A multi-module high-temperature gas-cooled reactor power coordination control method and device

Abstract

The application discloses a kind of multi-module high temperature gas cooled reactor power coordination control method and device, belong to high temperature reactor control field.Layered three-level collaborative control framework is constructed by the application, and the system is decomposed into module coordination control, inter-module intelligent decoupling control and load coordination control level;For the strong coupling characteristics in module, the feedforward cascade control structure is used to design the control strategy of nuclear steam supply system, and the linear modeling and setting of key parameters are completed;Based on fuzzy logic and neural network, a multi-dimensional controller is constructed, and a decoupling rule is formulated in a primary and secondary input combination manner to achieve dynamic characteristic decoupling between modules;Combined with load prediction and equipment state evolution law, a power distribution strategy is designed to dynamically adjust the thermal power command of each module, achieving global optimization and coordination between units and loads.The application improves the control accuracy and operational stability of the multi-module high temperature gas cooled reactor system.

Description

Technical Field

[0001] This invention relates to the field of high-temperature reactor control, and in particular to a method, apparatus, equipment and storage medium for coordinated power control of a multi-module high-temperature gas-cooled reactor. Background Technology

[0002] In multi-module high-temperature gas-cooled reactor systems, multiple reactor modules typically operate coupled through a shared steam-to-electricity conversion system to achieve higher power generation capacity and operational flexibility. However, during dynamic operation, this multi-module integrated arrangement results in significant thermo-hydraulic coupling and dynamic interactions between the reactor modules. Specifically, changes in the operating status of one reactor module—such as power regulation, temperature fluctuations, or coolant flow adjustments—can be transmitted to other modules via the shared secondary steam system, causing a chain reaction of deviations in key parameters (such as main steam pressure, temperature, and reactor power), potentially leading to severe deviations from the design safety range. This strong coupling effect between modules not only makes the overall dynamic characteristics of the system more complex but also poses a serious challenge to traditional control strategies based on single-module designs, significantly increasing the difficulty of operation and control and raising safety risks.

[0003] Therefore, research on power coordination control strategies for multi-module high-temperature gas-cooled reactors is of great theoretical and engineering significance for ensuring the safe and stable operation of reactors under various operating conditions, promoting the development of nuclear power plants towards an intensive configuration mode of "multi-module with one unit", improving the overall economy and reliability of the system, and promoting the large-scale application of high-temperature gas-cooled reactors in a wider range of scenarios such as combined heat and power and multi-energy complementarity. Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] To address this, this invention proposes a multi-module high-temperature gas-cooled reactor power coordination control method. By constructing a hierarchical three-level cooperative control architecture, it achieves intra-module coordinated control, inter-module intelligent decoupling control, and global load coordinated control, respectively. An intra-module control system is designed using a feedforward-cascade structure, and dynamic decoupling between modules is achieved based on fuzzy logic and neural networks. Finally, based on load forecasting and equipment status, the thermal power commands of each module are dynamically allocated to achieve globally optimized operation between the unit and the load.

[0006] Another objective of this invention is to provide a multi-module high-temperature gas-cooled reactor power coordination control device.

[0007] The third objective of this invention is to provide a computer device.

[0008] A fourth objective of this invention is to provide a non-transitory computer-readable storage medium.

[0009] To achieve the above objectives, this invention proposes a method for coordinated power control of a multi-module high-temperature gas-cooled reactor, comprising: S1, based on the system layout and coupling mechanism of the multi-module high-temperature gas-cooled reactor, constructs a hierarchical three-level collaborative control architecture, decomposing the control system into an intra-module coordination control layer, an inter-module intelligent decoupling control layer, and a load coordination control layer; S2, in view of the strong coupling characteristics within the module, adopts a feedforward-cascade control structure to design the control system of each nuclear steam supply system module, and linearizes and tunes the control parameters of reactor power, hot helium temperature, feedwater flow rate and steam temperature through classical control theory; S3, based on fuzzy logic and neural networks, constructs an n-dimensional fuzzy controller, using the controlled variable of the current module as the main input and the representational state parameters of other modules as auxiliary inputs, and formulates fuzzy logic rules to achieve decoupling of dynamic characteristics between modules; S4, based on load forecast results and equipment status evolution patterns, designs a power distribution control layer to dynamically adjust the thermal power commands of each nuclear steam supply system module, thereby achieving global coordination between the unit and the load.

[0010] The multi-module high-temperature gas-cooled reactor power coordination control method of this invention may also have the following additional technical features: In one embodiment of the present invention, a hierarchical three-level collaborative control architecture is constructed, including: S11, the module’s internal coordination control layer is designed as a feedforward-cascade control structure that includes nuclear power regulation, hot helium temperature control, feedwater flow control and steam temperature regulation. S12 configures the inter-module intelligent decoupling control layer to achieve cross-module dynamic decoupling of main steam pressure and main feedwater pressure through an n-dimensional fuzzy controller.

[0011] In one embodiment of the present invention, linearization modeling and tuning of control parameters are performed using classical control theory, including: S21. The nonlinear dynamic models of the pebble bed core, DC steam generator and feedwater system are linearized using Taylor expansion and Jacobian matrix to obtain the transfer function matrix. S22, based on crossover frequency Frequency domain parameters of ≥0.5 rad / s and phase margin ≥45° are used to design PID controller parameters analytically.

[0012] In one embodiment of the present invention, fuzzy logic rules are formulated to achieve dynamic characteristic decoupling between modules, including: S31, set the main input to the main steam pressure of the current module, and the auxiliary input to the feedwater flow rate and outlet steam temperature of other modules; S32 adjusts the weights of fuzzy control rules in real time through a dynamic coupling weight algorithm.

[0013] In one embodiment of the present invention, dynamically adjusting the thermal power command of each nuclear steam supply system module includes: S41 adopts a power allocation model based on equipment state evolution, which integrates the state parameters of core fuel temperature and steam generator thermal stress. S42, based on the power grid frequency deviation Temperature deviation of heating main pipe The thermal power command is corrected through a secondary frequency modulation control algorithm.

[0014] In one embodiment of the present invention, it further includes: S5, when the tripping of the high-voltage side circuit breaker of the main transformer is detected, the islanding operation mode is started, and the thermal power command of each nuclear steam supply system module is adjusted through the plant power load matching algorithm to make the plant power load matching error ≤5%.

[0015] To achieve the above objectives, another aspect of the present invention provides a multi-module high-temperature gas-cooled reactor power coordination control device, comprising: The hierarchical control architecture construction module is used to build a hierarchical three-level collaborative control architecture based on the system layout and coupling mechanism of multi-module high-temperature gas-cooled reactors. The control system is decomposed into an intra-module coordination control layer, an inter-module intelligent decoupling control layer, and a load coordination control layer. The feedforward-cascade control structure design module is used to design the control system of each nuclear steam supply system module with a feedforward-cascade control structure to address the strong coupling characteristics within the module. The control parameters of reactor power, hot helium temperature, feedwater flow rate and steam temperature are linearized and tuned using classical control theory. The n-dimensional fuzzy controller construction module is used to construct an n-dimensional fuzzy controller based on fuzzy logic and neural networks. It uses the controlled variable of the current module as the main input and the representational state parameters of other modules as auxiliary inputs, and formulates fuzzy logic rules to achieve decoupling of dynamic characteristics between modules. The power distribution control layer design module is used to design the power distribution control layer based on load forecast results and equipment status evolution patterns, dynamically adjust the thermal power commands of each nuclear steam supply system module, and achieve global coordination between the unit and the load.

