A method and device for monitoring characteristics in a thermal storage device based on a full-dimensional observer

Abstract

The application discloses a kind of based on full-dimensional observer's heat storage device internal characteristic monitoring method and device, device includes data acquisition unit, digital twin model unit and full-dimensional observer correction unit;Method level, the numerical simulation model of heat storage is discretized into Euler format, and the digital twin model of heat storage device is established;The inlet temperature, flow and outlet temperature data of real heat storage device are collected, and the prediction deviation of inlet and outlet temperature is used, and the predicted temperature field is corrected in real time by feedback to digital twin model unit in full-dimensional observer correction unit.It is only necessary to measure flow, inlet and outlet temperature to accurately obtain internal temperature distribution and energy storage state of heat storage device by relying on the device and method, thereby effectively solving the sensing bottleneck of heat storage internal characteristic "black box", adapting to packed bed, shell and tube and various heat storage device types, to provide reliable internal characteristic sensing method and hardware support for supply-demand matching and accurate regulation of heat storage system under dynamic conditions.

Description

Technical Field

[0001] This invention belongs to the field of thermal energy storage and dynamic control technology, and relates to a method and device for monitoring the internal characteristics of a thermal storage device based on a full-dimensional observer. Background Technology

[0002] As one of the core equipment for energy supply and demand regulation and renewable energy consumption, the performance of thermal storage devices is affected by the coupling of multiple factors such as the physical properties of thermal storage materials, the structure of device units, and operating conditions. The coupling involves multiple physical field characteristics and the internal characteristic correlation mechanism is complex.

[0003] In real-time operation, thermal storage devices need to match dynamically fluctuating load demands. Due to the complex internal geometry of the device (such as particle distribution in the packed bed and flow channel layout in the shell-and-tube type), the nonlinear changes in the medium state during phase change thermal storage, and the limitations of sensor deployment costs and maintenance difficulties, it is difficult to comprehensively deploy sensors at key internal monitoring points. As a result, core internal characteristics such as internal temperature distribution and real-time energy storage status of the thermal storage device cannot be directly obtained.

[0004] Existing thermal energy storage system control technologies largely rely on empirical judgments based on easily measurable external parameters such as outlet temperature. This only indirectly infers the system's operating status and fails to reflect the actual internal heat transfer process. This not only limits the effectiveness of control but also results in poor adaptability and limited generalization for different types and temperature zones of thermal energy storage devices, making it difficult to meet the precise supply-demand matching requirements of large-scale, dynamic operating systems. Therefore, there is an urgent need for an internal characteristic monitoring method that can overcome the "black box" limitation of the internal state of thermal energy storage devices, adapt to multiple types of devices, and be cost-effective, in order to support the efficient operation and control of the system. Summary of the Invention

[0005] To overcome the shortcomings of the prior art, the present invention aims to provide a method and device for monitoring the internal characteristics of a thermal storage device based on a full-dimensional observer. This method and device address the technical problem that it is difficult to directly obtain the internal temperature distribution and energy storage status of a thermal storage device when it is affected by dynamic fluctuations in external demand. It enables "white-box" perception of the internal characteristics of thermal storage and provides reliable support for the precise operation and control of the thermal storage system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: (I) A method for monitoring the internal characteristics of thermal storage devices based on a full-dimensional observer.

[0007] The specific steps of this method are as follows: 1. Discretization of numerical simulation models and construction of digital twin models: The digital twin model is constructed by discretizing the numerical simulation model of the thermal storage device using the Eulerian scheme. Specifically, the numerical simulation model of the thermal storage device is discretized into the Eulerian scheme to construct a corresponding digital twin model of the thermal storage device, which is used to simulate the operating state of the real device. The state-space expression is: in, x This represents the predicted temperature field vector from the digital twin model. To predict the rate of change of the temperature field vector, u The input vector consists of inlet temperature and flow rate. A The state matrix, B The input matrix is ​​denoted as .

[0008] 2. Real-time data acquisition: The inlet temperature and inlet flow rate of the actual thermal storage device are collected as inputs to the digital twin model, while the inlet temperature and outlet temperature data of the actual thermal storage device are collected as inputs for error correction.

[0009] 3. Digital twin model prediction: The collected inlet temperature and flow rate data are input into the data twin model, and the model outputs a predicted temperature field (reflecting the internal temperature distribution of the device) and predicted inlet and outlet temperatures.

