Steam heat release control method and system for modularized fused salt energy storage

Abstract

The invention discloses a steam heat release control method and system for modularized fused salt energy storage, and relates to the technical field of fused salt energy storage, and the method comprises the steps: building a BP neural network model of a modularized fused salt energy storage and heat release control system, and training the BP neural network model; defining output errors after normalization processing, and analyzing the BP neural network model according to the output errors; and processing the analyzed BP neural network model by adopting a variable rate regulation technology, establishing an actual object mathematical model opposite to the BP neural network model, and completing steam heat release control of modular fused salt energy storage. The steam heat release control precision and response speed of the modularized fused salt energy storage system are effectively improved, the stability and reliability of the control system are ensured, fine control over the frequency of the fused salt pump is achieved, the heat exchange process of fused salt and water vapor is optimized, the overall efficiency of the energy storage system is remarkably improved, and energy loss is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of molten salt energy storage, and in particular to a steam heat release control method and system for modular molten salt energy storage. Background Art

[0002] At present, molten salt has become an ideal thermal energy storage medium due to its high specific heat capacity, high thermal stability and good thermal conductivity under high temperature conditions. The emergence of molten salt energy storage technology has solved the contradiction between intermittent energy supply and continuous demand. Traditional solar thermal power generation systems cannot collect enough solar radiation at night or on cloudy days. Molten salt energy storage can store excess heat energy during the day and release it when needed, thereby ensuring a continuous supply of electricity. The molten salt energy storage system is coupled with thermal power generating units to realize the thermal-electrical decomposition coupling of the units to assist in peak and frequency regulation, which is the current development trend.

[0003] At present, the commonly used molten salt energy storage systems are double-tank type and single-tank type. These two types of systems are usually bulky, have high investment costs and are difficult to adjust, which limits their widespread application. Modular molten salt energy storage technology is an advanced thermal energy storage solution that combines the high efficiency of molten salt energy storage with the flexibility of modular design, providing a new way to integrate renewable energy and optimize traditional thermal power generation, while the modular design further enhances the scalability, economy and adaptability of the system.

[0004] The modular molten salt energy storage system solves the above problems by dividing the large energy storage unit into multiple smaller and independently operable modules. Each module can be independently manufactured, tested, transported and installed, greatly reducing the complexity and cost of on-site construction. In addition, the modular design also allows the system to be flexibly expanded according to actual needs, whether it is initial construction or later expansion, it can quickly adapt to different load requirements. Summary of the invention

[0005] In view of the problems existing in the existing steam heat release control method and system for modular molten salt energy storage, the present invention is proposed.

[0006] Therefore, the purpose of the present invention is to provide a steam heat release control method and system for modular molten salt energy storage. In view of the problem of insufficient control accuracy of existing molten salt energy storage systems, the present invention adopts BP neural network combined with variable rate regulation technology to solve it.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In the first aspect, an embodiment of the present invention provides a method for controlling steam heat release of modular molten salt energy storage, which includes establishing a BP neural network model of a modular molten salt energy storage heat release control system, training the BP neural network model, and obtaining node parameters of the model; normalizing the node parameters, defining an output error after normalization, and analyzing the BP neural network model according to the output error; using a variable rate regulation technology to process the analyzed BP neural network model, and based on the processed BP neural network model, establishing a mathematical model of an actual object opposed to it, thereby completing the steam heat release control of the modular molten salt energy storage.

[0009] As a preferred solution of the steam heat release control method of modular molten salt energy storage of the present invention, wherein: establishing the BP neural network model includes extracting T according to the input N energy storage heat release parameters y1 、T y2 、T y3 and T y4 The medium temperature as molten salt;

[0010] The input includes inputting the N energy storage heat release parameters through the input layer and outputting them through the hidden layer. The hidden layer includes obtaining the number of nodes of the hidden layer by using the number of nodes of the input layer. The number of nodes of the input layer includes x1 to x7, where x1→fy, x2→Lso, x3→Tsin, x4→T y1 ,x5→T y2 ,x6→T y3 ,x7→T y4 , at this time, x4→T y1 represents a hidden layer, the number of nodes in the hidden layer is L=h, then the output formula of the jth neuron in the hidden layer is:

[0011]

[0012] Where i = 1, 2, 3...n, j = 1, 2, 3...L, wh ij represents the weight from the input layer to the hidden layer, a j represents the hidden layer node threshold, h j represents the jth neuron in the hidden layer.

