Optimal scheduling control method for heliostat field in solar tower thermal power station

Abstract

The present invention discloses a method for optimizing the scheduling and control of heliostat fields in a solar tower-type solar thermal power station, which relates to the field of solar thermal power generation technology and includes the following steps: continuously and synchronously acquiring the temperature distribution matrix and heat flux density matrix of the tower top receiver surface with a millisecond sampling period, respectively recording their continuous change processes in the time dimension, and forming a thermal characteristic input data set for transient response analysis. The present invention generates a transient thermal shock intensity index with predictive capabilities by constructing a joint characteristic sequence of temperature and heat flux, thereby realizing feedforward identification and adaptive regulation of thermal disturbances. The system can actively adjust the working fluid flow rate and heliostat focusing behavior before a thermal shock occurs, dynamically balance heat input and conduction capacity, effectively prevent faults such as overheating and overpressure, and improve the receiver life and the operating efficiency of the solar thermal power generation system.

Description

Technical Field

[0001] The present invention relates to the technical field of solar thermal power generation, and in particular to a method for optimizing the scheduling and control of a heliostat field in a solar tower thermal power station. Background Art

[0002] Optimal scheduling and control of the heliostat field in a solar tower solar thermal power plant involves dynamically optimizing and intelligently scheduling the real-time position, tracking trajectory, and focusing strategy of a large-scale heliostat array in a solar tower solar thermal power generation system. This allows each heliostat to precisely adjust its reflection angle based on the spatial relationship between the sun's position and the target receiver, maximizing the focus of solar radiation on the tower's top receiver. This control process comprehensively considers variations in the sun's astronomical position, cloud shadows, mirror shading effects, fluctuations in heat load demand, and power plant operational constraints. It utilizes algorithmic models (such as heuristic algorithms, model predictive control, or machine learning) to coordinate and schedule the mirror field in a unified manner, thereby improving CSP collection efficiency, reducing energy loss, mitigating overheating risks, and enhancing overall plant operational stability and responsiveness. This method is one of the key control technologies for achieving efficient solar thermal conversion and intelligent energy management.

[0003] Existing technologies have the following shortcomings: To improve the efficiency of solar thermal energy utilization under shifting conditions, existing solar tower-type concentrated thermal power plants typically implement an "edge-chasing scheduling strategy" near cloud cover boundaries. This strategy uses a predicted cloud movement trend to preemptively direct heliostats in unobstructed areas to quickly align with the receivers, minimizing energy losses caused by the obstruction. However, in extreme weather conditions, where cloud edges sweep across the field at high speed due to high-altitude wind shear or atmospheric disturbances, the scheduling system may rapidly mobilize a large number of heliostats to reflect solar radiation to the same receiving area.

[0004] If the clouds suddenly dissipate completely at this moment, a massive amount of solar energy will converge unimpeded on the tower's receiver within seconds, causing a dramatic jump in heat load and a significant transient thermal shock effect. Because the receiver typically uses molten salt or thermal oil as the working fluid for heat energy transfer, when the heat flux density far exceeds the system's steady-state heat absorption capacity, it can easily trigger "supersaturation" instabilities, such as localized overheating of the medium, vaporization blockage, flow interruption, or uneven thermal expansion. In severe cases, this can lead to overheating and bursting of the receiver pipes, abnormal pressure increases, and frequent triggering of the safety valve, thus affecting the normal operation of the entire thermal storage and power generation chain.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The present invention aims to provide a method for optimizing the scheduling and control of heliostat fields in solar tower-type concentrated thermal power plants. By constructing a joint characteristic sequence of temperature and heat flux, this system generates a predictive transient thermal shock intensity index, enabling feedforward identification and adaptive control of thermal disturbances. The system proactively adjusts the working fluid flow rate and heliostat focusing behavior before a thermal shock occurs, dynamically balancing heat input and conduction capacity. This effectively prevents failures such as overheating and overpressure, improves receiver lifespan, and enhances the operating efficiency of the concentrated thermal power generation system, thereby addressing the aforementioned background art issues.

[0007] In order to achieve the above-mentioned object, the present invention provides the following technical solution: a method for optimizing the scheduling and control of a heliostat field in a solar tower-type concentrated thermal power station, comprising the following steps:

[0008] The temperature distribution matrix and heat flux density matrix of the tower top receiver surface are continuously acquired synchronously with a millisecond sampling period, and their continuous changes in the time dimension are recorded respectively to form a thermal characteristic input data set for transient response analysis.

[0009] A fixed-length sliding time window is used to traverse the heat flux matrix sequence. Time difference calculation is performed within each window to obtain the heat flux density change slope. The effective heating area of ​​the receiver corresponding to each heat flux pixel point is then weighted to generate a heat flux growth rate sequence.

[0010] The maximum temperature gradient value at each time point is extracted from the temperature distribution matrix. The maximum temperature gradient at each time point is paired with the corresponding heat flux growth rate. A thermal shock coupling factor sequence is generated through a standardized mapping function to characterize the joint changes in thermal gradient intensity and heat flux input rate.

[0011] Analyze the amplitude variation characteristics of the thermal shock coupling factor sequence within the current sliding cycle, combine it with the time evolution trend of the thermal disturbance, and calculate the transient thermal shock intensity index to reflect the degree of thermal load jump of the receiver;

[0012] When the transient thermal shock intensity index exceeds the preset threshold, the joint control strategy is triggered. By reducing the working fluid flow rate at the receiver inlet to extend the heat exchange residence time, and adjusting the incident angle of some heliostats to reduce the focusing efficiency of the reflected light, a dynamic balance between heat input and heat transfer output is achieved.

[0013] Preferably, the specific steps of obtaining the temperature distribution matrix and heat flux density matrix of the tower top receiver surface to form a thermal characteristic input data set for transient response analysis are as follows:

[0014] A three-dimensional coordinate reference system is established to grid the receiver surface. High-precision infrared imaging devices and radiation heat flow sensing modules are used to synchronously collect instantaneous temperature values ​​and unit area heat flux density values ​​at each grid cell to generate a high-resolution thermal field information matrix.

[0015] The temperature values ​​and heat flux values ​​obtained by continuous sampling are paired in time sequence according to a unified timestamp to construct a two-dimensional thermal field sequence structure, and a linear interpolation algorithm is used to fill in the missing data caused by sampling jitter;

[0016] The constructed thermal field sequence structure is subjected to discrete Fourier transform and wavelet packet decomposition to extract the local change characteristics in the time dimension and form a thermal response analysis vector set with time-frequency distribution information.

[0017] Preferably, the specific steps of using a fixed-length sliding time window to traverse the heat flux density matrix sequence and generate the heat flux growth rate sequence are as follows:

[0018] Set the length of the sliding time window and the sliding step parameters, and extract the two-dimensional heat flux snapshot data window by window in time order from the continuous heat flux density matrix sequence;

[0019] In each sliding window, the first-order difference calculation is performed on the heat flux density values ​​of the same heat flux pixel at adjacent time points to obtain the heat flux density change slope of the pixel in the current window, and the change slopes of all pixels are combined to form a complete heat flux density change slope matrix;

[0020] According to the effective heated area corresponding to each pixel point on the receiver surface, the heat flux density change slope matrix is ​​spatially integrated using a weighted average method to generate a unique heat flux growth rate value corresponding to the current sliding window, and a heat flux growth rate sequence is formed according to the window order.