[0016] In one embodiment of the present invention, it further includes: The status and load error monitoring module is used to start the islanded operation mode when the tripping of the main transformer high-voltage side circuit breaker is detected. It adjusts the thermal power command of each nuclear steam supply system module through the plant power load matching algorithm to make the plant power load matching error ≤5%.

[0017] This invention discloses a power coordination control method and apparatus for a multi-module high-temperature gas-cooled reactor, effectively solving the problems of complex control characteristics and easy deviation of operating parameters from the design range caused by strong coupling between multiple modules. It achieves precise coordination within modules and intelligent decoupling between modules through a hierarchical control architecture, combines fuzzy logic and neural networks to dynamically adjust control rules, and optimizes power allocation based on load forecasting and equipment status. This improves the system's control accuracy, operational stability, and overall coordination capability under complex operating conditions such as variable loads and islanding, providing reliable technical support for the safe, efficient, and flexible operation of multi-module high-temperature gas-cooled reactors.

[0018] To achieve the above objectives, a third aspect of this application provides a computer device, including a processor and a memory; wherein the processor runs a program corresponding to the executable program code by reading executable program code stored in the memory, for implementing a multi-module high-temperature gas-cooled reactor power coordination control method as described in the first aspect embodiment.

[0019] To achieve the above objectives, a fourth aspect of this application provides a non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements a multi-module high-temperature gas-cooled reactor power coordination control method as described in the first aspect embodiment.

[0020] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0021] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a power coordination control method for a multi-module high-temperature gas-cooled reactor according to an embodiment of the present invention; Figure 2 This is a schematic diagram of an NSSS module coordination control scheme based on fuzzy logic, which is another power coordination control method for multi-module high-temperature gas-cooled reactors according to an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of a multi-module high-temperature gas-cooled reactor power coordination control device according to an embodiment of the present invention; Figure 4 It is a computer device according to an embodiment of the present invention. Detailed Implementation

[0022] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] The following description, with reference to the accompanying drawings, describes a method, apparatus, equipment, and storage medium for coordinated power control of a multi-module high-temperature gas-cooled reactor according to an embodiment of the present invention.

[0025] The core idea of ​​this invention is to construct a dynamic graph model covering multiple levels of the battery pack, from individual cells to clusters to the entire pack. This model integrates electrical connections and physical proximity to establish a spatial topology, and introduces temporal edges to characterize the system's dynamic operation. Based on this, an attribute-decoupled encoder is used to divide node features into health, anomaly, and trend subspaces. Orthogonal constraints and mutual information constraints are used to achieve feature decoupling and redundancy suppression. Furthermore, a cross-layer message passing mechanism establishes bidirectional information paths from bottom to top and from top to bottom. Gated attention is used to dynamically adjust the interaction intensity between levels, achieving deep fusion of multi-level features and collaborative updating of global representations. Finally, health status and lifetime prediction heads are set based on shared representations, and a joint loss function containing multiple constraints is constructed. This is combined with a sliding time window and edge weight update mechanism for integrated training. This transforms the traditionally separate state assessment and lifetime prediction into an intelligent prediction system that can fully exploit hierarchical relationships, suppress feature interference, and achieve multi-task collaborative optimization, significantly improving the accuracy, stability, and engineering applicability of battery pack health status and remaining lifetime predictions.

[0026] Example 1 To achieve the above invention, embodiments of the present invention provide a method for coordinated power control of a multi-module high-temperature gas-cooled reactor, such as... Figure 1 As shown, it includes: S1, based on the system layout and coupling mechanism of the multi-module high-temperature gas-cooled reactor, constructs a hierarchical three-level collaborative control architecture, decomposing the control system into an intra-module coordination control layer, an inter-module intelligent decoupling control layer, and a load coordination control layer.

[0027] Specifically, this step decomposes the complex multi-module coupled control problem into three levels through system modeling and control structure design: intra-module coordination control layer, inter-module intelligent decoupling control layer, and load coordination control layer, thereby realizing the modularization, intelligence, and efficiency of the control system.

[0028] Specifically, this step first establishes a system-level control architecture model based on the physical layout and dynamic coupling characteristics of the multi-module high-temperature gas-cooled reactor. Each nuclear steam supply system (NSSS) module consists of a reactor and a steam generator, which are tightly coupled through primary / secondary loop interface parameters (such as helium temperature, steam pressure, and feedwater flow rate). To achieve intra-module control, a feedforward-cascade control structure is adopted, incorporating key variables such as reactor power, hot helium temperature, feedwater flow rate, and steam temperature into the control loop. Through linearization of the nonlinear dynamic model, the transfer function models of each subsystem are obtained, providing a foundation for subsequent controller design.

[0029] Furthermore, the control system design must meet performance requirements in both the frequency and time domains. In the frequency domain, the open-loop system must have a gain margin. Phase margin To ensure system stability; closed-loop systems, on the other hand, need to control the resonant peak value. Resonant frequency and bandwidth satisfy In the time domain, the system response must satisfy the overshoot requirement. Adjusting time This ensures dynamic performance under typical transient conditions.

[0030] Specifically, this control architecture is applicable to various operating modes of multi-module high-temperature gas-cooled reactor nuclear power plants, including grid peak shaving, heating mains pressure / temperature regulation, and plant power load matching under islanded operation. Through hierarchical control, the system can flexibly respond to different load demands while avoiding mutual interference between control actions of different modules, thereby improving overall operating efficiency and safety.

[0031] Specifically, the three-level collaborative control architecture constructed in this step effectively decouples the dynamic influences between modules, improving the robustness and response speed of the control system. Intra-module control ensures the stability of a single NSSS module, inter-module control achieves cross-module state awareness and collaborative response through fuzzy logic strategies, and load coordination control achieves global optimization through power allocation algorithms. This architecture provides systematic support for achieving the "stack-following-machine" control objective, and has significant engineering practical value and innovation.

[0032] Furthermore, S1 includes: S11 designs the module's internal coordination control layer as a feedforward-cascade control structure that includes nuclear power regulation, hot helium temperature control, feedwater flow control, and steam temperature regulation.

[0033] Specifically, this control structure effectively addresses the complex dynamic characteristics of strong coupling, nonlinearity, and time delay between the reactor and the steam generator by introducing a combination of feedforward control and cascade control, thereby improving the response speed and regulation accuracy of the overall control system.