[0013] As a preferred solution of the steam heat release control method of modular molten salt energy storage of the present invention, the output through the hidden layer includes introducing a node activation function to preprocess the j-th neuron of the hidden layer, accelerating the learning speed of the BP neural network model, and calculating the j-th neuron of the output layer. The specific calculation formula is:

[0014]

[0015] Among them, y j represents the jth neuron in the output layer, wy j represents the weight from the output layer to the hidden layer, b1 represents the node threshold of the output layer, h j represents the jth neuron in the hidden layer;

[0016] A preliminary neural network model is established for the j-th neuron in the hidden layer and the j-th neuron in the output layer, and all node parameters of the input and output energy storage and heat release are obtained according to the steam heat release under different conditions.

[0017] As a preferred solution of the steam heat release control method of modular molten salt energy storage of the present invention, wherein: the training BP neural network model includes setting the weights from the input layer to the hidden layer and the weights from the output layer to the hidden layer;

[0018] When the weight from the input layer to the hidden layer is greater than the hidden layer node threshold in the jth neuron, the BP neural network model enters the saturation zone. At this time, the BP neural network model converges, and the weight from the input layer to the hidden layer and the weight from the output layer to the hidden layer are sent to the saturation zone for setting, and the weight from the input layer to the hidden layer and the weight from the output layer to the hidden layer are randomly taken as non-zero values.

[0019] As a preferred solution of the steam heat release control method of modular molten salt energy storage of the present invention, the normalization of the node parameters includes unifying all node parameters using an excitation function, and the specific normalization calculation formula of the excitation function is:

[0020]

[0021] Among them, x represents the normalized value, x r Represents the physical quantity of the operation, x max represents the maximum value in the activation function, x min Represents the minimum value in the activation function;

[0022] The specific formula for defining the output error after normalization is:

[0023]

[0024] in, represents the output value of the BP neural network model, y1 represents the actual value, and E represents the output error after normalization.

[0025] As a preferred solution of the steam heat release control method of modular molten salt energy storage of the present invention, wherein: the BP neural network model according to the output error analysis includes setting a minimum error ∈;

[0026] When E<∈, the BP neural network model is established;

[0027] When E≥∈, the weights of the jth neuron in the hidden layer and the jth neuron in the output layer are increased by the gradient descent method. The specific calculation formula for the increase is:

[0028]

[0029]

[0030]

[0031]

[0032] Where μ represents the learning rate, represents the operator and y'1 represents the actual valued first derivative.

[0033] As a preferred solution of the steam heat release control method of modular molten salt energy storage of the present invention, the processing of the analyzed BP neural network model includes iterating the learning rate μ using a variable rate regulation technology, and outputting the BP neural network model of the molten salt pump frequency fy based on the iterative time value of the adjusted learning rate μ. The specific calculation formula of the variable rate regulation technology is:

[0034]

[0035] Among them, μ max represents the maximum learning rate, μ min represents the minimum learning rate, t max represents the maximum number of iterations, t represents the current number of iterations, and μ(t) represents the rate of change;

[0036] The steam heat release control includes substituting the number of nodes in the input layer into the BP neural network model of the output molten salt pump frequency fy, transmitting the output molten salt pump frequency fy to the molten salt pump inverter, and adjusting the heat exchange rate between molten salt and water vapor as the molten salt flow changes to complete the steam heat release control.

[0037] In the second aspect, an embodiment of the present invention provides a modular molten salt energy storage steam heat release control system, which includes: a training module, which establishes a BP neural network model of the modular molten salt energy storage heat release control system and trains the BP neural network model; an analysis module, which defines the output error after normalization and analyzes the BP neural network model according to the output error; a control module, which uses a variable rate regulation technology to process the analyzed BP neural network model, establishes a mathematical model of the actual object opposed to it, and completes the steam heat release control of the modular molten salt energy storage.

[0038] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, any step of the above-mentioned modular molten salt energy storage steam heat release control method is implemented.

[0039] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned method for controlling steam heat release of modular molten salt energy storage is implemented.