[0021] Preferably, a first-order difference calculation is performed on the heat flux density values ​​of the same heat flux pixel at adjacent time points in each sliding window to obtain the heat flux density change slope of the pixel in the current window, and the change slopes of all pixels are combined to form a complete heat flux density change slope matrix. The specific steps are as follows:

[0022] For the heat flux matrix sequence arranged by time in the current sliding window, the heat flux density value of each pixel point at all time sections is extracted, and the heat flux density time vector of the pixel point is constructed in time sequence;

[0023] Perform a first-order difference operation on each heat flux time vector, that is, calculate the heat flux increment between two adjacent time points, and divide the increment by the time interval to obtain the heat flux change slope sequence of the pixel point in each time period within the current sliding window;

[0024] The heat flux density change slope value of each pixel point in the entire sliding window is reconstructed into a two-dimensional matrix structure according to its position index on the receiver surface to form a complete heat flux density change slope matrix.

[0025] Preferably, the maximum temperature gradient value at each time point is extracted from the temperature distribution matrix, the maximum temperature gradient at each time point is paired with the corresponding heat flux growth rate, and the specific steps of generating the thermal shock coupling factor sequence through the standardized mapping function are as follows:

[0026] For the temperature distribution matrix corresponding to each time point, the temperature variation between adjacent pixels is calculated based on the spatial first-order difference method, and the maximum value is extracted as the maximum temperature gradient at the current time point;

[0027] The maximum temperature gradient value extracted at each time point is paired one-to-one with the heat flow growth rate calculated at the same time point to form a two-dimensional feature point sequence indexed by the time point;

[0028] A standardized mapping function is applied to the obtained two-dimensional feature point sequence to unify the numerical scale and enhance the mathematical correlation, thereby generating a thermal shock coupling factor sequence with a unified dimension.

[0029] Preferably, the specific steps of analyzing the amplitude variation characteristics of the thermal shock coupling factor sequence in the current sliding cycle and combining the time evolution trend of the thermal disturbance to calculate the transient thermal shock intensity index are as follows:

[0030] Select the thermal shock coupling factor sequence corresponding to the current sliding period and use the multi-index analysis method to extract the amplitude variation characteristics of the thermal shock coupling factor in the current time period;

[0031] The amplitude variation characteristics of the current sliding cycle and the consecutive sliding cycles before and after it are constructed as a time series. Polynomial fitting and slope extraction operations are performed on the current time series to establish a time evolution trend model of thermal disturbance.

[0032] Based on the amplitude change characteristics of the current sliding cycle and the trend indicators in the thermal disturbance evolution trend model, dynamic weighted calculation is performed to generate a transient thermal shock intensity index that reflects the sudden increase in short-term heat input and the evolution direction of future thermal shock risks.

[0033] Preferably, the amplitude variation characteristics of the current sliding cycle and the multiple consecutive sliding cycles before and after it are constructed into a time series, and polynomial fitting and slope extraction operations are performed on the current time series to establish a time evolution trend model of thermal disturbance. The specific steps are as follows:

[0034] Taking the current sliding cycle as the center, a continuous data segment containing a fixed number of sliding cycles before and after is selected. The corresponding amplitude change characteristic value of the thermal shock coupling factor is extracted from each cycle and arranged in chronological order to construct a time series of thermal shock amplitude change.

[0035] Based on the constructed time series of thermal shock amplitude changes, a polynomial function of a preset order is selected to perform a least squares fitting operation to generate a smooth fitting curve with continuous derivative properties;

[0036] A first-order derivative operation is performed on the fitting curve at the time point corresponding to the current sliding period to extract the trend slope index at the current time point, which is used to measure the growth rate and direction of the thermal disturbance.

[0037] Preferably, when the transient thermal shock intensity index exceeds a preset threshold, a joint control strategy is triggered. Based on the transient thermal shock intensity index generated by the thermal shock coupling factor sequence analysis results and the reference threshold set based on engineering experience, a control incentive reflecting the current severity of the thermal shock is established, and a thermal response control factor is constructed to characterize the driving strength of the thermal control by the intensity of the current thermal disturbance. The calculation expression is as follows:

[0038] ,

[0039] Where, is the transient thermal shock strength index, is the transient thermal shock intensity index reference threshold, It is the thermodynamic response regulating factor;

[0040] Based on the obtained thermal response control factor , the working fluid flow rate at the receiver inlet and the focusing efficiency of the heliostat are jointly adjusted to suppress the energy impact in a short period of time. The current working fluid flow rate is corrected by an exponential adjustment function to generate the target flow rate. The generation formula is as follows:

[0041] ,

[0042] Where, is the adjusted working fluid flow rate, is the current real-time working fluid inlet flow rate, is the natural base, is the working fluid flow rate response sensitivity coefficient;

[0043] At the same time, a nonlinear reduction model based on the hyperbolic tangent function is applied to the light focusing efficiency of the heliostat to generate a new focusing efficiency target value. The generation formula is as follows:

[0044] ,

[0045] Where, is the corrected target focusing efficiency target value, is the actual focusing efficiency of the current heliostat array, is the focusing efficiency response sensitivity coefficient, is the hyperbolic tangent function.

[0046] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0047] This invention constructs a joint characteristic sequence of temperature and heat flux to accurately capture the millisecond-scale variations in the receiver surface heat load. Combined with the multidimensional evolution of the thermal shock coupling factor, it constructs a predictive transient thermal shock intensity index, enabling feedforward triggering and adaptive intensity adjustment of control behavior. Compared to traditional passive response mechanisms, this invention proactively adjusts the working fluid flow rate and heliostat focusing behavior before the thermal shock causes irreversible damage, achieving a dynamic balance between heat input rate and thermal conductivity. This effectively prevents overheating, overpressure, and fluid instability, extending the receiver's service life and improving the continuity, safety, and energy conversion efficiency of the solar thermal power generation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0049] Figure 1 This is a flow chart of the method for optimizing the scheduling control method of the heliostat field of a solar tower-type thermal power station according to the present invention. DETAILED DESCRIPTION

[0050] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0051] The present invention provides Figure 1 The method for optimizing the scheduling and control of the heliostat field of a solar tower-type concentrated thermal power station shown in the figure includes the following steps:

[0052] The temperature distribution matrix and heat flux density matrix of the tower top receiver surface are continuously acquired synchronously with a millisecond sampling period, and their continuous changes in the time dimension are recorded respectively to form a thermal characteristic input data set for transient response analysis.

[0053] The specific steps to obtain the temperature distribution matrix and heat flux density matrix of the tower top receiver surface and form the thermal characteristic input data set for transient response analysis are as follows:

[0054] A three-dimensional coordinate reference system is established to grid the receiver surface. High-precision infrared imaging devices and radiation heat flow sensing modules are used to synchronously collect instantaneous temperature values ​​and unit area heat flux density values ​​at each grid cell to generate a high-resolution thermal field information matrix.

[0055] The temperature values ​​and heat flux values ​​obtained by continuous sampling are paired in time sequence according to a unified timestamp to construct a two-dimensional thermal field sequence structure. A linear interpolation algorithm is used to fill in missing data caused by sampling jitter to ensure the spatiotemporal continuity of thermal feature data.

[0056] The constructed thermal field sequence structure is subjected to discrete Fourier transform and wavelet packet decomposition to extract the local change characteristics in the time dimension, and form a thermal response analysis vector set with time-frequency distribution information, which is used as the input basis for the subsequent thermal shock assessment model.

