Cooperative control method and system for mirror field and heat absorber

Abstract

The invention relates to the technical field of solar thermal power generation, and discloses a mirror field and heat absorber cooperative control method and system, and the system comprises a simulation coupling module which determines a light incident angle based on irradiation data and a sun position model, builds a heliostat model in a simulation environment, simulates a three-dimensional thermal fluid, determines a heat absorber model, and controls the heliostat model and the heat absorber model; the control strategy module determines a control strategy according to the heliostat model and the heat absorber model, the control strategy module integrates a plurality of intelligent control algorithms, predicts a future state and adjusts control input based on the models, and the environment simulation module provides a training scene and a fault working condition scene of extreme weather and sets the extreme weather as a plurality of risk levels. The data analysis module collects and stores operation data, the operation state and performance indexes are displayed through a visual interface, control input is adjusted for the actual working condition and the future state on the basis of a dynamic model, and good control stability is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of solar thermal power generation, and in particular to a method and system for collaborative control of a mirror field and a heat absorber. Background Art

[0002] The rapid development of science and technology has led to higher requirements for solar thermal power generation (CSP), a technology that converts solar energy into electricity. It uses a field of mirrors to collect solar energy and convert it into heat. This heat is then converted into mechanical energy through a thermodynamic cycle, ultimately driving a generator to generate electricity. However, existing coordinated control of the mirror field and the receiver cannot accurately reflect actual operating conditions. Furthermore, commonly used control models are based on static models and cannot dynamically access actual operating data, resulting in certain limitations in control results compared to actual operating conditions. For example, during actual operation, heliostats are affected by factors such as wind speed and temperature changes, resulting in changes in their optical and tracking accuracy. However, existing control systems cannot obtain and correct this data in real time.

[0003] Therefore, how to provide a method and system for coordinated control of the mirror field and the absorber is a technical problem that technicians in this field urgently need to solve. Summary of the Invention

[0004] In view of this, the present invention proposes a method and system for coordinated control of the mirror field and the absorber, aiming to solve the problem that the actual operating conditions cannot be accurately reflected and the actual operating data cannot be dynamically accessed, resulting in certain limitations between the control results and the actual working conditions.

[0005] In one aspect, the present invention proposes a coordinated control system for a mirror field and a heat absorber, comprising:

[0006] a simulation coupling module configured to collect the length and width of a heliostat, collect illumination data of sunlight irradiating the heliostat during a preset time period, determine an incident angle of light based on the illumination data and a solar position model, establish a heliostat model in a simulation environment, simulate a three-dimensional thermal fluid, determine a heat sink model, and determine a control strategy based on the heliostat model and the heat sink model;

[0007] The control strategy module is configured to integrate several intelligent control algorithms, predict future states based on the model, adjust control inputs, and determine the optimization strategy;

[0008] An environmental simulation module is configured to provide training scenarios for extreme weather conditions and fault conditions, and to set the extreme weather conditions to several risk levels;

[0009] The data analysis module is configured to collect and store operating data and display operating status and performance indicators through a visual interface.

[0010] Furthermore, when collecting the length and width of the heliostat and collecting the illumination data of sunlight irradiating the heliostat in a preset time period, and determining the incident angle of light based on the illumination data and the sun position model, the method includes:

[0011] The simulation coupling module sets the preset time period to once per minute;

[0012] determining an area of ​​the heliostat according to the length and the width;

[0013] The illumination data includes sunlight direction data, sunlight intensity data and sunlight spectrum data;

[0014] A sunlight dataset is acquired, and the sun position model is established according to the sunlight dataset.

[0015] Furthermore, when establishing the sun position model according to the sunlight dataset, the method includes:

[0016] The simulation coupling module divides the sunlight dataset into a training set and a test set;

[0017] The neural network model is constructed by combining cross validation with grid search to find the parameters for the neural network model;

[0018] Fitting the neural network model with the training set, substituting the test set into the neural network model and calculating the accuracy of light incident angle prediction;

[0019] When the accuracy reaches a preset accuracy threshold, the light incident angle is determined according to the illumination data.

[0020] Furthermore, a heliostat model is established in a simulation environment, a three-dimensional thermal fluid is simulated, a heat absorber model is determined, and a control strategy is determined based on the heliostat model and the heat absorber model, including:

[0021] The simulation coupling module uses MATLAB simulation software to establish a simulation environment, simulates the process of light starting from the sun, being reflected by the heliostat and reaching the surface of the absorber, and establishes the heliostat model;

[0022] ANSYS Fluent software is used to perform three-dimensional thermal fluid simulation, and a heat absorber model is established based on the flow characteristics, heat conduction, convection heat transfer and radiation heat transfer of the heat absorber;

[0023] The heliostat model and the absorber model are fully coupled to determine the control strategy.