[0040] The beneficial effects of the present invention are as follows: the present invention effectively improves the steam heat release control accuracy and response speed of the modular molten salt energy storage system by introducing the BP neural network and variable rate regulation technology, ensures the stability and reliability of the control system by normalizing the node parameters and accurately analyzing the output error, and enables the neural network model to converge quickly by adjusting the learning rate and introducing the excitation function, thereby achieving fine control of the molten salt pump frequency, optimizing the heat exchange process between molten salt and water vapor, significantly improving the overall efficiency of the energy storage system, and reducing energy loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0042] Figure 1 A specific flow chart of a modular molten salt energy storage steam heat release control method and system provided for one embodiment of the present invention.

[0043] Figure 2 A system structure process flow chart of a modular molten salt energy storage system, which provides a steam heat release control method and system for modular molten salt energy storage according to an embodiment of the present invention.

[0044] Figure 3 A BP neural network structure diagram of a modular molten salt energy storage steam heat release control method and system provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0046] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0047] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0048] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0049] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0050] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0051] Example 1

[0052] Reference Figure 1 to Figure 3, which is the first embodiment of the present invention, and provides a steam heat release control method for modular molten salt energy storage, comprising:

[0053] S1: Establish a BP neural network model of the modular molten salt energy storage heat release control system, train the BP neural network model, and obtain the node parameters of the model.

[0054] Among them, the BP neural network model is established by extracting T according to the input N energy storage and heat release parameters. y1 、T y2 、T y3 and T y4 The medium temperature as molten salt;

[0055] The input includes inputting N energy storage heat release parameters through the input layer and outputting them through the hidden layer. The hidden layer includes obtaining the number of nodes of the hidden layer by using the number of nodes of the input layer. The number of nodes of the input layer includes x1~x7, where x1→fy, x2→Lso, x3→Tsin, x4→T y1 ,x5→T y2 ,x6→T y3 ,x7→T y4 , at this time, x4→T y1 represents the hidden layer, the number of nodes in the hidden layer is L = h, then the output formula of the jth neuron in the hidden layer is:

[0056]

[0057] Where i = 1, 2, 3...n, j = 1, 2, 3...L, wh ij represents the weight from the input layer to the hidden layer, a j represents the hidden layer node threshold, h j represents the jth neuron in the hidden layer.

[0058] Further, in the present invention, a BP neural network model of a modular molten salt energy storage heat release control system is established to accurately control the steam heat release process. The construction of the model is based on the input of N energy storage heat release parameters, which mainly include key indicators such as molten salt temperature, pressure and flow rate. For example, assuming that the input layer contains 12 nodes, corresponding to 12 key energy storage parameters, these parameters are input into the input layer and then processed by the hidden layer, wherein the number of nodes in the hidden layer is determined by matching the number of nodes in the input layer, assuming that the hidden layer contains 20 nodes, each hidden layer node outputs the result after being processed by the activation function according to the weight and threshold of the input layer node, and these output results are further used to extract the medium temperature of the molten salt to reflect the thermal state of the current system. Through such a neural network model, the dynamic behavior of the molten salt energy storage system can be accurately simulated, thereby laying the foundation for subsequent precise control. This method generates accurate output node parameters by processing a large amount of input data, provides efficient and real-time control capabilities for the system, and effectively improves the overall efficiency and response speed of energy storage heat release.

[0059] S1.1: Output through the hidden layer includes introducing a node activation function to preprocess the jth neuron of the hidden layer, speeding up the learning speed of the BP neural network model, and calculating the jth neuron of the output layer. The specific calculation formula is:

[0060]

[0061] Among them, y j represents the jth neuron in the output layer, wy j represents the weight from the output layer to the hidden layer, b1 represents the node threshold of the output layer, h j represents the jth neuron in the hidden layer;

[0062] A preliminary neural network model is established for the j-th neuron in the hidden layer and the j-th neuron in the output layer, and all node parameters of the input and output energy storage and heat release are obtained according to the steam heat release under different conditions.

[0063] Furthermore, in the present invention, in order to improve the learning efficiency of the BP neural network model, when outputting through the hidden layer, a node activation function is used to pre-process each neuron of the hidden layer. Specifically, assuming that the hidden layer contains 20 neurons, each neuron will be processed by the activation function, and the activation function plays a role in accelerating the convergence of the neural network. For example, when processing input data containing 1,000 data points, the activation function can quickly screen out parameters closely related to steam heat release, and pass these processed data to the output layer. The output layer may contain 5 neurons, and the connection weights between each neuron and the 20 neurons in the hidden layer are used to calculate the final output energy storage heat release parameters. Under different working conditions, such as high load or low load, the neurons in the output layer will generate different control signals to accurately adjust the steam heat release of the system. Through this preliminary neural network model, the system can analyze and obtain various input and output energy storage heat release parameters in real time, providing reliable data support for subsequent control decisions, thereby significantly improving the accuracy and efficiency of control.