[0057] Meshing the receiver surface means dividing the entire receiver irradiation surface into a number of regular small cells according to a preset spatial resolution on the basis of establishing a unified three-dimensional spatial reference system. Each grid cell represents a fixed observation area on the receiver surface. This division usually uses a rectangular coordinate system or a spherical coordinate system to uniformly divide the plane or curved surface grid, so that the temperature and heat flow measurement data have clear spatial positioning properties. The main function of grid division is to achieve a refined expression of the spatial distribution of thermal characteristics, so that high-precision infrared imaging devices and heat flow sensors can accurately collect temperature and heat flux data at each specific location, and then generate a high-resolution, time-space matching consistent thermal field information matrix, which provides a reliable data foundation and spatial positioning basis for subsequent heat flow gradient analysis, thermal shock factor extraction and dynamic control strategies.

[0058] The linear interpolation algorithm is a commonly used numerical interpolation method and one of the existing basic interpolation algorithms. Its core idea is to assume that the data changes linearly and uniformly between two known data points, thereby inferring the possible intermediate values ​​at any position between the two points. During the continuous sampling process of temperature and heat flux values, data at some time points may be missing or incomplete due to acquisition hardware jitter, signal interference, or instantaneous delays. At this time, the linear interpolation algorithm can smoothly fill the gap in time sequence based on the data of the two valid sampling points before and after the missing point, thereby restoring the temporal continuity and stability of the data. Its role is to ensure the integrity and analyzability of thermal field data in the time dimension, so that subsequent thermal shock modeling and dynamic response analysis can be carried out based on a data set with no missing values ​​and smooth transitions, thereby improving the overall real-time control accuracy and robustness of the system.

[0059] The purpose of applying discrete Fourier transform and wavelet packet decomposition to the constructed thermal field sequence structure is to extract implicit local variation characteristics and multi-scale dynamic information from the time series. The thermal field sequence reflects the temperature and heat flux density variations of the receiver at different time points. These changes often include both slow steady-state trends and high-speed transient disturbances. The discrete Fourier transform converts the thermal field sequence into a frequency domain representation over the entire time range, revealing the frequency components of periodic or sudden changes. Wavelet packet decomposition further analyzes the signal locally at multiple time scales, preserving temporal localization information and capturing the time-varying characteristics of heat flux at different frequencies. By combining these two methods, it is possible to not only identify whether there are drastic changes, high-frequency disturbances, or abnormal signals in the system, but also precisely locate the time periods when these changes occur. This results in a thermal response analysis vector set with joint time-frequency characteristics, providing detailed and traceable data support for subsequent thermal shock assessment, anomaly warning, and dynamic control strategies.

[0060] Continuously acquiring the temperature distribution matrix and heat flux matrix on the tower-top receiver surface at a millisecond sampling rate and recording their continuous temporal evolution is the core foundation for accurately identifying thermal dynamic responses and triggering control strategies in real-time within solar tower-type CSP plants. This process enables dynamic monitoring of the receiver surface's thermal state with high temporal resolution and spatial precision, capturing sudden changes in thermal load over extremely short periods of time. Because the heliostat array continuously adjusts its reflection direction based on the sun's trajectory and scheduling instructions, the distribution of the focused light spot on the receiver surface exhibits complex dynamic changes. In extreme situations, such as cloud cover dissipation and sudden weather changes, the heat flux can experience dramatic jumps within milliseconds. Relying solely on low-frequency data acquisition can easily miss critical precursors to thermal shocks, making it impossible to predict whether the system will enter overheating or unstable operation. By continuously acquiring high-frequency thermal field data and forming a complete time series structure, a thermal signature input dataset with causal relationships and evolutionary trends can be constructed, enabling the system to identify transient thermal shocks, train dynamic models, and fine-tune scheduling strategies. Furthermore, this dataset provides a reliable data foundation for subsequent frequency domain analysis, wavelet decomposition, thermal field modeling, and response prediction, making it an indispensable technical step in realizing intelligent CSP control systems. This process not only significantly improves the system's response sensitivity to high-frequency disturbances, but also lays a critical data foundation for improving CSP efficiency, protecting receiver equipment, and extending system life.

[0061] A fixed-length sliding time window is used to traverse the heat flux matrix sequence. Time difference calculation is performed within each window to obtain the heat flux density change slope. The effective heating area of ​​the receiver corresponding to each heat flux pixel point is then weighted to generate a heat flux growth rate sequence.

[0062] The specific steps of using a fixed-length sliding time window to traverse the heat flux density matrix sequence and generate the heat flux growth rate sequence are as follows:

[0063] Set the length of the sliding time window and the sliding step parameters, and extract the two-dimensional heat flow snapshot data window by window in chronological order in the continuous heat flux density matrix sequence to ensure that the data time period covered by each window has dynamic change continuity;

[0064] In order to realize the dynamic change analysis of the heat flux matrix sequence, it is first necessary to set the length of the sliding time window and the sliding step parameters according to the system response requirements to ensure that the time range covered by each window contains a sufficient number of heat flux data points to capture stable and representative change trends. Subsequently, in the entire heat flux matrix sequence, continuous two-dimensional heat flux snapshots in the current time period are extracted in chronological order, and these snapshots are arranged in time to form a complete data group as the input of the current sliding window. Next, the time window is shifted backward according to the preset sliding step to extract the heat flux snapshot data of the next time period, while ensuring that there is data overlap between adjacent windows, so that the entire time series has continuity and smooth transition, providing a stable and consistent input basis for subsequent heat flux differential analysis, change slope calculation and thermal response modeling.

[0065] In each sliding window, the first-order difference calculation is performed on the heat flux density values ​​of the same heat flux pixel at adjacent time points to obtain the heat flux density change slope of the pixel in the current window, and the change slopes of all pixels are combined to form a complete heat flux density change slope matrix;

[0066] According to the effective heated area corresponding to each pixel point on the receiver surface, the heat flux density change slope matrix is ​​spatially integrated using a weighted average method to generate a unique heat flux growth rate value corresponding to the current sliding window. The heat flux growth rate sequence is then composed according to the window order to characterize the dynamic evolution process of the heat flux input slope per unit time.

[0067] A fixed-length sliding time window is used to traverse the heat flux matrix sequence. Within each window, a temporal difference calculation is performed to obtain the slope of the heat flux change. This is then weighted by the effective heated area of ​​the receiver corresponding to each heat flux pixel to generate a heat flux growth rate sequence. This method is crucial for quantitatively extracting the dynamic changes in heat input to the tower-top receiver and enhancing its sensitive characteristics. As a key physical quantity measuring solar focusing intensity, heat flux changes over time directly reflect the combined influence of multiple factors, including heliostat scheduling, weather disturbances, and system response. By setting a fixed-length sliding time window, the entire heat flux data sequence can be segmented and analyzed, capturing local thermal trends within different time intervals and preventing global averaging from masking transient mutations. Temporal difference calculations within each window effectively calculate the slope of the heat flux change at each pixel over a short period of time, thereby identifying potential signs of thermal shock. By combining the actual heated area corresponding to each pixel on the receiver surface with area-weighted processing, the slope of the heat flux density variation, which has local spatial distribution differences, can be unified into a global heat flux growth rate value, enhancing the thermal representativeness and physical interpretability of the results. The resulting heat flux growth rate series not only reflects the overall trend of heat input, but also has high temporal resolution and strong response sensitivity. It serves as an important intermediate data foundation for constructing thermal shock indices, triggering scheduling control mechanisms, and implementing receiver thermal safety protection.

