PID (Proportion Integration Differentiation) parameter setting method and device of steam generator liquid level control system

Abstract

The invention discloses a PID (Proportion Integration Differentiation) parameter setting method and device for a liquid level control system of a steam generator. The method comprises the following steps: acquiring running data of the steam generator; constructing and training a neural network identification model based on the steam generator operation data; and optimizing PID parameters of the steam generator liquid level control system based on the trained neural network identification model and a parameter optimization algorithm. The device comprises an acquisition module used for acquiring operation data of the steam generator; the building module is used for building and training a neural network identification model based on the steam generator operation data; and the optimization module is used for optimizing PID parameters of the steam generator liquid level control system based on the trained neural network identification model and a parameter optimization algorithm. According to the method and device, PID parameters with good control effects under different working conditions can be obtained, the production efficiency and safety of the nuclear power station can be improved, and compared with an original steam generator liquid level control system, the overshoot is small, and the adjusting efficiency is higher.

Description

Technical Field

[0001] The present invention relates to the technical field of nuclear reactor control, and particularly to a method and device for tuning PID parameters of a steam generator liquid level control system. Background Art

[0002] The pressurized water reactor is one of the most widely used nuclear power plant reactor types in the world. The steam generator (SG) is a heat exchange device connecting the primary and secondary loops of a pressurized water reactor nuclear power plant and is also an important nuclear safety barrier. Its main function is to transfer the heat carried away by the primary coolant from the reactor core to the desalted water in the secondary loop through the SG tube wall, causing it to generate steam and drive the steam turbine to do work. During operation, the water level in the steam generator needs to always be at a safe position near the programmed set value. If the water level is too low, the U-shaped heat exchange tubes may be partially exposed to the steam, resulting in deteriorated heat transfer and thermal shock to the tube sheet, and may also cause steam to enter the feed water ring, generating dangerous water hammers. If the water level is too high, there is a risk of submerging the herringbone dryer, causing the steam humidity to be too high and endangering the steam turbine blades. The height of the SG liquid level directly affects the quality of the outlet steam and the safety of the equipment. Therefore, it is of great significance to improve the control effect of the liquid level control system. There are roughly five influencing factors affecting the steam generator liquid level: steam flow rate, feed water flow rate, average primary loop temperature, feed water temperature, and turbine load. In most cases of influencing factors, a "false liquid level" will be generated in the steam generator liquid level, causing liquid level fluctuations and bringing difficulties to liquid level control. The primary loop temperature can be kept constant by the reactor temperature control rod group; the feed water is heated by a high-pressure feed water heater, and the outlet temperature is also basically stable. Therefore, the influence of the feed water temperature on the liquid level can be equivalently considered as the influence of the secondary loop load. The steam flow rate is proportional to the secondary loop load, so the influence of the secondary loop load can be equivalently considered as the influence of the steam flow rate. Based on the above analysis, it can be determined that the most important factors affecting the evaporator liquid level are the change in the feed water flow rate and the disturbance of the steam flow rate, which will also be the main factors considered in the steam generator mathematical model and liquid level control scheme.

[0003] The existing patent CN110879620A discloses a method and system for controlling the liquid level of a vertical steam generator in a nuclear power plant. The method includes the steps of: S1, establishing a liquid level set value curve of the steam generator and obtaining reference values of fractional-order PID controller parameters under multiple typical power loads; S2, obtaining the current power load of the power plant, calculating the set value of the fractional-order PID controller parameters under the current power load, and obtaining the liquid level set value under the current power load according to the liquid level set value curve; S3, controlling the feed water flow rate of the steam generator according to the tuned fractional-order PID controller; S4, obtaining the real liquid level of the steam generator, and judging whether the difference between the real liquid level and the liquid level set value is within the allowable range; if not, updating the current power load of the power plant and returning to step S2 until the difference is within the allowable range.