[0064] S1.2: Training the BP neural network model includes setting the weights from the input layer to the hidden layer and the weights from the output layer to the hidden layer;

[0065] When the weight from the input layer to the hidden layer is greater than the hidden layer node threshold in the jth neuron, the BP neural network model enters the saturation zone. At this time, the BP neural network model converges, and the weight from the input layer to the hidden layer and the weight from the output layer to the hidden layer are sent to the saturation zone for setting. The weight from the input layer to the hidden layer and the weight from the output layer to the hidden layer are randomly taken as non-zero values.

[0066] In the training process of the present invention, in order to ensure the effectiveness of the BP neural network model, it is necessary to accurately set the weights between the input layer and the hidden layer, and between the hidden layer and the output layer. Assuming that the input layer has 12 nodes, the hidden layer has 20 nodes, and the output layer has 5 nodes, the connection weights between these nodes will be assigned non-zero values ​​according to the random value method in the initial stage. For example, the weights from the input layer to the hidden layer may be set to random values ​​between 0.01 and 0.1. As the training proceeds, when these weights gradually increase and exceed the node threshold of a neuron in the hidden layer, the network will enter the so-called saturation zone. In this area, the BP neural network model begins to converge, that is, gradually reduce the error and optimize the output effect of the network. The system will fix these weights in the saturation zone, and through further adjustment, ensure that the model can still effectively learn and adapt to new data input in the saturated state. In this way, the model can avoid falling into the dilemma of overtraining or undertraining, thereby ensuring the stability and accuracy of the system under different operating conditions, and effectively improving the accuracy of steam heat release control. The neural network weight setting data is shown in Table 1 below:

[0067] Table 1 Neural network weight setting data table

[0068] Node level Number of nodes Weight range Input Layer 12 - Hidden Layer 20 0.01 to 0.1 Output Layer 5 - Input layer to hidden layer 12×20 0.01 to 0.1 Hidden layer to output layer 20×5 -

[0069] The table shows the number of nodes in each layer of the BP neural network model and their weight settings. The input layer contains 12 nodes, the hidden layer has 20 nodes, and the output layer has 5 nodes. The table lists the weight range from the input layer to the hidden layer as 0.01 to 0.1, while the specific weight range from the hidden layer to the output layer is not clearly stated. Overall, the table summarizes the initial settings of the model weights and their configuration between different layers.

[0070] S2: Normalize the node parameters, define the output error after normalization, and analyze the BP neural network model based on the output error.

[0071] The normalization of node parameters includes unifying all node parameters using an excitation function. The specific normalization calculation formula of the excitation function is:

[0072]

[0073] Among them, x represents the normalized value, x r Represents the physical quantity of the operation, x max represents the maximum value in the activation function, x min Represents the minimum value in the activation function;

[0074] The specific formula for defining the output error after normalization is:

[0075]

[0076] in, represents the output value of the BP neural network model, y1 represents the actual value, and E represents the output error after normalization.

[0077] Furthermore, in the present invention, in order to improve the accuracy and stability of the BP neural network model, normalizing the node parameters is a key step. Specifically, when processing the steam heat release control of the molten salt energy storage system, it is necessary to uniformly process multiple input parameters, such as temperature, pressure, and flow rate. These parameters may differ greatly in physical magnitude. In order to avoid these differences from affecting the learning effect of the neural network, an excitation function is used to normalize these parameters. Assuming that the temperature range is 200°C to 500°C and the pressure range is 2MPa to 10MPa, the normalization process converts these parameters into a unified scale so that they fluctuate within the same range (such as 0 to 1).

[0078] After normalization, the system will further define the output error as an indicator to measure the performance of the neural network model. The accuracy of the model is determined by calculating the difference between the normalized output value and the actual value. For example, in a steam heat release operation, if the normalized output temperature is 0.8, and the normalized value corresponding to the actual value is 0.75, the output error will reflect this gap. Based on these error data, the system can analyze and adjust the BP neural network model to ensure that it can provide precise control under different working conditions. This process not only improves the robustness of the model, but also enhances the system's adaptability to complex dynamic environments, which helps to achieve more efficient energy storage heat release management.