[0068] In each sliding window, the first-order difference calculation is performed on the heat flux density values ​​of the same heat flux pixel at adjacent time points to obtain the heat flux density change slope of the pixel in the current window, and the change slopes of all pixels are combined to form a complete heat flux density change slope matrix. The specific steps are as follows:

[0069] For the heat flux matrix sequence arranged by time in the current sliding window, the heat flux density value of each pixel point at all time sections is extracted, and the heat flux density time vector of the pixel point is constructed in time sequence;

[0070] Perform a first-order difference operation on each heat flux time vector, that is, calculate the heat flux increment between two adjacent time points, and divide the increment by the time interval to obtain the heat flux change slope sequence of the pixel point in each time period within the current sliding window;

[0071] The heat flux density change slope value of each pixel point in the entire sliding window is reconstructed into a two-dimensional matrix structure according to its position index on the receiver surface to form a complete heat flux density change slope matrix, which is used to represent the dynamic characteristic distribution of the heat flux density change at each spatial position in the current sliding window.

[0072] Within each sliding window, a first-order difference calculation is performed on the heat flux density values ​​at adjacent time points for the same heat flux pixel. This calculation obtains the slope of the heat flux change for that pixel within the current window. The slopes of all pixel changes are then combined to form a complete heat flux slope matrix. This matrix primarily extracts the temporal differential of the heat flux density, thereby revealing the transient response characteristics of the heat input at various regions on the tower receiver surface. Compared to static heat flux values, the slope of the heat flux change more accurately reflects the immediate impact of external environmental disturbances (such as cloud movement and mirror scheduling) or internal control actions on the receiver's thermal state. Under the sliding window mechanism, the system segments the heat flux data based on time, ensuring local continuity and relative stability within each window. This ensures the physical rationality and computational accuracy of the difference operation. By performing first-order differences between consecutive time points for each pixel, the magnitude of the heat flux change at that point over a short period of time is captured, reflecting the slope of the local heat input change in the time series and characterizing the dynamic trend of an increase or decrease. After completing this operation for all pixels, their slopes are reconstructed according to their spatial positions into a two-dimensional heat flux density slope matrix. This not only preserves the spatial distribution structure of the receiver's heat input but also enhances the time sensitivity of the dynamic changes in heat flux. This matrix serves as a key intermediate result for subsequent heat flux growth rate calculations, thermal shock identification, and energy scheduling optimization. It provides a data foundation and analytical basis for accurately understanding the receiver's thermal response state, identifying potential thermal risks in advance, and triggering intelligent control. This improves the operational stability and thermal safety of the entire CSP plant system under complex meteorological conditions.

[0073] The maximum temperature gradient value at each time point is extracted from the temperature distribution matrix. The maximum temperature gradient at each time point is paired with the corresponding heat flux growth rate. A thermal shock coupling factor sequence is generated through a standardized mapping function to characterize the joint changes in thermal gradient intensity and heat flux input rate.

[0074] The specific steps for extracting the maximum temperature gradient value at each time point from the temperature distribution matrix, pairing the maximum temperature gradient at each time point with the corresponding heat flux growth rate, and generating the thermal shock coupling factor sequence through the standardized mapping function are as follows:

[0075] For the temperature distribution matrix corresponding to each time point, the temperature variation between adjacent pixels is calculated based on the spatial first-order difference method, and the maximum value is extracted as the maximum temperature gradient at the current time point, which is used to characterize the thermal imbalance intensity of the local area on the receiver surface.

[0076] The maximum temperature gradient value extracted at each time point is paired one-to-one with the heat flux growth rate calculated at the same time point to form a two-dimensional feature point sequence indexed by the time point, which is used to synchronously describe the joint characteristics of the heat input rate and the spatial thermal gradient.

[0077] A standardized mapping function is applied to the acquired two-dimensional feature point sequence to unify the numerical scale and enhance the mathematical correlation, thereby generating a thermal shock coupling factor sequence with a unified dimension, providing the input variable basis for the subsequent thermal shock intensity index calculation and the triggering of dynamic control strategies.

[0078] A standardized mapping function is applied to the acquired two-dimensional feature point sequence to uniformly convert different physical quantities (such as maximum temperature gradient and heat flux growth rate) to the same numerical scale, achieving dimensional uniformity, numerical comparability, and consistency in mathematical processing. This process first involves linearly transforming the maximum temperature gradient sequence and the heat flux growth rate sequence, respectively, using normalization or Z-score standardization to bring their numerical ranges into the same interval (e.g., 0 to 1, or with a mean of 0 and a standard deviation of 1), thereby eliminating asymmetries in magnitude between different physical quantities. Next, the standardized temperature gradient and heat flux growth rate values ​​at each time point are input as a set of two-dimensional vectors and mapped using a predefined weighted superposition function or bivariate fusion function (such as weighted averaging, nonlinear combination, or principal component transformation). This yields a single scalar value, the thermal shock coupling factor, that reflects the degree of coupling between the thermal gradient intensity and the heat flux input rate. In this way, the thermal shock coupling factor not only has a unified dimension and distribution characteristic in terms of numerical value, but also strengthens the dynamic correlation between the two key thermal response variables, making the indicator both physically interpretable and statistically sensitive, and able to provide a stable and reliable input basis for subsequent thermal shock intensity assessment and control decisions.

[0079] The maximum temperature gradient value at each time point is extracted from the temperature distribution matrix. The maximum temperature gradient at each time point is then paired with the corresponding heat flux growth rate. A sequence of thermal shock coupling factors is then generated using a standardized mapping function. Its core function is to achieve comprehensive risk quantification and joint feature modeling of the dynamic evolution of the receiver's thermal load. The temperature gradient is an important indicator of the uneven distribution of thermal energy in space. Especially in solar thermal systems, a sharp increase in local temperature gradient often indicates potential thermal stress accumulation, material expansion imbalance, and decreased system thermal stability. The heat flux growth rate directly reflects the rate of heat energy input into the system per unit time. Both are key physical factors contributing to transient thermal shock. While it is difficult to accurately reflect the overall thermal risk based on either variable alone, a joint analysis of the two can effectively improve the ability to assess the probability and intensity of thermal shock. By extracting the most dramatic temperature gradient change in the temperature field at each time point and correlating it with the heat flux growth rate at the same moment, a two-dimensional data pair reflecting the instantaneous thermal environment state is constructed. Furthermore, a standardized mapping function is introduced to perform dimensionless processing and fusion on this set of data pairs, ensuring that variables under different dimensions participate in the coupling analysis at the same scale, eliminating errors caused by differences in units, dimensions, and scales. The resulting thermal shock coupling factor sequence fully records the dynamic joint evolution trend of thermal gradient intensity and heat flux input rate in the time dimension, providing a continuous, stable, and sensitive composite indicator for subsequent judgment of whether there is an abnormal thermal jump and triggering regulatory actions. This step is a key link in converting thermal variables into discriminable control signals, and plays an important role in improving the system's ability to identify and dynamically respond to extreme meteorological disturbances.