[0024] Furthermore, when integrating several intelligent control algorithms, predicting future states based on models and adjusting control inputs to determine optimization strategies, this includes:

[0025] The intelligent control algorithm includes an adaptive control algorithm and a reinforcement learning algorithm;

[0026] The control strategy module uses Python language to develop a model predictive control model, which is used to predict future states and adjust control inputs;

[0027] The model predictive control model simulates changes in solar radiation, ambient temperature, and heat transfer fluid flow based on the adaptive control algorithm, and iterates the control input based on the reinforcement learning algorithm to determine the optimization strategy.

[0028] Furthermore, when providing extreme weather training scenarios and fault condition scenarios, and setting the extreme weather to several risk levels, it includes:

[0029] The environmental simulation module collects historical data of the extreme weather, the historical data including wind speed, rainfall and temperature, and uses a principal component analysis algorithm to extract data features of the historical data;

[0030] Comparing the data features with preset data features, determining the risk level of the extreme weather according to the comparison results, simulating the risk level in the simulation environment, establishing a fault condition scenario in the simulation environment, and determining fault condition simulation data;

[0031] Presetting a preset first data feature and a preset second data feature, wherein the preset first data feature is greater than the preset second data feature;

[0032] A preset first risk level, a preset second risk level, and a preset third risk level are pre-set, wherein the destructive power of the preset first risk level is greater than that of the preset second risk level, and the destructive power of the preset second risk level is greater than that of the preset third risk level;

[0033] When the data feature is greater than or equal to the preset first data feature, determining the risk level of the extreme weather as the preset first risk level;

[0034] When the data feature is smaller than the preset first data feature and larger than the preset second data feature, the risk level of the extreme weather is determined to be the preset first risk level;

[0035] When the data feature is less than or equal to the preset second data feature, the risk level of the extreme weather is determined to be the preset third risk level.

[0036] Furthermore, when collecting and storing operating data and displaying operating status and performance indicators through a visual interface, it includes:

[0037] The data analysis module uses a distributed data collection algorithm to collect the operating data and store it in a Hadoop distributed framework that supports fast query;

[0038] Developing an interactive visualization interface using JavaScript and D3.js, and displaying the operating status and the performance indicators on the interactive visualization interface;

[0039] Use Python to develop data analysis algorithms and combine them with machine learning libraries to build statistical analysis, trend prediction, and fault diagnosis.

[0040] Furthermore, when collecting and storing operating data and displaying operating status and performance indicators through a visual interface, it also includes:

[0041] The operating data includes heliostat angle, reflected light target point distribution, absorber temperature field, pressure field, and heat transfer medium parameters;

[0042] The statistical analysis is used to analyze the operating data and calculate the average value, maximum value, minimum value and standard deviation of key indicators;

[0043] The trend prediction uses a time series analysis algorithm to perform trend prediction on the operating data;

[0044] The fault diagnosis locates the fault source and provides repair suggestions based on the fault condition simulation data.

[0045] Furthermore, when the interactive visual interface displays the operating status and the performance indicators, it includes:

[0046] Determining the operating status and the performance indicators based on the results of the statistical analysis, the trend prediction, and the fault diagnosis, and presenting the operating status and the performance indicators in the form of graphs and charts;

[0047] The interactive visualization interface provides different data dimensions and time ranges.

[0048] Compared with the existing technology, the present invention has the following advantages: by collecting illumination data to determine the incident angle of light, a model of the heliostat and receiver is established, providing a precise basis for the control strategy, reducing the loss of concentration efficiency, and improving power generation efficiency. Furthermore, by predicting future conditions to adjust the control input, power generation efficiency is further improved. By simulating extreme weather and fault conditions and classifying extreme weather risk levels, the system's ability to cope with different conditions is enhanced, ensuring consistency between actual conditions and control results, improving control accuracy, ensuring the operating status of the mirror field and receiver, and further improving power generation efficiency. A visual interface displays motion status and performance indicators, providing strong data support for system control design and operation management, thereby improving system control performance. Precise simulation, intelligent control, and data-driven decision-making ensure the safe operation of the mirror field and receiver, effectively capturing and making timely adjustments when optical accuracy and tracking accuracy change, ensuring the reliability and stability of system control.