[0004] The existing patent CN108983607A discloses a tuning method for the liquid level control system of a pressurized water reactor steam generator. The main means is to equivalently transform the liquid level control system and then tune it according to the cascade control loop. First, according to the method of equivalent transformation of the block diagram, the liquid level control system of the pressurized water reactor steam generator is equivalently transformed into a cascade triple-impulse control loop. Then, based on the internal model control design method, the equivalent cascade control loop is tuned. Finally, the parameters of the original liquid level control system are obtained according to the equivalent transformation.

[0005] In summary, neither of the above two existing patents solves the problems that traditional PID control is difficult to achieve good control effects for nonlinear and multivariable systems such as steam generators, the control parameters are fixed, and the adaptability is poor. Summary of the Invention

[0006] Based on the above technical problems, the present invention proposes a PID parameter tuning method and device for a steam generator liquid level control system, which solves the problems that traditional PID control in the prior art is difficult to achieve good control effects for nonlinear and multivariable systems such as steam generators, the control parameters are fixed, and the adaptability is poor.

[0007] A PID parameter tuning method for a steam generator liquid level control system includes:

[0008] Obtain the operation data of the steam generator;

[0009] Construct and train a neural network identification model based on the operation data of the steam generator;

[0010] Optimize the PID parameters of the steam generator liquid level control system based on the trained neural network identification model and the parameter optimization algorithm.

[0011] Further, before obtaining the operation data of the steam generator, it further includes:

[0012] Build a steam generator liquid level control system.

[0013] Further, obtaining the operation data of the steam generator includes:

[0014] Apply a disturbance to the steam generator liquid level control system and obtain the corresponding operation data of the steam generator. The operation data of the steam generator includes the feed water flow rate and the corresponding liquid level value.

[0015] Further, constructing and training a neural network identification model based on the operation data of the steam generator includes:

[0016] Determine the training set and the test set based on the operation data of the steam generator;

[0017] Establish a BP neural network identification model;

[0018] Train the BP neural network identification model using the training set;

[0019] Evaluate the effect of the trained BP neural network identification model using the test set;

[0020] If the evaluation result does not meet the preset result requirements, adjust the network structure of the BP neural network identification model.

[0021] Furthermore, it also includes: verifying the effect of the trained neural network identification model.

[0022] Furthermore, based on the trained neural network identification model and the parameter optimization algorithm, optimize the PID parameters of the steam generator liquid level control system, including:

[0023] Predict the liquid level value of the steam generator based on the trained neural network identification model;

[0024] Based on the predicted liquid level value, use the parameter optimization algorithm to optimize the PID parameters of the steam generator liquid level control system. The PID parameters include the first proportional gain, the first integral time, and the differential time in the liquid level controller, and the second proportional gain and the second integral time in the flow controller.

[0025] Furthermore, the parameter optimization algorithm includes: any one of the genetic algorithm, the improved genetic algorithm NSGA-II, and the ant colony optimization algorithm.

[0026] Furthermore, it also includes: setting the optimization objective and parameters of the parameter optimization algorithm. The optimization objective is the overshoot and the adjustment time, and the population size in the parameters is 30, and the number of iterations is 100.

[0027] Furthermore, it also includes:

[0028] Input the optimized PID parameters into the steam generator liquid level control system, and determine the overshoot and the adjustment time corresponding to the optimized PID parameters;

[0029] Verify the control effect of the optimized PID parameters based on the overshoot and the adjustment time.

[0030] A device for tuning the PID parameters of a steam generator liquid level control system, including:

[0031] An acquisition module for acquiring the operation data of the steam generator;

[0032] A building module for constructing and training a neural network identification model based on the operation data of the steam generator;

[0033] An optimization module, configured to optimize the PID parameters of the steam generator liquid level control system based on a trained neural network identification model and a parameter optimization algorithm.

[0034] A computer-readable storage medium, the computer-readable storage medium includes a stored computer program, wherein the computer program can execute the above method when being run by an electronic device.

[0035] A computer program product, including a computer program, which implements the steps of the above method when being executed by a processor.

[0036] An electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the above method through the computer program.