[0010] 4. Error calculation and real-time model correction: The differences between the actual inlet temperature and the predicted inlet temperature, and between the actual outlet temperature and the predicted outlet temperature are calculated to obtain the prediction error. The prediction error is then fed back to the digital twin model through a full-dimensional observer to adjust the predicted temperature field vector of the digital twin model. x This enables real-time correction of the predicted temperature field, expressed as follows: in, This represents the inlet and outlet temperatures of a real thermal storage device. C The coefficient matrix, L Let be the gain matrix of the full-dimensional observer.

[0011] As time goes by, x The temperature distribution gradually becomes consistent with that inside a real thermal storage device.

[0012] (ii) Internal characteristic monitoring device of thermal storage device.

[0013] This device serves as the hardware platform for the aforementioned monitoring method. It achieves the entire process of the method through the collaborative operation of multiple functional units, specifically including: 1. Data Acquisition Unit: The "real-time data acquisition" step of the corresponding method connects to the heat storage inlet and outlet of the actual heat storage device, and is equipped with temperature sensors and flow sensors to collect the inlet temperature, inlet flow rate and outlet temperature data of the actual heat storage device, and synchronously transmits the data to subsequent units.

[0014] 2. Digital Twin Model Unit: The corresponding method includes the steps of "numerical simulation model discretization and digital twin model construction" and "digital twin model unit prediction". The digital twin model of the thermal storage device is pre-stored. After receiving the inlet temperature and flow data from the data acquisition unit, the digital twin model is run to output the predicted temperature field and the predicted inlet and outlet temperatures.

[0015] 3. Full-Dimensional Observer Correction Unit: The "error calculation and real-time model correction" step of the corresponding method is connected to the data acquisition unit and the digital twin model unit respectively. It receives the actual inlet and outlet temperatures of the data acquisition unit and the predicted inlet and outlet temperatures of the digital twin model unit. After calculating the difference between the actual inlet temperature and the predicted inlet temperature, and the actual outlet temperature and the predicted outlet temperature to obtain the prediction error, the prediction error is fed back to the digital twin model unit through the observer gain so as to perform real-time correction of the twin model.

[0016] In one embodiment, the numerical simulation model of the thermal storage device in step one is a numerical model describing the heat transfer law inside the thermal storage device.

[0017] In one embodiment, the thermal storage device includes a packed bed thermal storage device, a shell-and-tube thermal storage device, etc.; in step one, the state matrix is ​​adjusted through the derivation of a numerical simulation model. A Input matrix B The parameters enable the digital twin model to be adapted to different types of thermal storage devices.

[0018] In one embodiment, the predicted temperature field vector in step one x The vector contains temperature values ​​for each region inside the thermal storage device, and the number of elements in the vector is equal to the number of infinitesimal elements in the numerical simulation model.

[0019] In one embodiment, the acquisition frequency of the inlet temperature, inlet flow rate, and outlet temperature data is not less than 10Hz.

[0020] Compared with existing technologies, the method and device for monitoring the internal characteristics of a thermal storage device based on a full-dimensional observer, as described in this invention, have the following significant advantages and positive effects: 1. Break through the bottleneck of "black box" internal characteristics: Only by measuring the inlet temperature, flow rate and outlet temperature, the internal temperature distribution and energy storage status of the thermal storage device can be accurately perceived.

[0021] 2. High adaptability: It is compatible with various types of thermal storage devices such as packed bed and shell-and-tube. By adjusting the parameters of the digital twin model, it can be adapted to devices with different structures, demonstrating strong generalization ability.

[0022] 3. Low cost: It eliminates the need to deploy a large number of sensors inside the device, thus avoiding the deployment and maintenance costs of internal sensors.

[0023] 4. Supports precise control: Provides reliable internal characteristic data support for supply and demand matching and operation optimization of thermal storage systems under dynamic operating conditions. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the packed bed thermal storage device and the discrete micro-element of the present invention.

[0025] Figure 2 This is a schematic diagram of the characteristic monitoring method for a thermal storage device based on a full-dimensional observer according to the present invention.

[0026] Figure 3 The results of internal characteristic monitoring (temperature distribution) of the packed bed thermal storage device of the present invention are shown.