[0049] On the other hand, the present application also provides a method for coordinated control of a mirror field and a heat absorber, which is used to apply the above-mentioned coordinated control system of the mirror field and the heat absorber, including:

[0050] collecting the length and width of a heliostat and collecting illumination data of sunlight irradiating the heliostat during a preset time period; determining an incident angle of light based on the illumination data and a solar position model; establishing a heliostat model in a simulation environment; simulating a three-dimensional thermal fluid to determine an absorber model; and determining a control strategy based on the heliostat model and the absorber model;

[0051] Integrate several intelligent control algorithms to predict future states based on models and adjust control inputs to determine optimization strategies;

[0052] Provide training scenarios for extreme weather and fault conditions, and set the extreme weather into several risk levels;

[0053] Collect and store operating data, and display operating status and performance indicators through a visual interface.

[0054] It is understandable that the above-mentioned method and system for coordinated control of the mirror field and the heat absorber have the same beneficial effects and will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0056] Figure 1This is a functional block diagram of a mirror field and heat absorber coordinated control system provided by an embodiment of the present invention;

[0057] Figure 2 A flow chart of a method for coordinated control of a mirror field and a heat absorber provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, unless there is a conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0059] See Figure 1 As shown, in some embodiments of the present application, this embodiment provides a mirror field and heat absorber coordinated control system, including:

[0060] The simulation coupling module is configured to collect the length and width of the heliostat, collect sunlight exposure data on the heliostat during a preset time period, determine the incident angle of the light based on the exposure data and a solar position model, establish a heliostat model in a simulation environment, simulate a three-dimensional thermal fluid, determine a heat sink model, and determine a control strategy based on the heliostat model and the heat sink model.

[0061] The control strategy module is configured to integrate several intelligent control algorithms, predict future states based on the model, adjust control inputs, and determine the optimization strategy.

[0062] The environmental simulation module is configured to provide training scenarios for extreme weather and fault conditions, and to set extreme weather to several risk levels.

[0063] The data analysis module is configured to collect and store operating data and display operating status and performance indicators through a visual interface.

[0064] Specifically, the simulation coupling module is responsible for collecting the heliostat's dimensional data (length and width) and sunlight exposure data, and determining the light's incident angle in combination with the solar position model. This data collection process, based on the principles of geometric optics, accurately captures the light's incident angle, providing key data for the subsequent establishment of the heliostat model. When establishing the heliostat model, its physical properties are taken into account, and the light reflection path is simulated to accurately simulate the concentration process. Simultaneously, the three-dimensional thermal fluid is simulated to determine the absorber model. Based on thermal principles such as heat conduction, convection, and radiation, the flow and absorption of the heat transfer medium within the absorber are accurately simulated, enabling full-link dynamic simulation of the mirror field and absorber. This allows the system to reflect its operating status under different operating conditions in real time, effectively reducing concentration efficiency losses and thereby improving power generation efficiency. The control strategy module integrates intelligent control algorithms, predicts future states based on the model, and adjusts control inputs to adapt to varying operating conditions, improving system stability and adaptability. In actual operation, when solar irradiance or heat transfer fluid flow changes, the system responds quickly, ensuring stable operation and improving power generation efficiency while mitigating the risk of instability in the mirror field and receiver due to changing operating conditions. The environmental simulation module provides scenarios for extreme weather and fault conditions, categorizing extreme weather risk levels. Using data mining and machine learning techniques to construct simulations, the module enhances the system's control accuracy in responding to emergencies. The data analysis module collects and stores operational data, displaying operating status and performance indicators through a visual interface for efficient management and query. The visual interface presents operating status and performance indicators in the form of graphs and charts, enhancing control reliability and stability.

[0065] It is understandable that the various modules of the system cooperate with each other, starting from simulation, control, training and data analysis, so as to accurately control the mirror field and absorber, and improve the overall performance and reliability of solar thermal power generation.

[0066] In some embodiments of the present application, when collecting the length and width of a heliostat, collecting illumination data of sunlight irradiating the heliostat during a preset time period, and determining the light incidence angle based on the illumination data and a solar position model, the method includes: setting a simulation coupling module to once per minute during the preset time period, determining the area of ​​the heliostat based on the length and width, the illumination data including sunlight direction data, sunlight intensity data, and sunlight spectrum data, obtaining a sunlight dataset, and establishing a solar position model based on the sunlight dataset.