[0034] Furthermore, the feedforward control section, based on a predictive model of the system input variables, compensates in advance for major disturbances (such as load changes and feedwater flow fluctuations), reducing reliance on feedback control and improving the system's disturbance rejection capability. For example, in feedwater flow control, the feedforward controller calculates the rate of change of the main steam pressure setpoint. To suppress transient fluctuations in steam pressure, the opening of the feedwater regulating valve is adjusted in advance. The cascade control structure consists of a main controller and a secondary controller. The main controller sets the setpoints for the secondary controller, which then performs local closed-loop control on fast-response variables (such as feedwater flow rate and steam temperature). For example, in steam temperature regulation, the main controller adjusts the setpoint based on the main steam temperature deviation. The controller generates a setpoint for the water supply flow rate, and the secondary controller calculates the deviation between the actual water supply flow rate and the setpoint. PID control is used to achieve fast and precise temperature control.

[0035] Furthermore, this control structure requires the main controller to have a sampling period. The sampling period of the secondary controller is no more than 100ms. Typically, the time interval is 20ms to 50ms to meet control requirements at different time scales. Simultaneously, the control system must meet certain frequency domain stability parameters, such as open-loop gain margin. Phase margin and the resonant peak value of the closed-loop system bandwidth This is to ensure the robustness and dynamic performance of the system under different operating conditions.

[0036] Specifically, this control structure is suitable for the independent control layer of each NSSS module in a multi-module high-temperature gas-cooled reactor nuclear power plant. Especially in the "reactor-following" operation mode, it can quickly respond to turbine load changes and achieve synchronous regulation of reactor power and steam parameters. For example, during grid peak shaving or sudden changes in heating load, the feedforward-cascade control structure can effectively suppress overshoot of main steam pressure and temperature, reducing system settling time. This reduces equipment wear and response delay caused by frequent control rod movements.

[0037] Specifically, through the synergistic effect of feedforward compensation and cascade control, the dynamic coordination capability between subsystems within the module is significantly improved, the coupling effect of the control system is reduced, and the regulation accuracy and response speed are improved. This lays a solid foundation for the coordinated control between modules and between the unit and the load, and has significant engineering practical value and innovation.

[0038] S12 configures the inter-module intelligent decoupling control layer to achieve cross-module dynamic decoupling of main steam pressure and main feedwater pressure through an n-dimensional fuzzy controller.

[0039] Specifically, this control layer is located above the intra-module coordination control and aims to enhance the coordination capabilities between multiple NSSS modules, prevent the control actions of one module from causing adverse disturbances to other modules, and thus improve the overall system stability and regulation performance.

[0040] Furthermore, this n-dimensional fuzzy controller adopts a multiple-input single-output (MISO) structure, with the main control quantity being the main steam pressure or main feedwater pressure of the current NSSS module, denoted as... The auxiliary control parameters are derived from the status parameters of other modules, such as the main steam pressure deviation between adjacent modules. Main water supply pressure deviation Water supply flow rate change rate etc., denoted as ,in This indicates the number of other modules. The fuzzy controller uses a fuzzy rule base to reason about these input variables and outputs control signals to adjust the opening of the feedwater regulating valve or steam regulating valve of the current module, thereby achieving dynamic decoupled control of the main steam pressure and the main feedwater pressure.

[0041] Furthermore, the input variables of the fuzzy controller are typically normalized to fit the universe of discourse of the fuzzy set; for example, pressure deviation is mapped to the interval [-1, 1], and flow rate change rate is mapped to [-0.5, 0.5]. The design of the fuzzy rule base is based on expert experience and simulation data, adopting an "if-then" form, such as: "If the main steam pressure deviation is negative and the flow rate change rate of other modules is positive, then the output control signal is negative," thereby compensating for the coupling effect of the system.

[0042] Specifically, this control layer is deployed in the common conventional island control system of the multi-module high-temperature gas-cooled reactor. By collecting the operating parameters of each module in real time and combining them with a fuzzy inference mechanism, the control strategy is dynamically adjusted. Its technical effects are reflected in significantly reducing the impact of parameter coupling between modules on system stability, improving the regulation accuracy and response speed of main steam pressure and main feedwater pressure, thereby providing strong support for achieving the "reactor-following-the-machine" control objective.

[0043] S2, in response to the strong coupling characteristics within the module, adopts a feedforward-cascade control structure to design the control system of each nuclear steam supply system module, and uses classical control theory to linearize and tune the control parameters of reactor power, hot helium temperature, feedwater flow rate and steam temperature.

[0044] Specifically, this step aims to address the control challenges arising from the high degree of dynamic coupling between the reactor and steam generator within a single NSSS module, ensuring stable operation of the module under both steady-state and transient conditions.

[0045] Furthermore, each NSSS module consists of a single-zone pebble bed module high-temperature gas-cooled reactor and a helical DC steam generator. The two exchange heat via a primary loop of helium, exhibiting significant dynamic coupling characteristics. To achieve coordinated control among the subsystems within the module, this invention employs a feedforward-cascade control structure. Feedforward control compensates for known disturbances (such as load changes, feedwater flow fluctuations, etc.) to improve system response speed; cascade control, through a hierarchical structure of master and slave controllers, achieves coordinated regulation of primary variables (such as reactor power) and secondary variables (such as hot helium temperature and steam temperature). For example, the master controller adjusts the reactor power, while the slave controller adjusts the feedwater flow based on steam temperature deviations, thus forming a closed-loop feedback.

[0046] Furthermore, the control system design is based on classical control theory, employing the frequency domain method for controller parameter tuning. Specifically, by linearizing the nonlinear dynamic model, the transfer function models of each subsystem are obtained, such as the reactor power transfer function. Thermo-helium temperature transfer function Water supply flow transfer function etc. When designing the controller, the gain margin of the open-loop system is considered. and phase margin As a stability criterion, the resonant peak value of the closed-loop system is also considered. Resonant frequency and bandwidth Performance metrics, etc., are achieved by adjusting the parameters of the proportional-integral-derivative (PID) controller. , , Alternatively, a phase correction controller can be used to enable the system to have good dynamic response characteristics while meeting stability requirements.

[0047] Specifically, this control strategy is applicable to each independent NSSS module in a multi-module high-temperature gas-cooled reactor nuclear power plant. Especially in the "two reactors with one turbine" operation mode, it can effectively isolate dynamic interference between modules and improve the control accuracy and response speed of a single module. For example, when there are sudden changes in grid load or turbine demand, the control system can quickly adjust reactor power and steam parameters to ensure that the turbine inlet steam pressure and temperature are maintained within the design range, thereby achieving the control objective of "reactor following turbine".

[0048] Specifically, by combining the feedforward-cascade control structure with classical control theory, the decoupling capability and dynamic performance of each control loop within the module are significantly improved, reducing the difficulty of system debugging and enhancing operational reliability. At the same time, it provides a solid foundation for subsequent coordinated control between modules and between the unit and the load, and is an important guarantee for the safe, stable, and economical operation of the multi-module high-temperature gas-cooled reactor.

[0049] Furthermore, S2 includes: S21. The nonlinear dynamic models of the pebble bed core, DC steam generator, and feedwater system are linearized using Taylor expansion and Jacobian matrix to obtain the transfer function matrix.