[0037] Based on the above technical solutions, the present invention has at least the following beneficial effects:

[0038] 1. The present invention proposes to construct and train a neural network identification model by using the operation data of the steam generator, and then optimize the PID parameters of the steam generator liquid level control system based on the trained neural network identification model and a parameter optimization algorithm. This method can obtain PID parameters with good control effects under different working conditions, and has a smaller overshoot and higher adjustment efficiency compared with the original steam generator liquid level control system.

[0039] 2. The present invention proposes to apply different disturbances to the steam generator liquid level control system model of the nuclear power plant to extract and obtain the operation data of the steam generator as the original data, and then use a BP neural network to construct an identification model. Based on the trained BP neural network identification model, set the objectives and objective functions to be optimized through a genetic algorithm, and perform corresponding PID parameter tuning. This method can achieve accurate identification of the steam generator model during the variable working condition process, obtain the corresponding transfer function, and then perform automatic PID parameter tuning through an ant colony optimization algorithm or a genetic optimization algorithm, showing good control effects in various complex variable working conditions, being able to reduce the burden on operators, improve the production efficiency of the nuclear power plant, and enhance the safety during the operation of the nuclear power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The specification drawings forming a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0041] Figure 1 It is a flowchart of a method for tuning the PID parameters of a steam generator liquid level control system according to an embodiment of the present invention;

[0042] Figure 2Schematic diagram of the steam generator liquid level control system built using Simulink in an embodiment of the present invention;

[0043] Figure 3 Schematic diagram of the neural network structure in an embodiment of the present invention;

[0044] Figure 4 Schematic diagram of the random square wave disturbance of the feed water flow rate lasting for 300 s in an embodiment of the present invention;

[0045] Figure 5 Identification result graph of the BP neural network identification model in an embodiment of the present invention;

[0046] Figure 6 Flow chart of optimizing using the improved genetic algorithm NSGA-II in an embodiment of the present invention;

[0047] Figure 7 Schematic diagram of the optimal solution set after optimizing using the improved genetic algorithm NSGA-II in an embodiment of the present invention;

[0048] Figure 8 Comparison graph between the simulation result of the optimal solution point A under the step-down disturbance of the liquid level set value at 100% power level and the original control system;

[0049] Figure 9 Comparison graph between the simulation result of the optimal solution point B under the step-down disturbance of the liquid level set value at 100% power level and the original control system;

[0050] Figure 10 Schematic diagram of the PID parameter tuning device for a steam generator liquid level control system in an embodiment of the present invention;

[0051] Figure 11 Block diagram of the computer system of the electronic device for implementing the embodiments of the present application;

[0052] Figure 12 Schematic diagram of an electronic device for PID parameter tuning of a steam generator liquid level control system. Detailed implementation manners

[0053] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0054] The following further describes the present invention in detail with specific embodiments, and these embodiments should not be construed as limiting the scope claimed by the present invention.

[0055] Embodiment

[0056] To solve the problems in the prior art that traditional PID control is difficult to achieve good control effects for non - linear and multi - variable systems such as steam generators, with fixed control parameters and poor self - adaptability, the present invention proposes a method and device for tuning PID parameters of a steam generator liquid level control system.

[0057] According to one aspect of the embodiments of the present application, a method for tuning PID parameters of a steam generator liquid level control system is provided.

[0058] As Figure 1 shown in the flowchart of the method for tuning PID parameters of a steam generator liquid level control system according to an embodiment of the present invention, the above - mentioned method includes the following steps:

[0059] S1, obtain the operation data of the steam generator.

[0060] Further, before obtaining the operation data of the steam generator, complete the construction of the steam generator liquid level control system. As Figure 2 is a schematic diagram of the steam generator liquid level control system built using simulink in this embodiment.

[0061] Further, obtaining the operation data of the steam generator includes: applying a disturbance to the steam generator liquid level control system and obtaining the corresponding operation data of the steam generator. The operation data of the steam generator includes the feed water flow rate and the corresponding liquid level value. The data set is mainly constructed by Figure 2 applying a disturbance to the system shown to obtain different feed water flow rates and the corresponding liquid level values. The object of applying the disturbance can be the liquid level set value or the feed water flow rate. In this embodiment, the sampling time is 0.5 s, and the feed water flow rate disturbance and the corresponding liquid level change lasting for 300 s are taken as a set of data. Multiple sets of data are formed by applying different forms of disturbances to jointly constitute a data set for subsequent neural network training.