[0027] Figure 4 This is the monitoring result of the internal characteristics (residual heat) of the packed bed thermal storage device of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0029] A brief overview of this disclosure is provided below to offer a basic understanding of certain aspects of it. It should be understood that this overview is not an exhaustive summary of the disclosure. It is not intended to identify key or essential parts of the disclosure, nor is it intended to limit its scope. Its purpose is merely to present certain concepts in a simplified form as a prelude to the more detailed description that follows.

[0030] Taking a packed bed thermal storage device as an example, such as Figure 1 As shown, this paper describes a method and device for monitoring the internal characteristics of a thermal storage device based on a full-dimensional observer. A pseudo-two-dimensional concentric-diffusion model is often used to simulate the heat storage and release process in packed bed thermal storage devices. The energy equations for the heat transfer fluid and the thermal storage sphere are as follows: The initial conditions of the model, the heat transfer fluid, and the boundary conditions of the thermal storage sphere are shown below, where the heat transfer fluid inlet is a first-type boundary condition and the outer wall of the thermal storage sphere is a third-type boundary condition.

[0031] The above governing equations, when the time term is solved using the Euler method, the diffusion term is discretized using a second-order central difference scheme, and the unsteady-state and convection terms are discretized using a first-order upwind scheme, can be written in the form of a state-space expression: In the formula, x The temperature vector representing the discrete infinitesimal element includes the fluid temperature and the thermal storage sphere temperature of each infinitesimal element. u This indicates the heat transfer fluid velocity and inlet temperature of the packed bed. y This indicates the inlet and outlet temperatures of the heat transfer fluid.

[0032] Let the number of infinitesimal temperature elements of the heat transfer fluid along the height of the packed bed be... N H The number of infinitesimal elements along the radial direction of the thermal storage sphere is N R , x Vectors contain N H + N H × N R There are [number] elements. The elements are arranged as follows: The derived A , B The matrices are shown in Tables 1 and 2. A It is ( N H + N H × N R ) × ( N H + N H × N R The matrix of ) B It includes N H + N H × N R A one-dimensional column vector of elements. Define the collected inlet and outlet temperatures of the heat transfer fluid as the system output. y ,therefore C In the matrix, the indices are 1 and N HThe element value is 1, and the rest are 0. (Approximately assuming that the first and last infinitesimal elements represent the inlet and outlet temperatures.) Table 1. Heat transfer fluids A , B matrix Table 2 Thermal storage balls A , B matrix In the above formula, u This indicates the flow rate of the heat transfer fluid. >0 indicates that the fluid flows upward from the bottom, indicating a heat storage state, while <0 indicates that the fluid flows downward from the top, indicating a heat release state. q m Indicates the mass flow rate of the heat transfer fluid; T f,in Indicates the inlet temperature of the heat transfer fluid; ρ f Indicates the density of the heat transfer fluid; c p,f This indicates the specific heat capacity of the heat transfer fluid; k eff The equivalent thermal conductivity of the heat transfer fluid is equal to its porosity. ε Thermal conductivity of the heat transfer fluid k f The product of; d V d represents the infinitesimal volume along the height direction; H The length of the infinitesimal element in the height direction; h V Indicates the volumetric heat transfer coefficient; ρ p This indicates the density of the thermal storage sphere; c p,p This indicates the specific heat capacity of the thermal storage sphere; k p This indicates the thermal conductivity of the heat storage sphere; d p Indicates the particle size of the thermal storage spheres; d r This represents the length of a micro-element in the radial direction of the thermal storage sphere; h p This represents the convective heat transfer coefficient on the surface of the thermal storage sphere.

[0033] Based on the above derivation, a digital twin model of the packed bed thermal storage device is obtained.

[0034] Methods for monitoring the internal characteristics of thermal storage devices based on full-dimensional observers, such as... Figure 2As shown, the inlet temperature and inlet flow rate of the thermal storage device are collected as inputs to the digital twin model. The model outputs a predicted temperature field (reflecting the internal temperature distribution of the device) and predicted inlet and outlet temperatures. Inlet and outlet temperature data are collected as inputs for error correction. The differences between the actual inlet temperature and the predicted inlet temperature, and between the actual outlet temperature and the predicted outlet temperature are calculated to obtain the prediction error. The prediction error is fed back to the digital twin model through a full-dimensional observer to adjust the internal state vector of the digital twin model. x This enables real-time correction of the predicted temperature field.