[0050] Specifically, this step uses Taylor expansion and Jacobian matrix methods to approximate the complex nonlinear system as a linear system near the operating point, thus facilitating the subsequent controller design and parameter tuning based on classical control theory.

[0051] Furthermore, the linearization process first selects the steady-state operating point of the system under typical operating conditions, for example, the reactor thermal power is... The primary loop helium flow rate is Water supply flow rate is The main steam pressure is Etc. Near this operating point, a Taylor expansion is performed on the nonlinear dynamic model, retaining first-order terms and ignoring higher-order nonlinear terms, thus obtaining a linearized state-space model or transfer function matrix. Specifically, let the nonlinear dynamic model of the system be: ; ; in, For the state variable vector, To control the input vector, This is the output variable vector. At the steady state point... Linearization at this point yields: ; ; in, , , , , and represent the Jacobian matrix and output matrix of the system, respectively. By calculating these partial derivatives, the local linear model of the system can be obtained.

[0052] Furthermore, the accuracy of this linearized model depends on the selection of the operating point and the degree of system nonlinearity. In this invention, the operating point is typically selected near the rated power operating condition to ensure that the linearized model has high accuracy within the typical operating range. Simultaneously, to improve the robustness of the model, a multi-point linearization method can be optionally employed, i.e., linearization is performed at multiple typical operating points to construct multiple sets of transfer function matrices for switching control strategies under different operating conditions.

[0053] Specifically, this is typically implemented in simulation platforms (such as MATLAB / Simulink or NESSUS), where the Jacobian matrix is ​​calculated using numerical differentiation or analytical methods. In the design of high-temperature gas-cooled reactor control systems, linearized models are used to design classic control structures such as PID controllers and phase correction controllers, and provide a foundation for frequency domain analysis (such as gain margin, phase margin, resonant frequency, etc.), thereby ensuring that the control system has good dynamic response and stability under both steady-state and transient conditions.

[0054] Specifically, this step transforms the nonlinear system into a linear model through mathematical modeling, providing a theoretical basis and implementation foundation for subsequent control strategy design, and is an important prerequisite for achieving the "stack-following-machine" control objective.

[0055] S22, based on crossover frequency Frequency domain parameters of ≥0.5 rad / s and phase margin ≥45° are used to design PID controller parameters analytically.

[0056] Specifically, this step aims to ensure that the control system meets design requirements in terms of dynamic response and stability through frequency domain analysis, thereby providing basic support for the "reactor-follower" operation mode of the multi-module high-temperature gas-cooled reactor.

[0057] Specifically, this step first requires linearizing the nonlinear dynamic model of the high-temperature gas-cooled reactor and its supporting key equipment such as the steam generator and feedwater system. This is typically done using a small-disturbance linearization method, establishing a state-space model or transfer function model around the system's steady-state operating point. Subsequently, based on frequency domain analysis methods from classical control theory, such as Bode plots or Nyquist plots, the frequency response characteristics of the open-loop system are evaluated, and the crossover frequency is determined. and phase margin The actual value. Crossing frequency. The frequency at which the system's open-loop gain is 0 dB is an important indicator of the system's response speed; phase margin. This reflects the phase margin of the system at the crossing frequency and is a key parameter for measuring system stability.

[0058] Furthermore, an analytical method is used to design the PID controller, that is, the controller parameters are derived from the frequency domain parameters. Specifically, the controller's transfer function can be expressed as: ; in, For proportional gain, For integral gain, This is the differential gain. By adjusting these three parameters, the open-loop frequency response of the system is made to satisfy... and The requirements are as follows. In practice, the Ziegler-Nichols method, frequency domain optimization method, or model-based analytical design method can be used, combined with the system transfer function, to calculate the parameters.

[0059] Specifically, this step is mainly applied to subsystems within each NSSS module, such as reactor power control, hot helium temperature control, steam generator feedwater control, and steam temperature control. Due to the strong coupling between modules, the performance of the internal control systems directly affects the overall coordinated control effect. Therefore, by tuning PID parameters driven by frequency domain indicators, the response speed and stability of each subsystem within the module can be effectively improved, providing a reliable foundation for subsequent coordinated control between modules.

[0060] Furthermore, the technical effect of this step is reflected in: satisfying... and Under the premise of [specific conditions], the system possesses excellent dynamic response and disturbance rejection capabilities, enabling it to quickly track target power commands and suppress the impact of external disturbances on system stability. Simultaneously, this method avoids the blindness of traditional trial-and-error methods, improving the efficiency and accuracy of control system design, and providing theoretical support and engineering implementation path for the coordinated control of multi-module high-temperature gas-cooled reactors.

[0061] S3 is an n-dimensional fuzzy controller constructed based on fuzzy logic and neural networks. It uses the controlled variable of the current module as the main input and the representational state parameters of other modules as auxiliary inputs, and formulates fuzzy logic rules to achieve decoupling of dynamic characteristics between modules.

[0062] Specifically, the controller uses the controlled variables of the current NSSS module (such as main steam pressure, feedwater flow rate, etc.) as the main input, and introduces the characterization parameters of other modules (such as outlet steam temperature, feedwater regulating valve opening, etc.) as auxiliary inputs, so as to comprehensively consider the operating status of multiple modules in the control process and achieve coordination and decoupling of dynamic response.

[0063] Furthermore, the input variables of the n-dimensional fuzzy controller include the master and controlled variables of the current module. and status parameters of several auxiliary modules ( ),in This indicates the total number of NSSS modules participating in the coordinated control. The controller's output is the control action of the current module. Examples include adjusting the opening of the water supply regulating valve or controlling the speed of the main helium blower. The fuzzy rule base is designed based on the coupling characteristics between modules, and usually adopts a Mamdani-type fuzzy inference structure, completing the control decision through three stages: fuzzification, rule inference, and defuzzification.

[0064] Furthermore, the input variables of the fuzzy controller are typically divided into multiple fuzzy sets, such as "low," "medium," and "high," and quantized using triangular or trapezoidal membership functions. The formulation of fuzzy rules needs to combine system simulation data with actual operating experience to ensure good decoupling effects under different operating conditions. For example, when the main steam pressure of module 1... The water temperature is too high, while the water supply temperature of module 2 is... When the water level is too low, the fuzzy controller will adjust the opening of the water supply regulating valve of module 1 according to the preset rules to avoid causing secondary disturbance to module 2.

[0065] Specifically, this n-dimensional fuzzy controller is deployed in the coordination control layer of a multi-module high-temperature gas-cooled reactor to handle the coupling effects between modules in the main control system caused by changes in parameters such as steam pressure and feedwater flow. By introducing state parameters from other modules as auxiliary inputs, the controller can dynamically adjust the control strategy, avoiding system-level chain reactions caused by the control actions of a single module, thereby improving the robustness and response speed of the overall control system.

[0066] Specifically, the technical effect of this step is that it effectively alleviates the control conflict caused by parameter coupling in multi-module systems, improves the independence and coordination of each module in the dynamic adjustment process, and provides key support for achieving the "stack-following-machine" control objective.

[0067] Furthermore, S3 includes: S31, set the main input to the main steam pressure of the current module, and the auxiliary input to the feedwater flow rate and outlet steam temperature of other modules.