[0062] S2, build and train a neural network identification model based on the operation data of the steam generator.

[0063] In this embodiment, a BP neural network is used to establish the corresponding neural network identification model. It should be understood that in different embodiments, different neural networks can be used to establish the corresponding identification models. Further, the process of building and training a neural network identification model based on the operation data of the steam generator is as follows:

[0064] S201, determine the training set and the test set based on the operation data of the steam generator.

[0065] Use the operation data of the steam generator obtained in step S1 to construct a data set. In this embodiment, the training set accounts for 70% and the test set accounts for 30%.

[0066] S202, establish a BP neural network identification model.

[0067] Select a network identification structure with a construction step size of n = 3 and d = 1. The specific structure is: y[k] = F(y[k - 3], y[k - 2], y[k - 1], u[k - 2], u[k - 1], u[k]), and construct a neural network structure as shown in Figure 3 Figure. Among them, the number of neurons in the input layer of the network is 6, the number of neurons in the hidden layer is 30, and the activation function of the neurons in the output layer takes the tanh function.

[0068] S203, use the training set to train the BP neural network identification model.

[0069] Train the BP neural network. In this embodiment, the number of training iterations is set to 1000 times.

[0070] S204, use the test set to evaluate the effect of the trained BP neural network identification model.

[0071] S205, if the model effect does not meet the preset effect requirements, adjust the network structure of the BP neural network identification model.

[0072] In another embodiment, after using the test set to evaluate the effect of the trained BP neural network identification model, the effect of the trained neural network identification model is also verified. Specifically, the simulink-built steam generator liquid level control system is used to verify the effect of the trained neural network identification model.

[0073] S3, based on the trained neural network identification model and the parameter optimization algorithm, optimize the PID parameters of the steam generator liquid level control system.

[0074] Furthermore, based on the trained neural network identification model and the parameter optimization algorithm, optimizing the PID parameters of the steam generator liquid level control system includes the following two steps: S301, predict the liquid level value of the steam generator based on the trained neural network identification model; S302, based on the predicted liquid level value, use the parameter optimization algorithm to optimize the PID parameters of the steam generator liquid level control system.

[0075] The parameter optimization algorithm includes any one of the genetic algorithm, the improved genetic algorithm NSGA-II, and the ant colony optimization algorithm. Furthermore, before using the parameter optimization algorithm to optimize the PID parameters of the steam generator liquid level control system, it also includes: setting the optimization objective and parameters of the parameter optimization algorithm, and the optimization objectives are overshoot and settling time.

[0076] In another embodiment of the present invention, it further includes: inputting the optimized PID parameters into the steam generator liquid level control system to determine the overshoot and adjustment time corresponding to the optimized PID parameters; verifying the control effect of the optimized PID parameters based on the overshoot and adjustment time. The PID parameters include the first proportional gain, the first integral time, and the differential time in the liquid level controller, and the second proportional gain and the second integral time in the flow controller.

[0077] This embodiment uses the improved genetic algorithm NSGA-II to solve the multi-objective optimization problem, optimize the PID control parameters of the steam generator liquid level control system, and simulate and verify the optimized PID parameters in the Matlab / Simulink model. To verify the effectiveness of the proposed method, first use Figure 2 the simulink model in to generate simulation data for system identification. Subsequently, the variable data of the liquid level under continuous disturbance of the feed water flow is extracted, and the sampling time is set to 0.5 s. In the finally obtained data set, the input data dimension is [15000, 6], the output data dimension is [15000, 1], the number of training times is set to 1000 times, the initial learning rate is 0.01, and the neural network model training is started. After the training is completed, the BP neural network identification model is tested, and the test signal is as Figure 4 a random square wave disturbance of the feed water flow lasting for 300 s. The identification result of the BP neural network identification model is as Figure 5 shown. It can be seen from the identification effect that under the same feed water flow disturbance, the actual liquid level value output by the steam generator liquid level control system and the predicted value output by the BP neural network identification model have a high degree of fitting, thus proving the feasibility of using the BP neural network to identify the steam generator liquid level control system.