[0035] The results of internal characteristic monitoring are as follows Figure 3 , Figure 4 As shown, theoretical results indicate that this method can achieve real-time monitoring of the internal characteristics of the thermal storage device, with a temperature error of less than 2 K and a thermal storage capacity estimation error of less than 2%, thus supporting the efficient operation and control of the system.

[0036] As can be seen, relying on this device and method, the present invention only needs to measure the flow rate and inlet and outlet temperatures to accurately obtain the internal temperature distribution and energy storage status of the thermal storage device, effectively breaking through the perception bottleneck of the "black box" internal characteristics of thermal storage, adapting to various types of thermal storage devices such as packed beds and shell-and-tube types, and providing a reliable internal characteristic perception method and hardware support for the supply and demand matching and precise control of thermal storage systems under dynamic operating conditions.

[0037] The above specific embodiments are only for illustrating the technical concept and structural features of the present invention, and are intended to enable those skilled in the art to implement them. However, the above content does not limit the scope of protection of the present invention. Any equivalent changes or modifications made based on the technical features of the present invention should fall within the scope of protection of the present invention.

[0038] The foregoing description of this disclosure in conjunction with specific implementation schemes is exemplary and not intended to limit the scope of protection of this disclosure. Those skilled in the art can make various modifications and variations to this disclosure based on its spirit and principles, and such modifications and variations are also within the scope of this disclosure.

Claims

1. A method for monitoring the internal characteristics of a thermal storage device based on a full-dimensional observer, characterized in that, Includes the following steps: Step 1: Discretize the numerical simulation model of the thermal storage device into an Eulerian scheme to establish a digital twin model of the thermal storage device; wherein, the expression of the digital twin model is: , x This represents the predicted temperature field vector from the digital twin model. To predict the rate of change of the temperature field vector, u The input vector consists of inlet temperature and flow rate. A The state matrix, B The input matrix; Step 2: Collect the inlet temperature and flow rate of the actual thermal storage device as input to the digital twin model, and run the digital twin model to output the predicted temperature field and predicted inlet and outlet temperatures; Step 3: Calculate the difference between the inlet temperature of the actual thermal storage device and the inlet temperature predicted by the digital twin model, and the difference between the outlet temperature and the outlet temperature predicted by the digital twin model to obtain the prediction error. Feedback the prediction error to the digital twin model through a full-dimensional observer to adjust the temperature field vector predicted by the digital twin model. x This enables real-time correction of the predicted temperature field. The mathematical expression for the correction method is as follows: in, This represents the inlet and outlet temperatures of a real thermal storage device. C The coefficient matrix, L Let be the gain matrix of the full-dimensional observer.

2. The method for monitoring the internal characteristics of a thermal storage device based on a full-dimensional observer according to claim 1, characterized in that, In step one, the numerical simulation model of the thermal storage device is a numerical model that describes the heat transfer law inside the thermal storage device.

3. The method for monitoring the internal characteristics of a thermal storage device based on a full-dimensional observer according to claim 1, characterized in that, The thermal storage device is a packed bed thermal storage device or a shell-and-tube thermal storage device; in step one, the state matrix is ​​adjusted through the derivation of the numerical simulation model. A Input matrix B The parameters enable the digital twin model to be adapted to different types of thermal storage devices.

4. The method for monitoring the internal characteristics of a thermal storage device based on a full-dimensional observer according to claim 1, characterized in that, The predicted temperature field vector x The vector contains temperature values ​​for each region inside the thermal storage device, and the number of elements in the vector is equal to the number of infinitesimal elements in the numerical simulation model.

5. The method for monitoring the internal characteristics of a thermal storage device based on a full-dimensional observer according to claim 1, characterized in that, The time term of the numerical simulation model is solved using the Euler method, the diffusion term is discretized using a second-order central difference scheme, and the unsteady-state term and convection term are discretized using a first-order upwind scheme, thus obtaining the digital twin model of the thermal storage device.