[0079] S2.1: Analyzing the BP neural network model based on the output error includes setting the minimum error ∈;

[0080] When E<∈, the BP neural network model is established;

[0081] When E≥∈, the weights of the jth neuron in the hidden layer and the jth neuron in the output layer are increased by the gradient descent method. The specific calculation formula for the increase is:

[0082]

[0083]

[0084]

[0085]

[0086] Where μ represents the learning rate, represents the operator, y'1 represents the actual value first-order derivative

[0087] In the present invention, the analysis of the output error is an important step in the optimization of the BP neural network model. First, by setting a minimum error value, the system determines whether the model training has reached the ideal state. If the output error is less than the minimum error value, such as 0.001, it means that the model is accurate enough and the model training can be terminated. If the output error is greater than the minimum error value, it means that there is still a deviation in the model and further optimization is required. At this time, the system will use the gradient descent method to adjust the weights in the neural network.

[0088] In actual operation, assuming that the hidden layer contains 20 neurons and the output layer contains 5 neurons, the system will fine-tune the weight of each neuron to reduce the error. For example, in a calculation, if it is found that the weight of a hidden layer neuron is too high, resulting in a large output error, the gradient descent method will reduce the size of the weight through subtle adjustments, thereby reducing the overall error. Assuming that the learning rate is set to 0.01, this means that the amplitude of each adjustment is relatively small to ensure that the model gradually converges to the optimal state. After multiple iterations, the system can gradually reduce the error to the set range and finally complete the establishment of the BP neural network model. This sophisticated error analysis and weight adjustment method ensures the accuracy and reliability of the model and can provide efficient control in the complex energy storage and heat release process.

[0089] S3: The analyzed BP neural network model is processed by variable rate regulation technology. Based on the processed BP neural network model, a mathematical model of the actual object opposed to it is established to complete the steam heat release control of modular molten salt energy storage.

[0090] Among them, processing the analyzed BP neural network model includes iterating the learning rate μ by using the variable rate regulation technology, outputting the BP neural network model of the molten salt pump frequency fy based on adjusting the iteration time value of the learning rate μ, and the specific calculation formula of the variable rate regulation technology is:

[0091]

[0092] Among them, μ max represents the maximum learning rate, μ min represents the minimum learning rate, t max represents the maximum number of iterations, t represents the current number of iterations, and μ(t) represents the rate of change;

[0093] The steam heat release control includes substituting the number of nodes in the input layer into the BP neural network model of the output molten salt pump frequency fy, transmitting the output molten salt pump frequency fy to the molten salt pump inverter, and adjusting the heat exchange rate between molten salt and water vapor as the molten salt flow changes to complete the steam heat release control.

[0094] Preferably, in the present invention, variable rate regulation technology is used to optimize the learning rate of the BP neural network model. Initially, the system uses a larger learning rate, such as 0.1, to quickly converge the model; as the iteration proceeds, the learning rate gradually decreases, and may be reduced to 0.01 or lower for fine adjustment. After multiple iterations of optimization, the model calculates the molten salt pump frequency suitable for the current operating conditions, such as 1500RPM, and transmits this frequency to the molten salt pump inverter. The inverter controls the molten salt flow rate according to the adjusted frequency, thereby accurately adjusting the heat exchange rate between the molten salt and water vapor, and achieving efficient control of the steam heat release process. In this way, the system can maintain a high response speed and control accuracy in actual operation.

[0095] In a preferred embodiment, a modular molten salt energy storage steam heat release control system includes a training module, which establishes a BP neural network model of the modular molten salt energy storage heat release control system and trains the BP neural network model; an analysis module, which defines the output error after normalization and analyzes the BP neural network model based on the output error; a control module, which uses a variable rate regulation technology to process the analyzed BP neural network model, establishes a mathematical model of the actual object opposed to it, and completes the steam heat release control of the modular molten salt energy storage.

[0096] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0097] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0098] In summary, the present invention effectively improves the steam heat release control accuracy and response speed of the modular molten salt energy storage system by introducing BP neural network and variable rate regulation technology. The stability and reliability of the control system are ensured by normalizing the node parameters and accurately analyzing the output error. The neural network model can converge quickly by adjusting the learning rate and introducing the excitation function, thereby realizing fine control of the molten salt pump frequency, optimizing the heat exchange process between molten salt and water vapor, significantly improving the overall efficiency of the energy storage system, and reducing energy loss.