[0067] Specifically, setting the preset data collection timeframe to once per minute allows for high-frequency acquisition of real-time data on sunlight hitting the heliostats. During solar thermal power generation, the sun's position and lighting conditions change over time. A minute-by-minute data collection frequency ensures the system captures these subtle changes, providing accurate, real-time data for subsequent determination of the light's incident angle. For example, during sunrise and sunset, or during rapid cloud movement, lighting conditions can change rapidly. High-frequency data collection promptly reflects these changes, improving the accuracy of light's incident angle calculation. Sunlight direction data directly influences the incident angle, while sunlight intensity data reflects the energy density of solar energy and is crucial for assessing the concentration efficiency of the mirror field and the heat input to the absorber. Sunlight spectrum data helps understand the distribution of light at different wavelengths, as different wavelengths exhibit different characteristics when reflected by the heliostats and absorbed by the absorber. Considering spectral data allows for accurate simulation of the light-to-heat conversion process. The heliostat's area is determined based on its length and width, which is the product of its length and width. In ray tracing algorithms, the area of ​​the heliostat affects the distribution and intensity of reflected light. Accurate area data ensures that ray tracing results are more realistic, thereby improving the accuracy of the light's incident angle. Building a sun position model by acquiring a sunlight dataset lays the foundation for determining the light's incident angle.

[0068] In some embodiments of the present application, when establishing a solar position model based on a sunlight data set, it includes: a simulation coupling module divides the sunlight data set into a training set and a test set, uses cross-validation combined with grid search to find the establishment parameters of the neural network model, constructs a neural network model, uses the training set to fit the neural network model, and substitutes the test set into the neural network model and calculates the accuracy of the light prediction incident angle. When the accuracy reaches a preset accuracy threshold, the light incident angle is determined according to the illumination data.

[0069] Specifically, the sunlight dataset includes key data such as atmospheric transparency, cloud cover, and geographic location. This data records the incident angles of light on the heliostats and receivers under different conditions. The sunlight dataset is divided into training and test sets. Typically, 70%-80% of the data is used as the training set, and the remainder as the test set. Ensuring that both the training and test sets contain data from a variety of different scenarios improves the model's generalization. Cross-validation involves splitting the data into several parts and training the model multiple times to verify its stability and performance. Grid search improves model accuracy and stability by exhaustively searching for parameter combinations in the parameter space. Using the training set data to fit the neural network model improves model accuracy and stability. The test set data is then fed into the trained neural network model to measure the accuracy of the light incident angle predictions. Accuracy reflects the model's performance on unknown data and is a key metric for evaluating model performance. Once the model reaches a preset accuracy threshold, the current illumination data is substituted into the neural network model to enable real-time predictions of the illumination data, improving the accuracy and reliability of the light incident angle.

[0070] In some embodiments of the present application, a heliostat model is established in a simulation environment, a three-dimensional thermal fluid is simulated, an absorber model is determined, and a control strategy is determined based on the heliostat and absorber models. The process includes: a simulation coupling module uses MATLAB simulation software to establish a simulation environment, simulates the process of light starting from the sun, being reflected by the heliostat, and reaching the absorber surface, establishes the heliostat model, uses ANSYS Fluent software to perform a three-dimensional thermal fluid simulation, establishes an absorber model based on the flow characteristics, heat conduction, convection heat transfer, and radiation heat transfer of the absorber, fully couples the heliostat and absorber models, and determines the control strategy.

[0071] Specifically, MATLAB simulation software is used to simulate the process of light traveling from the sun, through heliostats, and onto the absorber surface. This allows for the calculation and simulation of the light propagation path, fully accounting for the reflection and focusing effects of light, thereby reducing light loss and improving concentration efficiency. For example, accurate ray tracing simulations can optimize the heliostat installation angle, allowing more sunlight to be accurately focused onto the absorber, thereby enhancing the overall energy capture capability of solar thermal power generation. ANSYS Fluent software is used for 3D thermal-fluid simulation, comprehensively considering various characteristics of the absorber, including flow, heat conduction, convection, and radiation. This allows for a precise absorber model to be established, providing a deep understanding of the heat transfer process and fluid flow patterns within the absorber, thereby improving the efficiency of coordinated control. Fully coupling the heliostat and absorber models allows for a realistic representation of the interaction and energy transfer between the mirror field and the absorber. In actual operation, the reflection effect of the heliostats directly affects the heating of the absorber, and the absorber's thermal state feeds back into the heliostat control strategy. Fully coupled simulations comprehensively consider these factors, enabling coordinated optimization of the mirror field and the absorber. For example, under different solar illumination conditions and environmental working conditions, the angle of the heliostat and the operating parameters of the heat absorber can be adjusted in real time according to the coupling model. Through simulation analysis of the model, a reasonable control strategy can be formulated to timely adjust the tracking angle of the heliostat and the flow control of the heat absorber, reducing power generation fluctuations caused by changes in working conditions, thereby improving power generation efficiency and stability.