[0078] Subsequently, the BP neural network identification model is used for PID parameter tuning. The steam generator liquid level control system in this embodiment adopts cascade PID control. Therefore, the controllers to be tuned are Kp1, Ki1, Kd1 of a PID controller and Kp2 and Ki2 in a PI controller. The parameter ranges are determined according to experience as Kp1 is [0, 20], Ki1 is [0, 1], Kd1 is [0, 1], Kp2 is [0, 1], and Ki2 is [0, 1]. The overshoot and adjustment time are selected as the optimization objectives, two objective functions F1 and F2 are constructed, the population size is set to 30, and the genetic algorithm optimization starts with 100 iterations. Figure 6It is the optimization flowchart of the improved genetic algorithm NSGA-II. The process starts with initializing the population and performing fast non-dominated sorting on the population to evaluate the quality of individuals. Then, the virtual fitness is calculated, and crossover and mutation operations are selected to generate a new offspring population. Subsequently, the elite retention strategy is adopted to merge the new and old populations, and it is checked whether the maximum number of generations of evolution has been reached. If not, it returns to continue the steps of selecting crossover and mutation. If so, the algorithm ends and outputs the final optimization result. Figure 7 It is the optimal solution set (Pareto front) after optimization of the improved genetic algorithm NSGA-II. In this embodiment, two points A and B on the Pareto front are selected for simulation tests. As Figure 8 and Figure 9 It is a comparison chart of the simulation results of points A and B under the step-down disturbance of the liquid level set value at 100% power level and the original control system (steam generator liquid level control system). Among them, Figure 8 the curve C in represents the PID-tuned liquid level change curve of the original control system, the curve D is the simulation result curve based on the PID parameters of point A, and the curve E represents the simulation result curve based on the PID parameters of point B. The original PID parameters and the tuned PID parameters are shown in Table 1:

[0079] Table 1 Initial PID parameters and tuned PID parameters corresponding to points A and B

[0080]

[0081] The two sets of tuned and optimized PID parameters are respectively input into the steam generator liquid level control system to determine the overshoot and adjustment time corresponding to the tuned and optimized PID parameters, and compare with the tuning effect of the steam generator liquid level control system. Among them, the overshoot of point A is 0.0032, and the adjustment time is 35.1059 s. The overshoot of point B is 0.04, and the adjustment time is 18.01 s. After comparison, although the control parameter effect of point B has a shorter adjustment time, the overshoot has increased compared with the original system. The overshoot and adjustment time of point A have both improved compared with the original system. It can be seen from the experimental results that the optimized control effects can all be improved in one or more indicators, which is better than the original control system, thus indicating the effectiveness of the steam generator liquid level control system parameter optimization method based on NSGA-II.

[0082] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0083] According to another aspect of the embodiments of the present application, the present invention further provides a device for tuning PID parameters of a steam generator liquid level control system.

[0084] As Figure 10 shown in the schematic diagram of a device for tuning PID parameters of a steam generator liquid level control system according to an embodiment of the present invention. The device includes: an acquisition module 901, a construction module 902, and an optimization module 903.

[0085] The acquisition module 901 is configured to acquire the operation data of the steam generator.

[0086] The construction module 902 is configured to construct and train a neural network identification model based on the operation data of the steam generator.

[0087] The optimization module 903 is configured to optimize the PID parameters of the steam generator liquid level control system based on the trained neural network identification model and a parameter optimization algorithm.

[0088] As an optional solution, the above device is further configured to: build a steam generator liquid level control system before acquiring the operation data of the steam generator.

[0089] As an optional solution, the above device is further configured to: apply a disturbance to the steam generator liquid level control system, and acquire the corresponding operation data of the steam generator, where the operation data of the steam generator includes the feed water flow rate and the corresponding liquid level value.