[0068] Specifically, this step enhances the control system's ability to perceive the coupling effect of multiple modules by introducing cross-module state parameters as auxiliary inputs, thereby improving the overall system's dynamic response performance and operational stability.

[0069] Specifically, this step is designed based on a fuzzy adaptive control strategy. Main steam pressure This is the core controlled variable of the current NSSS module, and its changes directly reflect the thermodynamic coupling state between the reactor core and the steam generator. To achieve coordination between modules, the control system also needs to incorporate feedwater flow rates from other modules. and outlet steam temperature (in (The numbers representing other modules) are used as auxiliary inputs. These parameters are shared through the main control system and have strong cross-module influence characteristics. For example, a change in the opening of the feedwater regulating valve of one module will cause a disturbance in the feedwater flow of all modules, while fluctuations in the outlet steam temperature will affect the feedwater temperature settings of other modules through the steam main pipe.

[0070] Furthermore, the input variables of the fuzzy controller typically include the main steam pressure deviation of the current module. And the deviation in water supply flow from other modules and the deviation of the outlet steam temperature The fuzzy rule base is designed based on fuzzy sets of these deviations (such as "negative large", "negative small", "zero", "positive small", "positive large") to achieve control over the variable. and Adaptive adjustment.

[0071] Furthermore, the control accuracy of the main steam pressure is typically required to be within... Within, the response time does not exceed The control range of water supply flow is generally within... to Between these points, the setpoint for the outlet steam temperature is typically... The allowable deviation is These indicators meet the typical requirements for the operation of the main steam system in a high-temperature gas-cooled reactor demonstration project.

[0072] Specifically, it is typically deployed in the coordination control layer of a multi-module high-temperature gas-cooled reactor to achieve dynamic decoupling and power allocation optimization among modules when load changes, equipment status fluctuations, or external disturbances occur. For example, during grid peak shaving or sudden changes in heating demand, by collecting the operating status of other modules in real time, the controller of the current module can dynamically adjust its control strategy to avoid system-level cascading disturbances caused by the adjustment actions of a single module.

[0073] Specifically, this step significantly enhances the fuzzy controller's adaptability to system coupling characteristics by introducing multi-module state parameters as auxiliary inputs, improves the control accuracy and regulation speed of the main steam pressure, and reduces the risk of mutual interference between modules. This method not only improves the operational reliability of the multi-module high-temperature gas-cooled reactor but also provides strong support for achieving the "reactor-following-the-engine" control objective.

[0074] S32 adjusts the weights of fuzzy control rules in real time through a dynamic coupling weight algorithm.

[0075] Specifically, this step is based on a fuzzy adaptive control strategy, which combines the real-time changes in the system's operating state to dynamically optimize the weight allocation of control rules for each module, thereby improving the overall control system's response speed and coordination capabilities.

[0076] Furthermore, the dynamic coupling weight algorithm calculates the weight coefficients of the current control rule by quantifying the degree of state coupling between modules. Its definition is: ; in, This represents the change in steam pressure of the i-th NSSS module. This represents the steam pressure change of the j-th NSSS module, where n is the total number of NSSS modules in the system. This formula reflects the dynamic coupling strength between modules by comparing the relative magnitudes of steam pressure changes, thus providing dynamic weight inputs for the fuzzy controller.

[0077] Furthermore, the algorithm first collects real-time steam pressure data from each module, calculates its rate of change, and substitutes it into the above formula to obtain the control weight of module i on module j. This weight is then input into the n-dimensional fuzzy controller as a weighting factor for the fuzzy rules, used to adjust the main control quantity. With auxiliary control quantity The strength of the effect. The main input of the fuzzy controller is the controlled variable of the current module (such as the main steam pressure), while the auxiliary inputs are the characteristic state parameters of other modules (such as steam temperature, feedwater flow rate, etc.). The coordinated optimization of control signals of multiple modules is achieved through the fuzzy inference mechanism.

[0078] Specifically, the dynamic coupling weighting algorithm requires that the steam pressure sampling frequency of each module be no less than 10 Hz to ensure the real-time performance of the dynamic response. Meanwhile, the weighting calculation uses... and The average pressure change should be based on a sliding time window (e.g., 5-10 seconds) to filter out transient noise interference. Weighting coefficients The value range of is [0,1], and it satisfies the normalization condition. This ensures the proper allocation of control signals.

[0079] Specifically, this algorithm is applicable to the coordinated control of multi-module high-temperature gas-cooled reactors under complex operating conditions such as load changes, grid peak shaving, and pressure fluctuations in the heating main pipe. Especially in the "reactor-following-turbine" operation mode, when the turbine load changes abruptly, the steam pressure response of each module differs. The dynamic coupling weight algorithm can quickly identify the coupling relationship between modules, adjust the weights of the fuzzy control rules, thereby avoiding control signal conflicts and improving system stability and response consistency.

[0080] Furthermore, the technical benefits of this step are reflected in significantly reducing dynamic deviations caused by control coupling between modules, and improving the robustness and adaptability of the control system. By introducing a dynamic weighting mechanism, the fuzzy controller can adaptively adjust the control strategy according to the real-time state of the system, achieving coordinated optimization of control signals among multiple modules, and providing strong support for the efficient and safe operation of multi-module high-temperature gas-cooled reactors.

[0081] S4, based on load forecast results and equipment status evolution patterns, designs a power distribution control layer to dynamically adjust the thermal power commands of each nuclear steam supply system module, thereby achieving global coordination between the unit and the load.

[0082] Specifically, this step involves constructing upper-level control logic to dynamically adjust the thermal power commands of each nuclear steam supply system (NSSS) module, thereby achieving optimal power allocation and precise load matching in a multi-module high-temperature gas-cooled reactor system.

[0083] Furthermore, the power allocation control layer primarily relies on a load forecasting mechanism. This mechanism, based on multi-source information such as historical load data, grid dispatch instructions, and user heat demand, employs time series analysis or machine learning models (such as ARIMA and LSTM) to perform short- and medium-term load forecasting. The forecast results are based on a time resolution, typically at the minute or hour level, and are used to generate load demand curves for a future time window. ,in Indicates the time step.

[0084] Furthermore, the power distribution control layer also needs to consider the evolution of equipment states, specifically the thermal power response characteristics of each NSSS module under different operating conditions. Because the power response of a high-temperature gas-cooled reactor is nonlinear and hysteretic, especially during control rod actuation where reactor power changes slowly, a dynamic compensation mechanism needs to be introduced during power command distribution. This mechanism predicts the module's power response capability at the next moment by real-time acquisition of primary loop helium temperature, pressure, flow rate, and other state parameters of each module, combined with their historical trends. ,in Indicates the first One NSSS module.

[0085] Optionally, the power allocation control layer employs a multi-objective optimization algorithm, such as linear programming (LP) or mixed-integer linear programming (MILP), with the objective function of minimizing power deviation and settling time, to establish the following optimization model: ; ; ; in, Indicates the first The power command of each module at the current moment. and These represent the minimum and maximum adjustable power ranges of the module, respectively. This represents the total number of NSSS modules. Using this model, the system can rationally allocate power commands to each module while meeting total load requirements, reducing adjustment conflicts caused by inter-module coupling.