[0072] In some embodiments of the present application, when integrating several intelligent control algorithms, predicting future states based on models and adjusting control inputs, and determining optimization strategies, it includes: the intelligent control algorithm includes an adaptive control algorithm and a reinforcement learning algorithm, the control strategy module uses Python language to develop a model predictive control model, the model predictive control model is used to predict future states and adjust control inputs, the model predictive control model simulates changes in solar radiation, ambient temperature, and heat transfer fluid flow based on an adaptive control algorithm, and iterates the control input based on a reinforcement learning algorithm to determine the optimization strategy.

[0073] Specifically, adaptive control algorithms can dynamically adjust control inputs based on real-time data to adapt to the ever-changing operating environment. During solar thermal power generation, factors such as solar irradiance, ambient temperature, and heat transfer fluid flow rate are constantly changing. For example, solar irradiance fluctuates with weather conditions and the passage of time, and ambient temperature can also vary throughout the day. Adaptive control algorithms can monitor these changes in real time, improving the system's adaptability to environmental changes and reducing power generation efficiency losses caused by environmental fluctuations. Reinforcement learning algorithms iteratively optimize control inputs through continuous trial and error, gradually learning the optimal control strategy. For example, under varying solar irradiance and ambient temperature conditions, a reinforcement learning algorithm can, through repeated trials, find the optimal combination of heliostat angle and heat transfer fluid flow rate to maximize power generation efficiency. As the system operates over time, the reinforcement learning algorithm accumulates experience and continuously refines the control strategy, enabling the system to continuously improve performance over time and adapt to various complex operating conditions. Python is a powerful and easy-to-learn and use programming language with a rich library of scientific computing and machine learning libraries, such as NumPy, SciPy, and TensorFlow. These libraries can implement complex algorithms and models, thereby improving development efficiency. The Python-based development environment makes the system scalable, allowing optimization strategies to be integrated into the system, further improving the system's performance and adaptability.

[0074] In some embodiments of the present application, when providing training scenarios for extreme weather and fault operating conditions, and setting extreme weather to several risk levels, the environment simulation module collects historical data of extreme weather, the historical data includes wind speed, rainfall and temperature, uses a principal component analysis algorithm to extract data features of the historical data, compares the data features with preset data features, determines the risk level of extreme weather according to the comparison results, simulates the risk level in a simulation environment, establishes a fault operating condition scenario in the simulation environment, determines fault operating condition simulation data, pre-sets a preset first data feature and a preset second data feature, and the preset first data feature is greater than the preset A second data feature is set, and a preset first risk level, a preset second risk level and a preset third risk level are pre-set. The destructive power of the preset first risk level is greater than the preset second risk level, and the destructive power of the preset second risk level is greater than the preset third risk level. When the data feature is greater than or equal to the preset first data feature, the risk level of extreme weather is determined to be the preset first risk level. When the data feature is less than the preset first data feature and greater than the preset second data feature, the risk level of extreme weather is determined to be the preset first risk level. When the data feature is less than or equal to the preset second data feature, the risk level of extreme weather is determined to be the preset third risk level.

[0075] Specifically, the environmental simulation module collects historical data on extreme weather conditions, such as wind speed, rainfall, and temperature, and uses principal component analysis (PCA) to extract data features. PCA transforms multiple correlated data sets into a small number of uncorrelated principal components, effectively reducing data dimensionality while preserving the key information. By extracting these key data features, the characteristics of extreme weather events can be accurately described, providing a foundation for subsequent risk assessment. The extracted data features, including wind speed, rainfall, and temperature, are compared with pre-set data features. The resulting extreme weather risk level is then determined, accurately assessing the potential damage to solar thermal power generation. These risk levels are then categorized into different risk levels. These determined risk levels are then simulated in a simulation environment, enabling operators to proactively understand the impact of extreme weather on the solar field and heaters at different risk levels. Furthermore, fault scenarios are established within the simulation environment and simulated data is generated for these fault conditions. This helps comprehensively consider various possible fault scenarios, such as heliostat failures and heat transfer fluid leaks. By simulating and analyzing these fault conditions, the coordinated control system's repair capabilities and control accuracy can be improved, enhancing system safety and reliability.