[0080] Analyze the amplitude variation characteristics of the thermal shock coupling factor sequence within the current sliding cycle, combine it with the time evolution trend of the thermal disturbance, and calculate the transient thermal shock intensity index to reflect the degree of thermal load jump of the receiver;

[0081] The specific steps for calculating the transient thermal shock intensity index by analyzing the amplitude variation characteristics of the thermal shock coupling factor sequence within the current sliding cycle and combining it with the time evolution trend of the thermal disturbance are as follows:

[0082] The thermal shock coupling factor sequence corresponding to the current sliding period is selected, and a multi-index analysis method combining maximum value, average value and standard deviation is used to extract the amplitude variation characteristics of the thermal shock coupling factor in the current time period, which is used to simultaneously characterize its overall fluctuation degree and local extreme value intensity.

[0083] To extract the amplitude variation characteristics of the thermal shock coupling factor within the current sliding cycle, a complete thermal shock coupling factor sequence is first selected from the current time window. This sequence consists of coupling factor values ​​calculated at each sampling time point within the cycle, representing the joint characteristics of the heat input rate and temperature gradient changes within this time period. Subsequently, the maximum, average, and standard deviation of this sequence are calculated. The maximum value characterizes the peak intensity of the local extreme thermal disturbance, the average value reflects the basic load state of the overall thermal shock level, and the standard deviation measures the degree of thermal disturbance fluctuation within the time period. These three factors together form a multi-index analysis system, achieving multi-dimensional quantification of the thermal dynamic behavior within the current sliding cycle. This can not only reveal the extreme risk of short-term thermal load surges but also identify stability trends during system operation. This process not only enhances the expressiveness of data features but also provides a high-dimensional, highly discriminative input parameter foundation for subsequent trend modeling and thermal shock intensity index calculation, improving the system's response sensitivity and decision-making accuracy in complex thermal disturbance scenarios.

[0084] The amplitude variation characteristics of the current sliding cycle and the consecutive sliding cycles before and after it are constructed as a time series. Polynomial fitting and slope extraction operations are performed on the current time series to establish a time evolution trend model of thermal disturbance to characterize the growth rate and change direction of thermal shock intensity.

[0085] To model the temporal evolution of thermal disturbances, a fixed number of sliding cycles are selected forward and backward, centered on the current sliding cycle, to form a continuous time period encompassing multiple cycles. The characteristic amplitude variation values ​​of the thermal shock coupling factor (such as maximum, mean, or standard deviation) are extracted from each cycle and arranged chronologically to form a one-dimensional time series, which reflects the trajectory of the thermal disturbance intensity over this continuous timeframe. Next, a polynomial fitting method is applied to the constructed time series, using the least squares method to construct a smooth trend curve. This fitting process removes the interference of short-term fluctuations and captures the overall direction of change and potential upward or downward trends. Finally, the first-order derivative of the fitted curve is calculated at the position corresponding to the current sliding cycle to extract the trend slope. This slope describes the speed and direction of change in thermal shock intensity at the current moment and is an important dynamic indicator for determining whether the thermal disturbance is continuing to intensify, weaken, or stabilize. This process forms a continuous, quantifiable, and predictive model of the thermal disturbance evolution trend, providing a key basis for determining whether a high-risk thermal shock state is imminent.

[0086] Based on the amplitude change characteristics of the current sliding cycle and the trend indicators in the thermal disturbance evolution trend model, dynamic weighted calculation is performed to generate a transient thermal shock intensity index that reflects the intensity of short-term heat input surges and the evolution direction of future thermal shock risks, providing a quantitative basis for the subsequent startup decision of the light and heat input control strategy.

[0087] To generate the transient thermal shock intensity index, the amplitude variation characteristics extracted during the current sliding cycle (such as the maximum, mean, and standard deviation of the thermal shock coupling factor) are first structurally combined with the trend slope value calculated from the thermal disturbance evolution trend model. This creates a multidimensional thermal feature vector that simultaneously reflects both the intensity state and the evolution trend of the current thermal disturbance. Next, a dynamic weighting calculation is performed on each characteristic component of this thermal feature vector based on pre-set weight coefficients. The weighting principle is adjusted based on the system's operational priorities, for example, increasing the weight of the trend slope during sudden weather conditions and increasing consideration of the average heat load during stable operation. This dynamic weighting process comprehensively assesses whether the current heat input is experiencing a sudden surge and predicts whether it is likely to continue to increase over time, thereby deriving a physically meaningful and response-oriented transient thermal shock intensity index. Higher values ​​of this index indicate a greater immediate risk of thermal shock and a more persistent likelihood of occurrence. This index can be used as a quantitative basis for determining when to initiate the control system's response mechanism, providing a reliable data-driven signal for dynamically adjusting the heliostat concentrating strategy and receiver thermal conductivity parameters.

[0088] Analyzing the amplitude variation characteristics of the thermal shock coupling factor sequence within the current sliding cycle and combining it with the temporal evolution of thermal disturbances to calculate a transient thermal shock intensity index (TTI) is crucial for accurately identifying the risk of sudden changes in receiver heat load in solar tower-type concentrated thermal power plants and providing a forward-looking, quantitative basis for real-time system control. Under complex lighting conditions and dynamic mirror field scheduling, the heat input to the receiver often does not vary uniformly but can experience dramatic increases in a very short period of time. The TTI, as a thermal response indicator that integrates the temperature gradient and heat input rate, is sensitive to these short-term intensity variations. By analyzing its amplitude characteristics, such as its maximum, average, and standard deviation within the current sliding cycle, it is possible to assess the intensity of the current transient thermal disturbance and identify whether local extremes exceed the threshold for stable operation. Further combining the temporal evolution of thermal disturbances—observing the direction and rate of change of the TTI over multiple sliding cycles—can determine whether the system is in a risk growth channel. By integrating these two types of information through a dynamically weighted calculation, the resulting transient thermal shock intensity index reflects both the magnitude of the sudden increase in heat input at the current moment and the likelihood that this trend will persist in the future. The role of this index is not limited to quantitatively expressing the real-time thermal state. It also provides executable control triggers, such as initiating strategies such as heliostat focus reduction and receiver thermal conductivity adjustment, thereby effectively avoiding engineering risks such as overheating, fluid instability, and system shock, and ensuring the safety, stability, and efficiency of the entire solar thermal system.

[0089] The amplitude variation characteristics of the current sliding cycle and the consecutive sliding cycles before and after it are constructed as a time series. Polynomial fitting and slope extraction operations are performed on the current time series to establish a time evolution trend model of thermal disturbance. The specific steps are as follows:

[0090] Taking the current sliding cycle as the center, a continuous data segment containing a fixed number of sliding cycles before and after is selected. The amplitude change characteristic value of the corresponding thermal shock coupling factor is extracted from each cycle, and the amplitude change time series of the thermal shock is constructed in chronological order to represent the dynamic change trajectory of the thermal disturbance over multiple cycles.

[0091] Based on the constructed time series of thermal shock amplitude changes, a polynomial function of a preset order is selected to perform a least squares fitting operation to generate a smooth fitting curve with continuous derivative properties, which is used to approximate the trend line of thermal shock intensity changes over time;

[0092] Polynomial functions are a fundamental type of function widely used in data modeling and trend analysis. Essentially, they are smooth function models composed of weighted combinations of power terms, exhibiting excellent adjustability and continuity. When analyzing a time series of thermal shock amplitude variations, a polynomial function of a preset order is selected for least squares fitting. The goal is to approximate the overall trend of the temporal evolution of thermal disturbance intensity with a smooth curve without altering the original data structure. Polynomial functions exhibit excellent mathematical differentiability, allowing the slope of the change to be directly extracted after fitting, helping to identify the current growth rate and direction of the thermal disturbance. Compared to other complex function models, polynomial functions offer simplicity and high stability, and their chosen order allows for flexible adaptation to different types of fluctuations. When thermal disturbances vary slowly, low-order functions are sufficient to represent the overall trend. However, when the data exhibit multiple peaks, transitions, or fluctuations, increasing the order can enhance the fitting capability. Using this method for least squares fitting can not only effectively eliminate local fluctuations and sampling errors in the original data, but also improve the accuracy of subsequent trend identification and risk prediction, providing a stable and explainable analytical basis for dynamic control strategies.

