system is adjusted in detail as needed to obtain a three-dimensional model of the compressed air energy storage system.

[0169] In some embodiments, the mechanism models corresponding to the components of the compressed air energy storage system include at least one of the following:

[0170] The mechanism model of the compressor, the mechanism model of the turbine, the mechanism model of the gas storage system and the mechanism model of the heat storage system.

[0171] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or for use with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing in a suitable manner if necessary, and then stored in a computer memory.

[0172] It should be understood that the various parts of the present disclosure can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in 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 (PGA), a field programmable gate array (FPGA), etc.

[0173] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be mentioned in any one or more embodiments or examples in an appropriate manner.

[0174] In the description of the present disclosure, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present disclosure and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present disclosure.

[0175] In addition, the terms "first", "second", etc. used in the embodiments of the present disclosure are only used for descriptive purposes and should not be understood as indicating or implying relative importance, or implicitly indicating the number of technical features indicated in the embodiments. Therefore, the features defined by the terms "first", "second", etc. in the embodiments of the present disclosure may explicitly or implicitly indicate that at least one of the features is included in the embodiment. In the description of the present disclosure, the word "multiple" means at least two or two or more, such as two, three, four, etc., unless otherwise clearly and specifically defined in the embodiments.

[0176] In the present disclosure, unless otherwise clearly specified or limited in the embodiments, the terms "installed", "connected", "connected" and "fixed" etc. appearing in the embodiments should be understood in a broad sense. For example, the connection can be a fixed connection, a detachable connection, or an integrated connection. It can be understood that it can also be a mechanical connection, an electrical connection, etc.; of course, it can also be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal connection of two elements, or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present disclosure can be understood according to the specific implementation situation.

[0177] In the present disclosure, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0178] Although the embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present disclosure. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present disclosure.