[0076] In some embodiments of the present application, when collecting and storing operating data and displaying operating status and performance indicators through a visual interface, it includes: the data analysis module adopts a distributed data collection algorithm to collect operating data and store it in the Hadoop distributed framework, the Hadoop distributed framework supports fast query, JavaScript and D3.js are used to develop an interactive visualization interface, and the operating status and performance indicators are displayed on the interactive visualization interface, the data analysis algorithm is developed using Python language, and statistical analysis, trend prediction and fault diagnosis are constructed in combination with the machine learning library.

[0077] In some embodiments of the present application, when collecting and storing operating data and displaying the operating status and performance indicators through a visual interface, it also includes: the operating data includes the heliostat angle, the distribution of the reflected light target points, the absorber temperature field, the pressure field, and the heat transfer fluid parameters; statistical analysis is used to analyze the operating data and calculate the average value, maximum value, minimum value and standard deviation of key indicators; trend prediction uses a time series analysis algorithm to predict the trend of the operating data; fault diagnosis locates the fault source and provides repair suggestions based on the fault condition simulation data.

[0078] Specifically, the distributed data collection algorithm enables rapid and accurate determination of operational data such as heliostat angles, the distribution of reflected light target points, and the receiver temperature field. This distributed data collection approach fully utilizes the computing power of multiple nodes, avoiding the bottlenecks inherent in single-point data collection and improving data collection efficiency. The collected operational data is stored in the Hadoop distributed framework. Hadoop boasts high reliability, scalability, and fault tolerance. By distributing data across multiple nodes, even if a node fails, data integrity and availability are not affected. Furthermore, the Hadoop distributed framework supports fast queries, meeting the system's need for rapid retrieval of real-time data. During system analysis and fault diagnosis, operators can quickly locate required information from massive amounts of data, providing strong support for appropriate control strategies. The interactive visualization interface, developed using JavaScript and D3.js, presents operational status and performance indicators in an intuitive and vivid manner. D3.js is a data visualization library that transforms complex data into various charts and graphs, such as line charts, bar charts, and heat maps. Interacting with the interactive visualization interface provides in-depth insights into system operation, visually displaying operational data such as heliostat angles and receiver temperature fields, improving system efficiency and safety. Data analysis algorithms developed in Python, combined with machine learning libraries, enable comprehensive statistical analysis of operational data. By calculating statistics such as the mean, maximum, minimum, and standard deviation of key performance indicators (KPIs), they provide a reliable basis for adjustments to the mirror field and receiver. A time series analysis algorithm is used to predict operational data trends, enabling proactive adjustments to the heat transfer fluid flow rate to ensure stable system operation and enhance system predictability and controllability. Fault diagnosis based on simulated fault condition data quickly locates the source of the fault and provides remediation recommendations. If an anomaly occurs during system operation, the data analysis module compares normal operating data with simulated fault condition data to accurately determine the fault type and location and provide appropriate remediation solutions. For example, if the receiver temperature suddenly rises, the fault diagnosis module can analyze the relevant data to determine whether insufficient heat transfer fluid flow or abnormal heliostat reflection is the cause, and provide specific remediation measures. This improves system control accuracy and reliability.

[0079] In some embodiments of the present application, when the operating status and performance indicators are displayed on an interactive visualization interface, it includes: determining the operating status and performance indicators based on the results of statistical analysis, trend prediction and fault diagnosis, and displaying the operating status and performance indicators in the form of graphics and charts. The interactive visualization interface provides different data dimensions and time ranges.

[0080] Specifically, by selecting different data dimensions (such as the heliostat angles in the mirror field, the absorber temperature field, and heat transfer fluid parameters) for viewing in the interactive visualization interface, comprehensive and accurate data support is provided for the control strategy. By displaying data in different time ranges (such as daily, weekly, monthly, and annual), one can gain an in-depth understanding of the operating patterns between the data, thereby analyzing the impact of long-term data and seasonal changes on the mirror field and heliostats, and adjusting the heliostat tracking strategy and heat transfer fluid flow in advance, thereby improving power generation efficiency. The interactive visualization interface displays results in the form of graphics and charts, facilitating communication between personnel with different professional backgrounds, thereby improving the accuracy and coordination adaptability of system control.