[0099] Example 2

[0100] Reference Figure 1 to Figure 3 , which is the second embodiment of the present invention, and this embodiment provides a steam heat release control method for modular molten salt energy storage. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0101] During the experiment, a BP neural network model of the modular molten salt energy storage heat release control system was first established. At the beginning of the experiment, the input layer was set with 15 nodes, the hidden layer contained 25 nodes, and the output layer had 6 nodes. In order to evaluate the performance of the model, the weights from the input layer to the hidden layer were initialized and set between 0.02 and 0.08, while the weights from the hidden layer to the output layer ranged from 0.05 to 0.15. In the training phase, 1,000 sets of experimental data were used, each of which included parameters such as molten salt temperature and flow rate. The model training adopted a variable rate adjustment technology. The initial learning rate was 0.05 and gradually decreased to 0.005. The training was iterated 5,000 times, and the weights were gradually adjusted to optimize the model performance.

[0102] During the test phase, new molten salt temperature data, such as 300°C and 320°C, and flow rate data, such as 0.8L / s and 1.2L / s, were input. The molten salt pump frequencies output by the model were 1800RPM and 1900RPM respectively. The frequency values ​​were transmitted to the molten salt pump inverter to adjust the heat exchange rate between molten salt and water vapor. Finally, the experimental results showed that the system could accurately adjust the steam heat release process, improve the control accuracy, and reduce the error range to within 5%, verifying the effectiveness of the model in practical applications. The experimental data are summarized in Table 2 below:

[0103] Table 2 Experimental data summary

[0104] Experimental content data Number of input layer nodes 15 Number of hidden layer nodes 25 Number of output layer nodes 6 Weight range from input layer to hidden layer 0.02 to 0.08 Weight range from hidden layer to output layer 0.05 to 0.15 Number of experimental data sets 1000 Initial learning rate 0.05 Final learning rate 0.005 Number of training iterations 5000 Temperature data 300℃,320℃ Flow rate data 0.8L / s,1.2L / s Output pump frequency 1800RPM,1900RPM Control accuracy error range 5％

[0105] The table summarizes the key data in the experimental process, including the number of nodes, weight range and specific parameters of training and testing of the BP neural network model. The table shows the number of nodes and weight range of the input layer, hidden layer and output layer, the number of data sets, learning rate and number of iterations in the training process, the temperature and flow rate data used in the test phase, and the pump frequency and control accuracy error range of the model output. The comparison between the technical solution of the present invention and the prior art is shown in Table 3 below:

[0106] Table 3 Comparison between the technical solution of the present invention and the prior art

[0107]

[0108] The table compares the main differences and advantages of the technical solution of the present invention with the prior art. The present invention adopts a BP neural network model, which can handle more complex nonlinear problems than traditional linear models, provide higher accuracy, more precise weight initialization, and adaptive adjustment of the learning rate, making the model training more stable and efficient. In terms of data processing, data consistency and model convergence are improved through normalization, control accuracy is significantly improved, the error range is reduced to 5%, and more data and training iterations are used to enhance the generalization ability of the model. Finally, the rapid adjustment capability of output feedback improves the timeliness of control response. Overall, the present invention is superior to the prior art in all aspects and provides a more accurate and stable control solution.

[0109] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for controlling steam heat release of modular molten salt energy storage, characterized in that: include, Establish a BP neural network model for the modular molten salt energy storage heat release control system, train the BP neural network model, and obtain the node parameters of the model; Normalize the node parameters, define the output error after normalization, and analyze the BP neural network model based on the output error; The variable rate regulation technology is used to process the analyzed BP neural network model. Based on the processed BP neural network model, a mathematical model of the actual object opposite to it is established to complete the steam heat release control of modular molten salt energy storage.