Claims

1. A compressed air energy storage system state assessment method based on a digital twin model, characterized in that: include: Based on the structural parameters of the components of the compressed air energy storage system when combined, a three-dimensional model of the compressed air energy storage system is constructed; wherein the three-dimensional model reflects the combined connection relationship of the components of the compressed air energy storage system; wherein, based on the structural parameters of the components of the compressed air energy storage system when combined, the three-dimensional model of the compressed air energy storage system is constructed, including: obtaining the structural parameters of the components of the compressed air energy storage system, and obtaining the relative positions and connection methods between the components; importing the structural parameters of the components into the modeling software, and drawing the wireframe model of the components; arranging the positions and connection relationships of the components in the modeling software according to the actual layout to form a wireframe model of the whole system; and adjusting the details of the wireframe model of the whole system as needed to obtain the three-dimensional model of the compressed air energy storage system; Constructing a digital twin model of the compressed air energy storage system based on the three-dimensional model of the compressed air energy storage system, the mechanism models corresponding to the components of the compressed air energy storage system, and the operating status data of the compressed air energy storage system during operation; Based on the evaluation index of the compressed air energy storage system and in combination with the digital twin model, a state evaluation system of the compressed air energy storage system is constructed; Based on the state assessment system of the compressed air energy storage system, a neural network model is trained to obtain a state assessment model of the compressed air energy storage system; Based on the state evaluation model of the compressed air energy storage system, the operating state of the compressed air energy storage system is evaluated in real time, and the evaluation results are displayed in 3D in the digital twin model; the evaluation index of the compressed air energy storage system includes at least one of the following: The operating life of components of the compressed air energy storage system, fault diagnosis of components of the compressed air energy storage system, and the overall performance of the compressed air energy storage system; The evaluation index based on the compressed air energy storage system is combined with the digital twin model to construct a state evaluation system of the compressed air energy storage system, including: Based on the evaluation index of the compressed air energy storage system and in combination with the operating status data in the digital twin model, a state evaluation system of the compressed air energy storage system is constructed; wherein the state evaluation system contains the corresponding correlation relationship between the operating status data and the evaluation index; the state evaluation model based on the compressed air energy storage system evaluates the operating status of the compressed air energy storage system in real time, and displays the evaluation results in 3D in the digital twin model, including: Collecting real-time status data of the current operating status of the compressed air energy storage system; Inputting the real-time status data into the status assessment model to obtain a status assessment result of the current operating status of the compressed air energy storage system; The state assessment result is displayed in 3D in the digital twin model; wherein the state assessment result at least includes An evaluation result corresponding to at least one indicator of the operating life of components of the compressed air energy storage system, fault diagnosis of components of the compressed air energy storage system, and overall performance of the compressed air energy storage system; The mechanism models corresponding to the components of the compressed air energy storage system include at least one of the following: The mechanism model of the compressor, the mechanism model of the turbine, the mechanism model of the gas storage system and the mechanism model of the heat storage system; The mechanism model of the compressor is as follows: The outlet air temperature of the i-th stage compressor It can be expressed as in, represents the compressor inlet air temperature, represents the compressor pressure ratio, κ represents the air adiabatic index, η c,i represents the isentropic efficiency of the compressor; Input power W of the i-th compressor c,i And the total compression energy W c,tol They are Among them, m a Indicates the quality of compressed air, c p represents the specific heat capacity of air at constant pressure, N com represents the total number of compression stages; the mechanism model of the turbine is as follows: The outlet air temperature of the i-th stage expander It can be expressed as in, represents the input air temperature of the expander, represents the expansion ratio, η e,i represents the isentropic efficiency of the expander; The output power of the i-th stage expander W e,i and the total expansion power generation W e,tol for Among them, N exp represents the total expansion order, κ represents the air adiabatic index; The mechanism model of the gas storage system is as follows: The relationship between the quality of compressed air and the pressure of the air storage chamber container is as follows: Among them, V st Indicates the volume of the gas storage chamber, m a Indicates the quality of compressed air, R g is the universal gas constant, pr as is the gas pressure in the gas storage container, T0 is the ambient temperature; The mechanism model of the heat storage system is as follows: Generalized efficiency of the pressure side and turbine side heat exchanger ε i satisfy in, represents the inlet / outlet air temperature of the i-th stage heat exchanger, represents the inlet / outlet heat transfer fluid temperature of the i-th stage heat exchanger, m f,i represents the mass of heat transfer fluid flowing through the i-th stage heat exchanger, c F represents the specific heat capacity of the heat transfer fluid, m a Indicates the quality of compressed air, c p It represents the specific heat of air at constant pressure.

2. The compressed air energy storage system state assessment method based on the digital twin model according to claim 1 is characterized in that: The 3D display of the state assessment result in the digital twin model includes: According to the evaluation results corresponding to the evaluation indicators, the status levels of the component operation statuses corresponding to the evaluation indicators are respectively divided; The status level corresponding to each evaluation index is displayed in 3D at each component position in the digital twin model; wherein the status level includes at least four levels: normal, caution, abnormal and severe.

3. The compressed air energy storage system state assessment method based on the digital twin model according to claim 2 is characterized in that: The 3D display of the state assessment result in the digital twin model includes: Assign different colors to the four levels; According to the evaluation results corresponding to the evaluation indicators of each component, the color of the status level corresponding to the evaluation result is mapped to each component area of ​​the digital twin model.

4. The compressed air energy storage system state assessment method based on the digital twin model according to claim 1 is characterized in that: The digital twin model of the compressed air energy storage system is constructed based on the three-dimensional model of the compressed air energy storage system, the mechanism models corresponding to the components of the compressed air energy storage system, and the operating status data of the compressed air energy storage system during operation, including: The calculation process and logic of the mechanism model are embedded into the corresponding components or objects of the 3D model, and the mechanism model is called for real-time calculation and simulation during the operation of the 3D model; The calculation results of the mechanism model are mapped to the corresponding positions of the three-dimensional model, and compared with the operating status data of the compressed air energy storage system during operation, so as to couple the multi-dimensional data and laws of multi-physical fields, realize the real-time symbiosis of the physical entity and virtual entity of the compressed air energy storage system, and obtain the digital twin model.

Images