[0090] As an optional solution, the above device is further configured to:

[0091] Determine a training set and a test set based on the operation data of the steam generator;

[0092] Build a BP neural network identification model;

[0093] Train the BP neural network identification model using the training set;

[0094] Evaluate the effect of the trained BP neural network identification model using the test set;

[0095] If the evaluation result does not meet the preset result requirements, adjust the network structure of the BP neural network identification model.

[0096] As an optional solution, the above device is further configured to: verify the effect of the trained neural network identification model.

[0097] As an optional solution, the above device is further configured to:

[0098] Predict the liquid level value of the steam generator based on the trained neural network identification model;

[0099] Based on the predicted liquid level value, use a parameter optimization algorithm to optimize the PID parameters of the steam generator liquid level control system. The PID parameters include the first proportional gain, the first integral time, and the differential time in the liquid level controller, and the second proportional gain and the second integral time in the flow controller.

[0100] As an alternative solution, the above device is also used for a parameter optimization algorithm, including: any one of a genetic algorithm, an improved genetic algorithm NSGA-II, and an ant colony optimization algorithm.

[0101] As an alternative solution, the above device is also used for: setting the optimization objective and parameters of the parameter optimization algorithm. The optimization objective is the overshoot and the adjustment time. The population size in the parameters is 30, and the number of iterations is 100.

[0102] As an alternative solution, the above device is also used for:

[0103] Input the optimized PID parameters into the steam generator liquid level control system, and determine the overshoot and adjustment time corresponding to the optimized PID parameters;

[0104] Verify the control effect of the optimized PID parameters based on the overshoot and the adjustment time.

[0105] In the embodiments of the present application, the term "module" or "unit" refers to a computer program with a predetermined function or a part of a computer program, which works together with other related parts to achieve a predetermined goal, and can be fully or partially implemented by using software, hardware (such as a processing circuit or a memory), or a combination thereof. Similarly, one processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be a part of an overall module or unit that includes the function of that module or unit.

[0106] Regarding the device in the above embodiments, the specific manner in which each module performs operations has been described in detail in the embodiments related to the method, and will not be elaborated here.

[0107] According to one aspect of the present application, a computer program product is provided, and the computer program product includes a computer program.

[0108] The serial numbers of the embodiments of the present application above are only for description and do not represent the advantages or disadvantages of the embodiments.

[0109] Figure 11 Schematically shows a block diagram of a computer system of an electronic device for implementing the embodiments of the present application.

[0110] It should be noted that Figure 11 the computer system 1100 of the illustrated electronic device is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0111] As Figure 11 shown, the computer system 1100 includes a central processing unit 1101 (CPU), which can perform various appropriate actions and processes according to the program stored in the read-only memory 1102 (ROM) or the program loaded from the storage section 1108 into the random access memory 1103 (RAM). In the random access memory 1103, various programs and data required for system operation are also stored. The central processing unit 1101, the read-only memory 1102, and the random access memory 1103 are connected to each other via a bus 1104. The input / output interface 1105 (Input / Output interface, i.e., I / O interface) is also connected to the bus 1104.

[0112] The following components are connected to the input / output interface 1105: an input section 1106 including a keyboard, a mouse, etc.; an output section 1107 including such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and speakers, etc.; a storage section 1108 including a hard disk, etc.; and a communication section 1109 including a network interface card such as a local area network card, a modem, etc. The communication section 1109 performs communication processing via a network such as the Internet. A drive 1110 is also connected to the input / output interface 1105 as required. A removable medium 1111, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1110 as required so that the computer program read from it can be installed into the storage section 1108 as required.

[0113] Specifically, according to the embodiments of the present application, the processes described in each method flowchart can be implemented as computer software programs. For example, the embodiments of the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from the network through the communication section 1109, and / or installed from the removable medium 1111. When the computer program is executed by the central processing unit 1101, various functions defined in the system of the present application are executed.

[0114] In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 1109, and / or installed from the removable medium 1111. When the computer program is executed by the central processing unit 1101, various functions provided by the embodiments of the present application are executed.