[0081] In summary, the beneficial effects of the present invention are as follows: by collecting illumination data to determine the incident angle of light, a heliostat and receiver model is established, providing a precise basis for control strategies, reducing concentration efficiency losses, and improving power generation efficiency. Furthermore, by predicting future conditions to adjust control inputs, power generation efficiency is further improved. By simulating extreme weather and fault conditions and classifying extreme weather risk levels, the system's ability to cope with different conditions is enhanced, ensuring consistency between actual operating conditions and control results, improving control accuracy, ensuring the operating status of the mirror field and receiver, and further improving power generation efficiency. Displaying motion status and performance indicators through a visual interface provides powerful data support for system control design and operation management, thereby improving system control performance. Precise simulation, intelligent control, and data-driven decision-making ensure the safe operation of the mirror field and receiver, effectively capturing and timely adjusting changes in optical and tracking accuracy, and ensuring the reliability and stability of system control.

[0082] In another preferred embodiment based on the above embodiment, refer to Figure 2 As shown, this embodiment provides a method for coordinated control of a mirror field and a heat absorber, which is used to apply the above-mentioned coordinated control system of the mirror field and the heat absorber, including:

[0083] S100: Collecting the length and width of the heliostat and collecting sunlight exposure data on the heliostat during a preset time period; determining the incident angle of the light based on the exposure data and a solar position model; establishing a heliostat model in a simulation environment; simulating a three-dimensional thermal fluid; determining a heat absorber model; and determining a control strategy based on the heliostat and heat absorber models.

[0084] S200: Integrates several intelligent control algorithms, predicts future states based on models, adjusts control inputs, and determines optimization strategies.

[0085] S300: Provides training scenarios for extreme weather and fault conditions, and sets extreme weather into several risk levels.

[0086] S400: Collects and stores operating data, and displays operating status and performance indicators through a visual interface.

[0087] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0089] These computer program instructions may also be stored in a computer-readable storage device that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable storage device produce an article of manufacture comprising an instruction device that implements the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A coordinated control system of a mirror field and a heat absorber, characterized in that: include: a simulation coupling module configured to collect the length and width of a heliostat, collect illumination data of sunlight irradiating the heliostat during a preset time period, determine an incident angle of light based on the illumination data and a solar position model, establish a heliostat model in a simulation environment, simulate a three-dimensional thermal fluid, determine a heat sink model, and determine a control strategy based on the heliostat model and the heat sink model; The control strategy module is configured to integrate several intelligent control algorithms, predict future states based on the model and adjust control inputs to determine the optimization strategy; An environmental simulation module is configured to provide training scenarios for extreme weather conditions and fault conditions, and to set the extreme weather conditions to several risk levels; The data analysis module is configured to collect and store operating data and display operating status and performance indicators through a visual interface.

2. The mirror field and heat absorber coordinated control system according to claim 1, characterized in that: When collecting the length and width of the heliostat, collecting the illumination data of sunlight irradiating the heliostat in a preset time period, and determining the incident angle of light based on the illumination data and a solar position model, the method includes: The simulation coupling module sets the preset time period to once per minute; determining an area of ​​the heliostat according to the length and the width; The illumination data includes sunlight direction data, sunlight intensity data and sunlight spectrum data; A sunlight dataset is acquired, and the sun position model is established according to the sunlight dataset.

3. The mirror field and heat absorber coordinated control system according to claim 2, characterized in that: When establishing the sun position model according to the sunlight dataset, the method includes: The simulation coupling module divides the sunlight dataset into a training set and a test set; Cross-validation combined with grid search is used to find the parameters of the neural network model and construct the neural network model; Fitting the neural network model with the training set, substituting the test set into the neural network model and calculating the accuracy of light incident angle prediction; When the accuracy reaches a preset accuracy threshold, the light incident angle is determined according to the illumination data.

4. The mirror field and heat absorber coordinated control system according to claim 3, characterized in that: The method includes establishing a heliostat model in a simulation environment, simulating a three-dimensional thermal fluid, determining a heat absorber model, and determining a control strategy based on the heliostat model and the heat absorber model, including: The simulation coupling module uses MATLAB simulation software to establish a simulation environment, simulates the process of light starting from the sun, being reflected by the heliostat and reaching the surface of the absorber, and establishes the heliostat model; ANSYS Fluent software is used to perform three-dimensional thermal fluid simulation, and a heat absorber model is established based on the flow characteristics, heat conduction, convection heat transfer and radiation heat transfer of the heat absorber; The heliostat model and the absorber model are fully coupled to determine the control strategy.