[0086] Specifically, this step is applicable to the multi-module, single-unit operation mode of high-temperature gas-cooled reactor nuclear power plants, and has significant engineering value, especially under complex operating conditions such as grid peak shaving, heating mains pressure / temperature regulation, and islanded operation. Through dynamic power command allocation, the system can effectively improve power response speed, reduce regulation overshoot, and enhance the overall operational economy and safety.

[0087] Furthermore, S4 includes: S41 adopts a power allocation model based on equipment state evolution, integrating the state parameters corresponding to core fuel temperature and steam generator thermal stress.

[0088] Specifically, this step is the top-level control layer in the entire three-level coordinated control system. Its core objective is to rationally allocate the thermal power output of each NSSS module based on the load forecast results and the dynamic evolution of the status of each module equipment, so as to achieve a balance between power and heat supply and demand and improve the economy and safety of system operation.

[0089] Furthermore, this power allocation model collects key state parameters of each module, including core fuel temperature. Steam generator thermal stress Main steam pressure Water supply flow rate Reactor thermal power Helium cold / hot end temperature Steam outlet temperature Core neutron flux Control rod position Main helium blower speed Core inlet and outlet pressure Steam flow rate at the steam generator outlet Core fuel temperature change rate Steam generator thermal stress change rate and the core power response time constant A multidimensional state-space model is constructed using 15 or more parameters.

[0090] Furthermore, this model combines load forecasting algorithms (such as ARIMA, LSTM, etc.) to predict the electrical load within a future time window. and heat load The system analyzes the changing trends and, based on the current status and historical operating data of each module, uses optimization algorithms (such as linear programming and dynamic weight allocation) to calculate the heat power command that should be allocated to each module. ,in Indicates the first There are NSSS modules. The power allocation strategy must meet the following constraint: the sum of the thermal power of all modules equals the total load demand, i.e. The power change rate of each module does not exceed its dynamic response capability, i.e. Core fuel temperature change rate This is to ensure that the core safety boundary is not breached.

[0091] Specifically, in practical applications, this step can be deployed in the central control system of a nuclear power plant. It collects operational data from each module through a DCS (Distributed Control System) and combines this data with instructions from the load dispatch center to achieve dynamic power allocation. In scenarios such as grid peak shaving, heating mains pressure regulation, and islanded operation, this model can quickly respond to load changes, preventing system instability caused by excessively rapid power adjustments in a single module.

[0092] Specifically, through this step, the present invention realizes the power coordination control of a multi-module high-temperature gas-cooled reactor under complex operating conditions, effectively alleviates the control problem caused by strong coupling between modules, improves the stability and response speed of system operation, and provides key support for the realization of the "reactor-follower" control objective.

[0093] S42, based on the power grid frequency deviation Temperature deviation of heating main pipe The thermal power command is corrected through a secondary frequency modulation control algorithm.

[0094] Specifically, this step involves collecting power grid frequency deviations. Temperature deviation of heating main pipe As an input signal, it reflects the degree of matching between the current power grid frequency stability and the heating system's heat load. Among them, It represents the difference between the mains frequency and the rated frequency (usually 50Hz or 60Hz), in Hz; This indicates the deviation between the actual temperature and the set temperature of the heating main pipe, expressed in °C. These two deviation signals represent the dynamic response requirements of the power system and the heating system, respectively, and are important bases for achieving the "reactor-following-the-machine" control objective.

[0095] Furthermore, this step uses a linear combination method to correct the thermal power reference value, with the correction amount being... The calculation formula is: ,in and These are correction factors for frequency deviation and temperature deviation, respectively, with units of MW / Hz and MW / ℃. These two factors need to be tuned based on the system's dynamic response characteristics, control objectives, and the thermoelectric coupling relationships of each module. They are typically determined through a combination of frequency domain analysis (such as Bode plots and Nyquist plots) and time domain simulation (such as step response, settling time, and overshoot) to ensure the system has good regulation performance and stability under different operating conditions.

[0096] Optionally, in the actual control system, this correction amount It will be superimposed on the original thermal power command. This forms a new thermal power reference value. This algorithm guides the power regulation actions of each NSSS module. It is suitable for rapid response scenarios in multi-module high-temperature gas-cooled reactors when grid frequency fluctuates or heating loads change abruptly, such as secondary frequency regulation compensation after primary grid frequency regulation and power redistribution when the heating main pipe temperature is abnormal.

[0097] Specifically, this step plays a crucial role in the three-level coordinated control system of the multi-module high-temperature gas-cooled reactor. On the one hand, it receives external disturbance signals from the power grid and heating network; on the other hand, it sends the corrected power commands to the inter-module and intra-module control systems, achieving dynamic optimization of global power allocation. This is achieved by introducing... and As a control input, this method effectively improves the system's adaptability to changes in electrical and thermal loads, enhances the coordination and robustness of multi-module operation, and thus improves the overall operating efficiency and safety of the nuclear power plant.

[0098] S5, when the tripping of the high-voltage side circuit breaker of the main transformer is detected, the islanding operation mode is started, and the thermal power command of each nuclear steam supply system module is adjusted through the plant power load matching algorithm to make the plant power load matching error ≤5%.

[0099] Specifically, the core of this step is to dynamically adjust the thermal power commands of each nuclear steam supply system (NSSS) module through a plant power load matching algorithm, so as to ensure that the matching error of the plant power load is controlled within ≤5%, thereby ensuring the continuous operation of critical equipment and system safety.

[0100] Specifically, this step relies on the power distribution control layer within the three-level coordinated control system of the multi-module high-temperature gas-cooled reactor. When the main transformer circuit breaker trips, causing the external power grid to disconnect from the nuclear power plant, the system enters islanded operation. At this time, the turbine is no longer driven by the grid load, but by the plant's auxiliary power load as the primary regulation target. The auxiliary power load matching algorithm, based on real-time collected auxiliary power demand data and the thermal power output capability of each NSSS module, dynamically adjusts the thermal power command of each module by optimizing the allocation strategy. This algorithm typically employs model predictive control (MPC) or fuzzy adaptive control strategies, combined with a load forecasting model and equipment condition monitoring system, to achieve rapid response and accurate allocation of thermal power commands.

[0101] Furthermore, the control target for the plant power load matching error is ≤5%, meaning the deviation between the adjusted thermal power output and the plant power load demand must not exceed 5%. This error index is typically quantified using the following formula: ; in, This indicates the current power load demand of the plant. Indicates the first The system calculates the thermal power output of each NSSS module in real time and feeds this error back to the power distribution control layer to dynamically correct the control commands of each module.

[0102] Specifically, this step applies to scenarios where multi-module high-temperature gas-cooled reactors maintain plant power load during grid faults or power outages. For example, after the main transformer's high-voltage side circuit breaker trips, the system needs to switch from grid-connected operation to islanded operation within seconds and quickly respond to load changes through the power distribution control layer to ensure a dynamic balance between reactor power output and plant power load. This control strategy is of great significance in high-temperature gas-cooled reactor demonstration projects, especially in the "two reactors with one generator" operation mode, as it can effectively avoid chain reactions caused by the instability of a single module, improving the overall robustness and safety of the system.