[0093] A first-order derivative operation is performed on the fitting curve at the time point corresponding to the current sliding period to extract the trend slope index at the current time point, which is used to measure the growth rate and direction of the thermal disturbance and serves as the core dynamic parameter for constructing the thermal disturbance time evolution trend model.

[0094] The amplitude variation characteristics of the current sliding cycle and its preceding and succeeding sliding cycles are constructed as a time series. Polynomial fitting and slope extraction are performed on this time series. This establishes a thermal disturbance trend identification mechanism with time-evolution capabilities, thereby accurately characterizing the rate and direction of change in thermal shock intensity. In solar tower-type concentrated thermal power plants, the heat load on the receiver surface fluctuates in a complex dynamic manner due to factors such as heliostat focusing strategies, solar irradiance variations, and atmospheric disturbances. It is difficult to determine whether the fluctuations are persistent or incidental based solely on the thermal shock data from the current cycle. Therefore, by constructing a time series of amplitude variation characteristics encompassing the current sliding cycle and its adjacent cycles, the evolution of the thermal shock over time can be fully captured. Polynomial fitting is then used to smooth this time series, eliminating local fluctuations and extracting a stable, continuous trend line. By extracting the slope of the fitted curve at the current time point, a quantitative indicator is obtained to determine whether the current thermal disturbance is in an increasing, decreasing, or relatively stable state. This slope information not only reflects the rate of change in heat input intensity but also serves as a basis for predicting future risk trends, providing a crucial reference for the control system's response strategy selection. Overall, this step is a critical link in transitioning from thermal shock perception to dynamic trend assessment. It serves as a crucial bridge connecting raw thermal response characteristics with dispatch control logic, helping to enhance the system's intelligent response capabilities and operational safety in complex climate conditions.

[0095] When the transient thermal shock intensity index exceeds a preset threshold, a joint control strategy is triggered. The flow rate of the working fluid at the receiver inlet is reduced to extend the heat exchange residence time, while the incident angle of some heliostats is adjusted to reduce the focusing efficiency of the reflected light, thereby achieving a dynamic balance between heat input and heat transfer output.

[0096] When the transient thermal shock intensity index exceeds the preset threshold, the joint control strategy is triggered. Based on the transient thermal shock intensity index generated by the thermal shock coupling factor sequence analysis results and the reference threshold set based on engineering experience, a control incentive reflecting the current severity of the thermal shock is established. The thermal response control factor is constructed to characterize the driving strength of the thermal control by the intensity of the current thermal disturbance. The calculation expression is as follows:

[0097] ,

[0098] Where, It is a transient thermal shock intensity index, which represents the fusion index of the slope of the heat flux density change and the superposition effect of the temperature gradient extreme value suffered by the receiver per unit time during the current sliding cycle. It is used to measure the "intensity" and "severity" of thermal disturbances and reflect whether the current heat input is sudden and high-risk. It is the reference threshold of transient thermal shock intensity index, which represents the upper limit of thermal disturbance that the equipment can safely withstand. It is the thermal response control factor, which is an intermediate variable that maps the transient thermal shock index to the control intensity. By comparing the ratio of the current index to the reference threshold and taking the logarithm, its growth rate and amplitude are controlled;

[0099] After sensing the risk of thermal shock, the current transient thermal shock intensity is compared with the system safety threshold, and a thermal response control factor is constructed through logarithmic mapping, thereby converting the complex degree of thermal disturbance into a continuous and quantifiable control excitation quantity, providing a direct driving force for the subsequent refined control of the thermal input and output paths, ensuring that the control response is both sensitive and stable.

[0100] Based on the obtained thermal response control factor , the working fluid flow rate at the receiver inlet and the focusing efficiency of the heliostat are jointly adjusted to suppress the energy impact in a short period of time. The current working fluid flow rate is corrected by an exponential adjustment function to generate the target flow rate. The generation formula is as follows:

[0101] ,

[0102] Where, It is the adjusted working fluid flow rate. By reducing the flow rate, the residence time of the working fluid in the heat absorption area is prolonged, and the instantaneous heat energy absorption and buffering capacity is improved. is the current real-time working fluid inlet flow rate, which is used as the reference input value for control calculation. is the natural base, Is the working fluid flow rate response sensitivity coefficient; used to adjust right The greater the value, the more sensitive the working fluid flow rate response is. It will cause a greater drop in flow rate; usually set according to physical characteristics such as receiver thermal inertia and heat transfer delay of heat exchanger;

[0103] An exponential regulation function is a common nonlinear regulation function. Its core characteristic is that its output exponentially increases or decreases with changes in the input variable. This type of function is a fundamental function widely used in control engineering and system modeling, and possesses the mathematical properties of fast response, continuous smoothness, and no abrupt changes. In thermal control systems, an exponential regulation function is used to modify the current working fluid flow rate, primarily to achieve sensitive response and dynamic buffering to thermal disturbances. Its advantages lie in: when the thermal shock intensity is low (low excitation factor), the regulation effect is weak, and the system maintains its original stable state. However, when the thermal shock intensity increases rapidly (high excitation factor), the regulation function accelerates the flow rate, rapidly reducing heat input and improving response speed and safety. This nonlinear decreasing mechanism is more sensitive to high-risk conditions than linear regulation, avoiding delayed system response and energy overload under critical thermal load conditions. Modifying the working fluid flow rate through an exponential function not only achieves flexible transitions and dynamic protection, but also automatically adjusts the control amplitude according to the disturbance intensity, making it an effective mathematical tool for ensuring thermal balance and stability of the receiver.

[0104] This expression reduces the entry velocity of the working fluid and prolongs its residence time in the heat absorption area, thereby improving the heat absorption efficiency per unit mass of the working fluid and achieving buffered control of energy absorption and transmission.

[0105] At the same time, a nonlinear reduction model based on the hyperbolic tangent function is applied to the light focusing efficiency of the heliostat to generate a new focusing efficiency target value. The generation formula is as follows:

[0106] ,

[0107] Where, It is the corrected target focusing efficiency target value, ranging from 0 to 1, and is used to dynamically adjust the incident angle or attitude of the heliostat to reduce the local energy density in the focusing area, thereby reducing the risk of local overheating. is the actual focusing efficiency of the current heliostat array, that is, the proportion of energy effectively focused to the receiver by the heliostat system at the current moment. It serves as the reference value for regulation and determines the amplitude of the change in the final target focusing intensity. is the focus efficiency response sensitivity coefficient, used to adjust The sensitivity of the focusing efficiency control result is as follows: the larger the value, the more sensitive the focusing efficiency adjustment is. It can quickly disperse the light energy in response to thermal shock, and is suitable for mirror systems with fast response and high focusing accuracy. is the hyperbolic tangent function.