[0115] According to another aspect of the embodiments of the present application, an electronic device for tuning PID parameters of a steam generator liquid level control system is further provided. In this embodiment, the electronic device is taken as an example of a terminal device for illustration. As Figure 12 shown, the electronic device includes a memory 1202 and a processor 1204. A computer program is stored in the memory 1202, and the processor 1204 is configured to execute the steps in any of the above method embodiments through the computer program.

[0116] Optionally, in this embodiment, the above electronic device may be at least one network device among multiple network devices of a computer network.

[0117] Optionally, in this embodiment, the above processor may be configured to execute the methods in the embodiments of the present application through a computer program.

[0118] Optionally, those of ordinary skill in the art can understand that Figure 12 the structure shown is only schematic, Figure 12 and it does not limit the structure of the above electronic device. For example, the electronic device may further include more or fewer components (such as a network interface, etc.) than those shown, or have a different configuration from that shown. Figure 12 shown, or have a different configuration from that shown. Figure 12 shown.

[0119] Among them, the memory 1202 can be used to store software programs and modules, such as program instructions / modules corresponding to a method and device for tuning PID parameters of a steam generator liquid level control system in the embodiments of the present application. The processor 1204 executes various functional applications and data processing by running the software programs and modules stored in the memory 1202, that is, implements the above method for tuning PID parameters of a steam generator liquid level control system. The memory 1202 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory 1202 may further include a memory remotely disposed relative to the processor 1204, and these remote memories can be connected to the terminal through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof. Among them, the memory 1202 may specifically but not limitedly be used to store steam generator operation data information. As an example, as Figure 12As shown, the above-mentioned memory 1202 may but is not limited to include the acquisition module 901, the establishment module 902, and the optimization module 903 in the above-mentioned PID parameter tuning device of a steam generator liquid level control system. In addition, it may also include but is not limited to other module units in the above-mentioned device, which will not be elaborated in this example.

[0120] Optionally, the above-mentioned transmission device 1206 is used to receive or send data via a network. Specific examples of the above-mentioned network may include a wired network and a wireless network. In one example, the transmission device 1206 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices and routers through a network cable so as to communicate with the Internet or a local area network. In one example, the transmission device 1206 is a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0121] In addition, the above-mentioned electronic device further includes: a display 1208 for displaying the above-mentioned steam generator operation data; and a connection bus 1210 for connecting each module component in the above-mentioned electronic device.

[0122] In other embodiments, the above-mentioned terminal device or server may be a node in a distributed system. Among them, the distributed system may be a blockchain system, and the blockchain system may be a distributed system formed by connecting the multiple nodes in a form of network communication. Among them, the nodes can form a peer-to-peer network, and any form of computing device, such as an electronic device such as a server or a terminal, can become a node in the blockchain system by joining the peer-to-peer network.

[0123] According to one aspect of the present application, there is provided a computer-readable storage medium. The processor of the electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device executes a PID parameter tuning method for a steam generator liquid level control system provided in various optional implementation manners of the above-mentioned aspect.

[0124] Optionally, in this embodiment, the above-mentioned computer-readable storage medium may be set to store instructions for executing the methods in the various embodiments of the present application.

[0125] Optionally, in this embodiment, those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the relevant hardware of the terminal device through a program. The program can be stored in a computer-readable storage medium, and the storage medium can include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, an optical disk, etc.

[0126] The serial numbers of the embodiments of the present application above are only for description and do not represent the superiority or inferiority of the embodiments.

[0127] If the integrated unit in the above embodiments is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in the above computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing one or more electronic devices to execute all or part of the steps of the methods described in the various embodiments of the present application.

[0128] In the above embodiments of the present application, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0129] In the several embodiments provided by the present application, it should be understood that the disclosed application program can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some interfaces, and the indirect coupling or communication connection of units or modules can be in an electrical or other form.