[0103] Specifically, by introducing a plant power load matching algorithm, the system can quickly enter islanded operation after grid disconnection and achieve matching of thermal power with plant power load in a short time, thereby avoiding equipment shutdowns or system instability caused by power imbalance. Furthermore, this control strategy reduces the need for operator intervention in emergency situations and improves the automation level and operational reliability of the nuclear power plant.

[0104] This invention discloses a power coordination control method for a multi-module high-temperature gas-cooled reactor. Through a hierarchical control architecture and intelligent decoupling strategy, it effectively solves the core challenges of complex control characteristics and easily deviated operating parameters from design ranges caused by strong coupling between multiple modules. This method achieves end-to-end collaborative optimization from intra-module coordination and inter-module dynamic decoupling to global load allocation, significantly improving the system's control accuracy, response speed, and operational stability. While ensuring the safe and reliable operation of the reactor, it enhances the unit's adaptability to complex operating conditions such as grid peak shaving, heating fluctuations, and islanding operation, thereby improving the overall operating efficiency and economy of the multi-module high-temperature gas-cooled reactor.

[0105] Example 2 To achieve the above invention, embodiments of the present invention also provide another method for coordinated power control of a multi-module high-temperature gas-cooled reactor, including: Specifically, based on the system layout and coupling mechanism of multi-module high-temperature gas-cooled reactors (NSSS), an overall framework for a power coordination control scheme for NSSS is proposed. This coordination control scheme adopts a hierarchical control structure, decomposing the coordination control of the NSSS into coordination control within each NSSS module, coordination control between NSSS modules, and coordination control between the unit and the load.

[0106] First, the influence mechanism of the feedwater system configuration on the main steam pressure control of the multi-module high-temperature gas-cooled reactor (NSSS) is determined. The advantages and disadvantages of unit layout and main pipe layout of the feedwater system are compared and analyzed to determine the feedwater system configuration scheme for the NSSS. Second, based on the operation scheme and control mode of the NSSS, the control system structure within each NSSS module is determined to coordinate the control between strongly coupled parameters within each NSSS module. Classical control theory is used to tune the control system parameters of each module. Then, to enhance the coordination among the NSSS modules, a fuzzy adaptive coordinated control strategy based on fuzzy logic or neural networks is formulated. Finally, based on the load forecasting mechanism and equipment state evolution laws, a power distribution control layer is designed to coordinate the allocation of thermal power commands to each NSSS module, achieving coordination between the unit and the load.

[0107] In one embodiment of the present invention, a coordinated control system within an NSSS module is designed based on classical control theory.

[0108] Specifically, each NSSS module includes a single-zone pebble bed module high-temperature gas-cooled reactor and a helical once-through steam generator. Because the reactor and steam generator are arranged side-by-side, their dynamic characteristics are closely coupled, requiring coordinated control between the reactor and steam generator within the module. This intra-module coordinated control ensures the operational stability of the individual NSSS module and that steady-state and transient regulation parameters meet requirements, including the regulation of nuclear power, primary loop helium flow rate, steam generator feedwater flow rate, primary loop helium cold / hot end temperature, and steam generator outlet steam temperature.

[0109] First, based on multivariable control theory, we will conduct a multivariable frequency domain coupling characteristic analysis of the multi-module high-temperature gas-cooled reactor (NSSS) module to determine the correspondence between the control variables and the controlled variables, and design the control system structure within the NSSS module using a feedforward-cascade control method. Second, we will evaluate the performance of the control system using frequency domain indices (gain margin, phase margin, etc.) of the open-loop system and frequency domain indices (closed-loop resonant peak value, resonant frequency, bandwidth, etc.) of the closed-loop system. We will design the NSSS control system using frequency domain analysis and complete the tuning of relevant control parameters. Finally, we will use time domain analysis to simulate and study the steady-state performance, dynamic performance (overshoot and settling time) of key system parameters under typical transient conditions, as well as the stability of the control system, to verify the performance of the control system.

[0110] Furthermore, the steps for designing the control system using classical control theory are as follows: First, the nonlinear dynamic models of key equipment or systems such as the pebble bed core, DC steam generator, and feedwater system are linearized to obtain the corresponding transfer function models. Second, using controller design functions developed based on the analytical method of crossover frequency and phase margin, PID controllers or phase correction controllers for control systems such as reactor power, hot helium temperature, steam generator feedwater, and steam temperature are designed. Finally, time-domain analysis is used to simulate and study the control dynamic performance of key system parameters.

[0111] In one embodiment of the present invention, NSSS module coordination control is based on an intelligent control algorithm.

[0112] Specifically, both feedwater and steam in the unit adopt a common pipe system, with multiple NSSS modules tightly coupled through a common conventional island. This results in the dynamic characteristics of each NSSS module influencing each other. Changes in the opening of the feedwater regulating valve of any NSSS module will alter the feedwater flow rate of all modules, and changes in the outlet steam temperature of any NSSS module will also affect the feedwater temperature of all modules. Therefore, coordinated control among the multiple NSSS modules is required. Inter-module coordinated control, based on intra-module coordinated control, ensures the operational stability and dynamic and steady-state regulation performance of the multiple NSSS modules meet requirements. This mainly includes the regulation of main steam pressure and main feedwater pressure, decoupling control of feedwater flow rate across multiple modules, power distribution, and feedwater flow rate adjustment and allocation.

[0113] First, simulation analysis clarifies the coupling mechanism of multiple NSSS modules in a multi-module high-temperature gas-cooled reactor system. Then, a fuzzy adaptive coordinated control strategy based on fuzzy logic is formulated to address the coordination control requirements among the NSSS modules. The core idea is to design fuzzy logic rules that use the controlled variable of the current NSSS module as the main input and the state parameters representing the other NSSS modules as auxiliary inputs. By comprehensively considering the operating states of different NSSS modules, coordination among the NSSS modules in the multi-module high-temperature gas-cooled reactor is achieved. A schematic diagram of the fuzzy logic-based coordinated control scheme for NSSS modules is shown below. Figure 2 An n-dimensional fuzzy controller is proposed, with only one primary control variable (umain) and the rest being auxiliary control variables (uauxiliary). In the design of the coordination control layer among the NSSS modules of a multi-module high-temperature gas-cooled reactor, when control of a particular NSSS module is required, the primary control variable is the controlled variable of that NSSS module, while the auxiliary control variables originate from the other NSSS modules. Coordination among the NSSS modules is achieved by introducing the characteristic state parameters of the other NSSS modules into the current NSSS module control system.

[0114] In one embodiment of the present invention, a power distribution control layer is designed based on load forecasting and equipment status.