[0108] In the process of regulating the focusing efficiency of the heliostat, the hyperbolic tangent function ( ) as a nonlinear response function, is mainly based on its continuous, differentiable, and saturated output characteristics to achieve smooth suppression and progressive limiting control of the thermal disturbance response intensity. The hyperbolic tangent function grows almost linearly when the input is small, and can sensitively reflect the trend of slight thermal disturbances; when the input increases, its output tends to saturate and gradually converges to the boundary of a fixed interval (approaching 1), thereby effectively preventing over-regulation and avoiding a significant reduction in the focusing efficiency of the heliostat, which leads to a decrease in system energy efficiency. Compared with linear or other radical functions, This function exhibits good control flexibility under low and medium thermal shock intensities, while providing a natural upper limit for regulation under high-intensity thermal shocks, ensuring both rapid and stable control responses. It is suitable for control scenarios like thermal shocks, which exhibit nonlinear growth trends while also requiring system continuity protection. Therefore, this function is preferred for constructing a heliostat response control mechanism that is both sensitive and robust.

[0109] Based on the current level of thermal disturbance, the flow rate of the working fluid at the receiver inlet and the focusing efficiency of the heliostats are jointly adjusted to achieve synchronized dynamic balance control of heat input and heat output. By reducing the working fluid flow rate to extend its residence time within the receiver, while moderately weakening the focusing ability of some heliostats, the dramatic increase in heat load caused by transient thermal shocks can be effectively mitigated, preventing local overheating and heat runaway, thereby ensuring the safe and stable operation of the CSP system under high thermal disturbance conditions.

[0110] During the operation of a solar tower-type concentrated solar power plant, when the system detects that the transient thermal shock intensity index exceeds a preset threshold, the core function of triggering a joint control strategy is to rapidly suppress the risk of transient thermal shocks to the receiver and the entire energy conversion chain, while achieving a dynamic balance between solar thermal input and heat transfer output. This step establishes a closed-loop control mechanism with responsiveness and control flexibility by coordinating the receiver's working fluid flow rate with the focusing efficiency of the heliostats. First, lowering the receiver's inlet working fluid flow rate prolongs the working fluid's residence time within the absorber, thereby increasing the heat absorption capacity per unit volume of the working fluid. This allows the energy input of transient high heat flux to be uniformly absorbed and transferred over a longer timescale, effectively mitigating the risks of instability caused by sudden heat flux increases, such as local overheating, uneven thermal expansion, or flow disturbances. Second, by adjusting the incident angle of some heliostats so that their reflected light slightly deviates from the focal direction, the luminous flux density per unit area on the receiver surface can be reduced, actively reducing the intensity of external solar radiation input. This "soft limiting" strategy for focusing efficiency helps prevent light energy from concentrating in a limited receiving area over a short period of time, avoiding the formation of hot spot effects or the risk of oversaturated energy burns. The two work together to form a coordinated control mechanism of "internal control absorption + external control input." While ensuring that the system's heat transfer efficiency is largely unaffected, it significantly improves the system's adaptability and stable operation to severe fluctuations in heat loads under extreme weather disturbances, providing key support for the efficient, safe, and intelligent dynamic operation of CSP plants.

[0111] When the thermal shock intensity falls back to a safe range, the control system gradually restores the normal flow rate of the working fluid and orderly restores the focusing angle of the heliostat to avoid "reverse shock" during the warming period and ensure the smooth transition and thermal field reconstruction of the receiver after the thermal disturbance ends.

[0112] To achieve smooth transition control after thermal disturbances, the control system needs to activate the recovery control mechanism after detecting that the transient thermal shock intensity index has fallen back to a safe range, gradually restoring the balance between heat input and heat output in stages. First, the system slowly increases the flow rate of the working fluid (such as molten salt or thermal oil) at the receiver inlet based on the current heat flow change trend, gradually restoring it from a restricted state to a normal operating flow rate. This process is achieved through flexible transition strategies such as segmented interpolation or exponential recovery to avoid pressure fluctuations or flow instabilities caused by transient changes in the medium flow rate. At the same time, to match the gradual recovery of the working fluid's heat absorption capacity, the system uses a grouping strategy to orderly restore the focusing angle of the heliostats, preferentially activating the mirror group away from the center of the receiver and gradually advancing towards the core area to ensure that the heat load recovery rate is always within the controllable range of the receiver's thermal inertia.

[0113] The core function of this control step is to prevent the "reverse shock" phenomenon, that is, after the thermal load has just stabilized, the system will recover too quickly, resulting in a secondary thermal energy shock, thereby causing structural stress concentration, thermal fatigue accumulation, or the risk of rapid cooling and heating of materials. In addition, this step can also achieve spatial reconstruction of the thermal field, so that a stable, uniform, and controllable temperature gradient distribution can be re-established on the surface of the receiver, providing a reliable thermal foundation for subsequent continuous power generation or heat storage operations. Through this progressive recovery mechanism, the system can smoothly transition from a severely disturbed state to a steady-state operation, significantly improving the thermal safety, operational stability, and equipment life of the power station.

[0114] Through the above-mentioned solar tower-type solar thermal power station heliostat field optimization scheduling control method, it is possible to achieve high-resolution perception, dynamic feature extraction and intelligent response control of the thermal disturbance process, significantly improving the thermal safety and operational stability of the power station under extreme meteorological conditions. This method accurately captures the changing trend of the receiver surface heat load at the millisecond scale by constructing a joint feature sequence of temperature and heat flow, and combines the multi-dimensional evolution characteristics of the thermal shock coupling factor to construct a transient thermal shock intensity index with predictive capabilities, thereby realizing feedforward triggering and intensity adaptive adjustment of the control behavior. Compared with the traditional passive response mechanism, the present invention can actively adjust the working fluid flow rate and the heliostat focusing behavior before the thermal shock causes irreversible damage, achieve a dynamic balance between the heat input rate and the heat conduction capacity, effectively avoid the occurrence of overheating, overpressure, fluid instability and other phenomena, extend the service life of the receiver, and improve the continuity, safety and energy conversion efficiency of the solar thermal power generation system.

[0115] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0116] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

[0117] It should be noted that, in this document, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0118] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0119] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0120] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0121] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0122] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0123] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0124] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. A method for optimizing the scheduling and control of a heliostat field in a solar tower-type concentrated thermal power station, characterized in that: The following steps are involved: The temperature distribution matrix and heat flux density matrix of the tower top receiver surface are continuously acquired synchronously with a millisecond sampling period, and their continuous changes in the time dimension are recorded to form a thermal characteristic input data set for transient response analysis. A fixed-length sliding time window is used to traverse the heat flux matrix sequence. Time difference calculation is performed in each window to obtain the heat flux density change slope. The heat flux growth rate sequence is generated by area weighting based on the effective heating area of ​​the receiver corresponding to each heat flux pixel point. The maximum temperature gradient value at each time point is extracted from the temperature distribution matrix. The maximum temperature gradient at each time point is paired with the corresponding heat flux growth rate. A thermal shock coupling factor sequence is generated through a standardized mapping function to characterize the joint changes in thermal gradient intensity and heat flux input rate. Analyze the amplitude variation characteristics of the thermal shock coupling factor sequence within the current sliding cycle, combine it with the time evolution trend of the thermal disturbance, and calculate the transient thermal shock intensity index to reflect the degree of thermal load jump of the receiver; When the transient thermal shock intensity index exceeds the preset threshold, the joint control strategy is triggered. By reducing the working fluid flow rate at the receiver inlet to extend the heat exchange residence time, and adjusting the incident angle of some heliostats to reduce the focusing efficiency of the reflected light, a dynamic balance between heat input and heat transfer output is achieved.