2. The steam heat release control method of modular molten salt energy storage according to claim 1, characterized in that: The BP neural network model is established by extracting T according to the input N energy storage and heat release parameters. y1 , T y2 , T y3 and T y4 The medium temperature as molten salt; The input includes inputting the N energy storage heat release parameters through the input layer and outputting them through the hidden layer. The hidden layer includes obtaining the number of nodes of the hidden layer by using the number of nodes of the input layer. The number of nodes of the input layer includes x1 to x7, where x1→fy, x2→Lso, x3→Tsin, x4→T y1 ,x5→T y2 ,x6→T y3 ,x7→T y4 , at this time, x4→T y1 represents a hidden layer, the number of nodes in the hidden layer is L=h, then the output formula of the jth neuron in the hidden layer is: Where i = 1, 2, 3...n, j = 1, 2, 3...L, wh ij represents the weight from the input layer to the hidden layer, a j represents the hidden layer node threshold, h j represents the jth neuron in the hidden layer.

3. The steam heat release control method of modular molten salt energy storage according to claim 2, characterized in that: The output through the hidden layer includes introducing a node activation function to pre-process the j-th neuron of the hidden layer, accelerating the learning speed of the BP neural network model, and calculating the j-th neuron of the output layer. The specific calculation formula is: Among them, y j represents the jth neuron in the output layer, wy j represents the weight from the output layer to the hidden layer, b1 represents the node threshold of the output layer, h j represents the jth neuron in the hidden layer; A preliminary neural network model is established for the j-th neuron in the hidden layer and the j-th neuron in the output layer, and all node parameters of the input and output energy storage and heat release are obtained according to the steam heat release under different conditions.

4. The steam heat release control method of modular molten salt energy storage according to claim 3, characterized in that: The training of the BP neural network model includes setting the weight from the input layer to the hidden layer and the weight from the output layer to the hidden layer; When the weight from the input layer to the hidden layer is greater than the hidden layer node threshold in the jth neuron, the BP neural network model enters the saturation zone. At this time, the BP neural network model converges, and the weight from the input layer to the hidden layer and the weight from the output layer to the hidden layer are sent to the saturation zone for setting, and the weight from the input layer to the hidden layer and the weight from the output layer to the hidden layer are randomly taken as non-zero values.

5. The steam heat release control method of modular molten salt energy storage according to claim 4, characterized in that: The normalization of the node parameters includes unifying all the node parameters using an excitation function, and the specific normalization calculation formula of the excitation function is: Among them, x represents the normalized value, x r Represents the physical quantity of the operation, x max represents the maximum value in the activation function, x min Represents the minimum value in the activation function; The specific formula for defining the output error after normalization is: in, represents the output value of the BP neural network model, y1 represents the actual value, and E represents the output error after normalization.

6. The steam heat release control method of modular molten salt energy storage according to claim 5, characterized in that: The analyzing the BP neural network model according to the output error includes setting a minimum error ∈; When E<∈, the BP neural network model is established; When E≥∈, the weights of the jth neuron in the hidden layer and the jth neuron in the output layer are increased by the gradient descent method. The specific calculation formula for the increase is: Where μ represents the learning rate, represents the operator and y'1 represents the actual valued first derivative.

7. The steam heat release control method of modular molten salt energy storage according to claim 6, characterized in that: The processing of the analyzed BP neural network model includes iterating the learning rate μ by using a variable rate regulation technology, and outputting the BP neural network model of the molten salt pump frequency fy based on the iterative time value of adjusting the learning rate μ. The specific calculation formula of the variable rate regulation technology is: Among them, μ max represents the maximum learning rate, μ min represents the minimum learning rate, t max represents the maximum number of iterations, t represents the current number of iterations, and μ(t) represents the rate of change; The steam heat release control includes substituting the number of nodes in the input layer into the BP neural network model of the output molten salt pump frequency fy, transmitting the output molten salt pump frequency fy to the molten salt pump inverter, and adjusting the heat exchange rate between molten salt and water vapor as the molten salt flow changes to complete the steam heat release control.

8. A modular molten salt energy storage steam heat release control system, based on the modular molten salt energy storage steam heat release control method according to any one of claims 1 to 7, characterized in that: include, A training module, which establishes a BP neural network model of a modular molten salt energy storage heat release control system and trains the BP neural network model; An analysis module, which defines the output error after normalization and analyzes the BP neural network model according to the output error; The control module uses variable rate regulation technology to process the analyzed BP neural network model, establishes a mathematical model of the actual object opposite to it, and completes the steam heat release control of the modular molten salt energy storage.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the steam heat release control method for modular molten salt energy storage according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the steam heat release control method for modular molten salt energy storage according to any one of claims 1 to 7 are implemented.

Images