[0130] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0131] In addition, in each embodiment of the present application, each functional unit may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0132] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0133] In summary, as can be seen from the above description, the above embodiments of the present invention achieve the following technical effects:

[0134] 1. The present invention proposes to construct and train a neural network identification model using the operation data of the steam generator, and then optimize the PID parameters of the steam generator liquid level control system based on the trained neural network identification model and the parameter optimization algorithm. This method can obtain PID parameters with good control effects under different working conditions, and has a smaller overshoot and higher adjustment efficiency compared with the original steam generator liquid level control system.

[0135] 2. The present invention proposes to apply different disturbances to the steam generator liquid level control system model of the nuclear power plant to extract and obtain the operation data of the steam generator as the original data, and then use a BP neural network to construct an identification model. Based on the trained BP neural network identification model, set the optimization objectives and objective functions to be optimized through the genetic algorithm, and perform corresponding PID parameter tuning. This method can achieve accurate identification of the steam generator model during the variable working condition process, obtain the corresponding transfer function, and then perform automatic PID parameter tuning through the ant colony optimization algorithm or the genetic optimization algorithm, showing good control effects in various complex variable working conditions, being able to reduce the burden on operators, improve the production efficiency of the nuclear power plant, and enhance the safety during the process of the nuclear power plant.

[0136] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0137] It should be noted that in the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

Claims

1. A PID parameter setting method for a steam generator liquid level control system, characterized in that: include: Obtain steam generator operation data; Building and training a neural network identification model based on the steam generator operation data; Based on the trained neural network identification model and parameter optimization algorithm, the PID parameters of the steam generator level control system are optimized.

2. The method according to claim 1, characterized in that Before obtaining steam generator operation data, it also includes: Build a steam generator level control system.

3. The method according to claim 1, characterized in that Obtain steam generator operating data, including: A disturbance is applied to the steam generator liquid level control system to obtain corresponding steam generator operation data, wherein the steam generator operation data includes a feed water flow rate and a corresponding liquid level value.

4. The method according to claim 3, characterized in that Building and training a neural network identification model based on the steam generator operation data includes: Determine a training set and a test set based on the steam generator operation data; Establish BP neural network identification model; Using the training set to train the BP neural network identification model; Use the test set to evaluate the effect of the trained BP neural network recognition model; If the evaluation result does not meet the preset result requirement, the network structure of the BP neural network identification model is adjusted.

5. The method according to claim 4, characterized in that Also includes: The effect of the trained neural network recognition model is verified.

6. The method according to claim 1, characterized in that Based on the trained neural network identification model and parameter optimization algorithm, the PID parameters of the steam generator liquid level control system are optimized, including: Predicting the liquid level value of the steam generator based on the trained neural network identification model; Based on the predicted liquid level value, the parameter optimization algorithm is used to optimize the PID parameters of the steam generator liquid level control system, wherein the PID parameters include a first proportional gain, a first integral time and a differential time in the liquid level controller, and a second proportional gain and a second integral time in the flow controller.

7. The method according to claim 6, characterized in that The parameter optimization algorithm comprises: Any one of the genetic algorithm, improved genetic algorithm NSGA-Ⅱ and ant colony optimization algorithm.

8. The method according to claim 6, characterized in that Also includes: The optimization target and parameters of the parameter optimization algorithm are set. The optimization target is the overshoot and the adjustment time. The population size in the parameters is 30 and the number of iterations is 100.

9. The method according to claim 6, characterized in that Also includes: Inputting the optimized PID parameters into the steam generator liquid level control system to determine the overshoot and adjustment time corresponding to the optimized PID parameters; The control effect of the optimized PID parameters is verified based on the overshoot and the adjustment time.

10. A PID parameter setting device for a steam generator liquid level control system, characterized in that: include: An acquisition module, used for acquiring steam generator operation data; Establishing a module for constructing and training a neural network identification model based on the steam generator operation data; The optimization module is used to optimize the PID parameters of the steam generator liquid level control system based on the trained neural network identification model and parameter optimization algorithm.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein the computer program can be executed by an electronic device to perform the method described in any one of claims 1 to 9.

12. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 9 are implemented.

13. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 9 through the computer program.

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