5. The mirror field and heat absorber coordinated control system according to claim 4, characterized in that: When integrating several intelligent control algorithms, predicting future states based on models and adjusting control inputs to determine optimization strategies, this includes: The intelligent control algorithm includes an adaptive control algorithm and a reinforcement learning algorithm; The control strategy module uses Python language to develop a model predictive control model, which is used to predict future states and adjust control inputs; The model predictive control model simulates solar radiation changes, ambient temperature changes, and heat transfer fluid flow changes based on the adaptive control algorithm, and iterates the control input based on the reinforcement learning algorithm to determine the optimization strategy.

6. The mirror field and heat absorber coordinated control system according to claim 5, characterized in that: When providing extreme weather training scenarios and fault condition scenarios, and setting the extreme weather into several risk levels, including: The environmental simulation module collects historical data of the extreme weather, the historical data including wind speed, rainfall and temperature, and uses a principal component analysis algorithm to extract data features of the historical data; Comparing the data features with preset data features, determining the risk level of the extreme weather according to the comparison results, simulating the risk level in the simulation environment, establishing a fault condition scenario in the simulation environment, and determining fault condition simulation data; Presetting a preset first data feature and a preset second data feature, wherein the preset first data feature is greater than the preset second data feature; A preset first risk level, a preset second risk level, and a preset third risk level are pre-set, wherein the destructive power of the preset first risk level is greater than that of the preset second risk level, and the destructive power of the preset second risk level is greater than that of the preset third risk level; When the data feature is greater than or equal to the preset first data feature, determining the risk level of the extreme weather as the preset first risk level; When the data feature is smaller than the preset first data feature and larger than the preset second data feature, the risk level of the extreme weather is determined to be the preset first risk level; When the data feature is less than or equal to the preset second data feature, the risk level of the extreme weather is determined to be the preset third risk level.

7. The mirror field and heat absorber coordinated control system according to claim 6, characterized in that: When collecting and storing operating data and displaying operating status and performance indicators through a visual interface, it includes: The data analysis module uses a distributed data collection algorithm to collect the operating data and store it in a Hadoop distributed framework that supports fast query; Developing an interactive visualization interface using JavaScript and D3.js, and displaying the operating status and the performance indicators on the interactive visualization interface; Use Python to develop data analysis algorithms and combine them with machine learning libraries to build statistical analysis, trend prediction, and fault diagnosis.

8. The mirror field and heat absorber coordinated control system according to claim 7, characterized in that: When collecting and storing operating data and displaying operating status and performance indicators through a visual interface, it also includes: The operating data includes heliostat angle, reflected light target point distribution, absorber temperature field, pressure field, and heat transfer medium parameters; The statistical analysis is used to analyze the operating data and calculate the average value, maximum value, minimum value and standard deviation of key indicators; The trend prediction uses a time series analysis algorithm to perform trend prediction on the operating data; The fault diagnosis locates the fault source and provides repair suggestions based on the fault condition simulation data.

9. The mirror field and heat absorber coordinated control system according to claim 8, characterized in that: When the interactive visual interface displays the operating status and the performance indicators, it includes: Determining the operating status and the performance indicators based on the results of the statistical analysis, the trend prediction, and the fault diagnosis, and presenting the operating status and the performance indicators in the form of graphs and charts; The interactive visualization interface provides different data dimensions and time ranges.

10. A method for coordinated control of a mirror field and a heat absorber, for applying the coordinated control system of a mirror field and a heat absorber according to any one of claims 1 to 9, characterized in that: include: collecting the length and width of a heliostat and collecting illumination data of sunlight irradiating the heliostat during a preset time period; determining an incident angle of light based on the illumination data and a solar position model; establishing a heliostat model in a simulation environment; simulating a three-dimensional thermal fluid to determine an absorber model; and determining a control strategy based on the heliostat model and the absorber model; Integrate several intelligent control algorithms to predict future states based on models and adjust control inputs to determine optimization strategies; Provide training scenarios for extreme weather and fault conditions, and set the extreme weather into several risk levels; Collect and store operating data, and display operating status and performance indicators through a visual interface.

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