2. The method for optimizing the scheduling and control of the heliostat field of a solar tower-type thermal power station according to claim 1, characterized in that: The specific steps to obtain the temperature distribution matrix and heat flux density matrix of the tower top receiver surface and form the thermal characteristic input data set for transient response analysis are as follows: A three-dimensional coordinate reference system is established to grid the receiver surface. High-precision infrared imaging devices and radiation heat flow sensing modules are used to synchronously collect instantaneous temperature values ​​and unit area heat flux density values ​​at each grid cell to generate a high-resolution thermal field information matrix. The temperature values ​​and heat flux values ​​obtained by continuous sampling are paired in time sequence according to a unified timestamp to construct a two-dimensional thermal field sequence structure, and a linear interpolation algorithm is used to fill in the missing data caused by sampling jitter; The constructed thermal field sequence structure is subjected to discrete Fourier transform and wavelet packet decomposition to extract the local change characteristics in the time dimension and form a thermal response analysis vector set with time-frequency distribution information.

3. The method for optimizing the scheduling and control of the heliostat field of a solar tower-type thermal power station according to claim 1, characterized in that: The specific steps of using a fixed-length sliding time window to traverse the heat flux density matrix sequence and generate the heat flux growth rate sequence are as follows: Set the length of the sliding time window and the sliding step parameters, and extract the two-dimensional heat flux snapshot data window by window in time order from the continuous heat flux density matrix sequence; In each sliding window, the first-order difference calculation is performed on the heat flux density values ​​of the same heat flux pixel at adjacent time points to obtain the heat flux density change slope of the pixel in the current window, and the change slopes of all pixels are combined to form a complete heat flux density change slope matrix; According to the effective heated area corresponding to each pixel point on the receiver surface, the heat flux density change slope matrix is ​​spatially integrated using a weighted average method to generate a unique heat flux growth rate value corresponding to the current sliding window, and a heat flux growth rate sequence is formed according to the window order.

4. The method for optimizing the scheduling and control of the heliostat field of a solar tower-type thermal power station according to claim 3, characterized in that: In each sliding window, the first-order difference calculation is performed on the heat flux density values ​​of the same heat flux pixel at adjacent time points to obtain the heat flux density change slope of the pixel in the current window, and the change slopes of all pixels are combined to form a complete heat flux density change slope matrix. The specific steps are as follows: For the heat flux matrix sequence arranged by time in the current sliding window, the heat flux density value of each pixel point at all time sections is extracted, and the heat flux density time vector of the pixel point is constructed in time sequence; Perform a first-order difference operation on each heat flux time vector, that is, calculate the heat flux increment between two adjacent time points, and divide the increment by the time interval to obtain the heat flux change slope sequence of the pixel point in each time period within the current sliding window; The heat flux density change slope value of each pixel point in the entire sliding window is reconstructed into a two-dimensional matrix structure according to its position index on the receiver surface to form a complete heat flux density change slope matrix.

5. The method for optimizing the scheduling and control of the heliostat field of a solar tower-type thermal power station according to claim 1, characterized in that: The specific steps for extracting the maximum temperature gradient value at each time point from the temperature distribution matrix, pairing the maximum temperature gradient at each time point with the corresponding heat flux growth rate, and generating the thermal shock coupling factor sequence through the standardized mapping function are as follows: For the temperature distribution matrix corresponding to each time point, the temperature variation between adjacent pixels is calculated based on the spatial first-order difference method, and the maximum value is extracted as the maximum temperature gradient at the current time point; The maximum temperature gradient value extracted at each time point is paired one-to-one with the heat flow growth rate calculated at the same time point to form a two-dimensional feature point sequence indexed by the time point; A standardized mapping function is applied to the obtained two-dimensional feature point sequence to unify the numerical scale and enhance the mathematical correlation, thereby generating a thermal shock coupling factor sequence with a unified dimension.

6. The method for optimizing the scheduling and control of the heliostat field of a solar tower-type thermal power station according to claim 1, characterized in that: The specific steps for calculating the transient thermal shock intensity index by analyzing the amplitude variation characteristics of the thermal shock coupling factor sequence within the current sliding cycle and combining it with the time evolution trend of the thermal disturbance are as follows: Select the thermal shock coupling factor sequence corresponding to the current sliding period and use the multi-index analysis method to extract the amplitude variation characteristics of the thermal shock coupling factor in the current time period; The amplitude variation characteristics of the current sliding cycle and the multiple consecutive sliding cycles before and after it are constructed as a time series. Polynomial fitting and slope extraction operations are performed on the current time series to establish a time evolution trend model of thermal disturbance. Based on the amplitude change characteristics of the current sliding cycle and the trend indicators in the thermal disturbance evolution trend model, dynamic weighted calculation is performed to generate a transient thermal shock intensity index that reflects the sudden increase in short-term heat input and the evolution direction of future thermal shock risks.

7. The method for optimizing the scheduling and control of the heliostat field of a solar tower-type thermal power station according to claim 6, characterized in that: The amplitude variation characteristics of the current sliding cycle and the consecutive sliding cycles before and after it are constructed as a time series. Polynomial fitting and slope extraction operations are performed on the current time series to establish a time evolution trend model of thermal disturbance. The specific steps are as follows: Taking the current sliding cycle as the center, a continuous data segment containing a fixed number of sliding cycles before and after is selected. The corresponding amplitude change characteristic value of the thermal shock coupling factor is extracted from each cycle and arranged in chronological order to construct a time series of thermal shock amplitude change. Based on the constructed time series of thermal shock amplitude changes, a polynomial function of a preset order is selected to perform a least squares fitting operation to generate a smooth fitting curve with continuous derivative properties; A first-order derivative operation is performed on the fitting curve at the time point corresponding to the current sliding period to extract the trend slope index at the current time point, which is used to measure the growth rate and direction of the thermal disturbance.

8. The method for optimizing the scheduling and control of the heliostat field of a solar tower-type thermal power station according to claim 1, characterized in that: When the transient thermal shock intensity index exceeds the preset threshold, the joint control strategy is triggered. Based on the transient thermal shock intensity index generated by the thermal shock coupling factor sequence analysis results and the reference threshold set based on engineering experience, a control incentive reflecting the current severity of the thermal shock is established. The thermal response control factor is constructed to characterize the driving strength of the thermal control by the intensity of the current thermal disturbance. The calculation expression is as follows: ， Where, is the transient thermal shock strength index, is the transient thermal shock intensity index reference threshold, It is the thermodynamic response regulating factor; Based on the obtained thermal response control factor , the working fluid flow rate at the receiver inlet and the focusing efficiency of the heliostat are jointly adjusted to suppress the energy impact in a short period of time. The current working fluid flow rate is corrected by an exponential adjustment function to generate the target flow rate. The generation formula is as follows: ， Where, is the adjusted working fluid flow rate, is the current real-time working fluid inlet flow rate, is the natural base, is the working fluid flow rate response sensitivity coefficient; At the same time, a nonlinear reduction model based on the hyperbolic tangent function is applied to the light focusing efficiency of the heliostat to generate a new focusing efficiency target value. The generation formula is as follows: ， Where, is the corrected target focusing efficiency target value, is the actual focusing efficiency of the current heliostat array, is the focusing efficiency response sensitivity coefficient, is the hyperbolic tangent function.

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