6. The method for monitoring the internal characteristics of a thermal storage device based on a full-dimensional observer according to claim 5, characterized in that, The thermal storage device is a packed bed thermal storage device. The numerical simulation model is established using the concentric-diffusion method, and the input vector... u The heat transfer fluid velocity and inlet temperature of the packed bed are represented by the predicted temperature field vector. x This represents the temperature vector of discrete infinitesimal elements, including the temperature of each infinitesimal fluid element and the temperature of each infinitesimal heat storage sphere. Let the number of infinitesimal temperature elements of the heat transfer fluid along the height of the packed bed be . N H The number of temperature infinitesimals along the radial direction of the thermal storage sphere is N R , x Include N H + N H × N R There are 10 elements, arranged as follows: In the formula, T f ( i ) indicates the first i The heat transfer fluid temperature elements are arranged sequentially from the bottom of the heat storage device upwards as 1, 2, ... N H ; T p ( i , j ) indicates the height direction of the thermal storage sphere. i The radial direction from the center of the sphere towards the surface is... j The temperature of each micro-element is represented radially from the center of the sphere towards the surface micro-elements as 1, 2, ... N R .

7. The method for monitoring the internal characteristics of a thermal storage device based on a full-dimensional observer according to claim 6, characterized in that, State matrix A It is ( N H + N H N R ) × ( N H + N H N R The matrix, input matrix B It includes N H + N H N R A one-dimensional column vector of elements, coefficient matrix C The index is 1 and N H The element with a value of 1, and the rest with 0; the state matrix of the heat transfer fluid. A Input matrix B as follows: Heat transfer fluid temperature element 1: Heat transfer fluid temperature micro-element 2~ N H -1: Heat transfer fluid temperature micro-element N H : State matrix of thermal storage sphere A Input matrix B as follows: Thermal storage ball temperature micro-element 1: Thermal storage ball temperature micro-element 2~ N R : Thermal storage ball temperature micro-element N R : In the above formula, u This indicates the flow rate of the heat transfer fluid. >0 indicates that the fluid flows upward from the bottom, indicating a heat storage state, while <0 indicates that the fluid flows downward from the top, indicating a heat release state. q m Indicates the mass flow rate of the heat transfer fluid; T f,in Indicates the inlet temperature of the heat transfer fluid; ρ f Indicates the density of the heat transfer fluid; c p,f This indicates the specific heat capacity of the heat transfer fluid; k eff The equivalent thermal conductivity of the heat transfer fluid is equal to its porosity. ε Thermal conductivity of the heat transfer fluid k f The product of; d V d represents the infinitesimal volume along the height direction; H The length of the infinitesimal element in the height direction; h V Indicates the volumetric heat transfer coefficient; ρ p This indicates the density of the thermal storage sphere; c p,p This indicates the specific heat capacity of the thermal storage sphere; k p This indicates the thermal conductivity of the heat storage sphere; d p Indicates the particle size of the thermal storage spheres; d r This represents the length of a micro-element in the radial direction of the thermal storage sphere; h p This represents the convective heat transfer coefficient on the surface of the thermal storage sphere.

8. A characteristic monitoring device for a thermal storage device based on a full-dimensional observer, characterized in that, The method for monitoring the internal characteristics of a thermal storage device based on a full-dimensional observer as described in claim 1 includes: Data acquisition unit: connected to the thermal storage inlet and outlet of the actual thermal storage device, and equipped with temperature sensor and flow sensor, used to collect the inlet temperature, inlet flow rate and outlet temperature data of the actual thermal storage device, and transmit the data synchronously; Digital twin model unit: Pre-stores a digital twin model of the thermal storage device, which is constructed by discretizing the numerical simulation model of the thermal storage device using the Eulerian method; the digital twin model unit receives the inlet temperature, outlet temperature, and flow rate data transmitted by the data acquisition unit, and runs the digital twin model to output the predicted temperature field and the predicted inlet and outlet temperatures; The full-dimensional observer correction unit is connected to the data acquisition unit and the digital twin model unit respectively. It receives the actual inlet and outlet temperatures transmitted by the data acquisition unit and the predicted inlet and outlet temperatures transmitted by the digital twin model unit. It calculates the difference between the actual inlet temperature and the predicted inlet temperature, and the difference between the actual outlet temperature and the predicted outlet temperature to obtain the prediction error. The prediction error is fed back to the digital twin model unit through the observer gain to perform real-time correction on the digital twin model.

9. The thermal storage device characteristic monitoring device based on a full-dimensional observer according to claim 2, characterized in that, The acquisition frequency of the inlet temperature, inlet flow rate and outlet temperature data shall not be less than 10 Hz.

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