[0115] Specifically, since the generating units can not only participate in grid peak shaving and ensure that the temperature and pressure of the heating main pipe meet the requirements by using load tracking, but also automatically match the plant power load through islanding operation after the main transformer high-voltage side circuit breaker trips, it is necessary to achieve load coordination control on the basis of coordinated control within and between modules to achieve a balance between power and heat supply and demand, maintain grid frequency stability and heating main pipe temperature and pressure stability, mainly including heating load control as well as primary frequency regulation and secondary frequency regulation control, etc.

[0116] Based on this, according to the load characteristics of different nuclear energy application scenarios such as power generation, hydrogen production, heating and steam supply, a power distribution control layer is designed based on load forecasting and equipment status to coordinate the allocation of thermal power commands for each NSSS module, thereby achieving coordination between the unit and the load.

[0117] Another power coordination control method for multi-module high-temperature gas-cooled reactors in this invention effectively solves the core problems of complex dynamic characteristics and difficulty in coordinating operating parameters caused by strong coupling between multiple modules through a hierarchical collaborative control architecture. It achieves full-process coordination from precise control within modules and intelligent decoupling between modules to global power optimization allocation, significantly improving the control accuracy and operational stability of the system under various operating conditions such as steady state, variable load, and islanding. While ensuring the safe operation of the reactor, it enhances the unit's adaptability to complex tasks such as grid frequency regulation and heating regulation, providing reliable technical support for the intensive operation and multi-scenario application of multi-module high-temperature gas-cooled reactors.

[0118] Example 3 To achieve the above invention, such as Figure 3 As shown, this embodiment also provides a multi-module high-temperature gas-cooled reactor power coordination control device 10, which includes: The hierarchical control architecture construction module 100 is used to construct a hierarchical three-level collaborative control architecture based on the system layout and coupling mechanism of the multi-module high-temperature gas-cooled reactor. The control system is decomposed into an intra-module coordination control layer, an inter-module intelligent decoupling control layer, and a load coordination control layer.

[0119] The feedforward-cascade control structure design module 200 is used to design the control system of each nuclear steam supply system module by adopting a feedforward-cascade control structure to address the strong coupling characteristics within the module. It uses classical control theory to linearize and tune the control parameters of reactor power, hot helium temperature, feedwater flow rate and steam temperature.

[0120] The n-dimensional fuzzy controller construction module 300 is used to construct an n-dimensional fuzzy controller based on fuzzy logic and neural networks. It uses the controlled variable of the current module as the main input and the representational state parameters of other modules as auxiliary inputs, and formulates fuzzy logic rules to achieve decoupling of dynamic characteristics between modules.

[0121] The power distribution control layer design module 400 is used to design the power distribution control layer based on load forecast results and equipment status evolution patterns, dynamically adjust the thermal power commands of each nuclear steam supply system module, and achieve global coordination between the unit and the load.

[0122] In one embodiment of the present invention, it further includes: a status and load error monitoring module, used to start islanded operation mode when the tripping of the main transformer high-voltage side circuit breaker is detected, and adjust the thermal power command of each nuclear steam supply system module through the plant power load matching algorithm so that the plant power load matching error is ≤5%.

[0123] This invention discloses a power coordination control device for a multi-module high-temperature gas-cooled reactor. By constructing a hierarchical collaborative control architecture, it effectively solves the problems of dynamic interference and coordination difficulties caused by strong coupling between multiple modules. The device achieves integrated control throughout the entire process, from precise adjustment of parameters within modules and intelligent dynamic decoupling between modules to adaptive global load allocation, significantly improving the system's control accuracy, response speed, and operational stability. While ensuring reactor safety, it enhances the unit's adaptability to complex operating conditions such as grid frequency regulation, heating fluctuations, and islanded operation, providing reliable hardware support and system assurance for the safe, efficient, and flexible operation of multi-module high-temperature gas-cooled reactors.

[0124] To implement the methods of the above embodiments, the present invention also provides a computer device, such as... Figure 4 As shown, the computer device 600 includes a memory 601 and a processor 602; wherein, the processor 602 reads the executable program code stored in the memory 601 to run a program corresponding to the executable program code, so as to implement the various steps of the multi-module high-temperature gas-cooled reactor power coordination control method described above.

[0125] To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a multi-module high-temperature gas-cooled reactor power coordination control method as described in the foregoing embodiments.

[0126] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0127] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A method for coordinated power control of a multi-module high-temperature gas-cooled reactor, characterized in that, include: S1, based on the system layout and coupling mechanism of the multi-module high-temperature gas-cooled reactor, constructs a hierarchical three-level collaborative control architecture, decomposing the control system into an intra-module coordination control layer, an inter-module intelligent decoupling control layer, and a load coordination control layer; S2, in view of the strong coupling characteristics within the module, adopts a feedforward-cascade control structure to design the control system of each nuclear steam supply system module, and linearizes and tunes the control parameters of reactor power, hot helium temperature, feedwater flow rate and steam temperature through classical control theory; S3, based on fuzzy logic and neural networks, constructs an n-dimensional fuzzy controller, using the controlled variable of the current module as the main input and the representational state parameters of other modules as auxiliary inputs, and formulates fuzzy logic rules to achieve decoupling of dynamic characteristics between modules; S4, based on load forecast results and equipment status evolution patterns, designs a power distribution control layer to dynamically adjust the thermal power commands of each nuclear steam supply system module, thereby achieving global coordination between the unit and the load.

2. The method as described in claim 1, characterized in that, Construct a hierarchical, three-tiered collaborative control architecture, including: S11, the module’s internal coordination control layer is designed as a feedforward-cascade control structure that includes nuclear power regulation, hot helium temperature control, feedwater flow control and steam temperature regulation. S12 configures the inter-module intelligent decoupling control layer to achieve cross-module dynamic decoupling of main steam pressure and main feedwater pressure through an n-dimensional fuzzy controller.

3. The method as described in claim 1, characterized in that, Linearization modeling and tuning of control parameters are performed using classical control theory, including: S21. The nonlinear dynamic models of the pebble bed core, DC steam generator and feedwater system are linearized using Taylor expansion and Jacobian matrix to obtain the transfer function matrix. S22, based on crossover frequency Frequency domain parameters of ≥0.5 rad / s and phase margin ≥45° are used to design PID controller parameters analytically.

4. The method as described in claim 1, characterized in that, Formulate fuzzy logic rules to achieve dynamic decoupling between modules, including: S31, set the main input to the main steam pressure of the current module, and the auxiliary input to the feedwater flow rate and outlet steam temperature of other modules; S32 adjusts the weights of fuzzy control rules in real time through a dynamic coupling weight algorithm.

5. The method as described in claim 1, characterized in that, Dynamically adjust the thermal power commands of each nuclear steam supply system module, including: S41 adopts a power allocation model based on equipment state evolution, which integrates the state parameters of core fuel temperature and steam generator thermal stress. S42, based on the power grid frequency deviation Temperature deviation of heating main pipe The thermal power command is corrected through a secondary frequency modulation control algorithm.

6. The method as described in claim 1, characterized in that, Also includes: S5, when the tripping of the high-voltage side circuit breaker of the main transformer is detected, the islanding operation mode is started, and the thermal power command of each nuclear steam supply system module is adjusted through the plant power load matching algorithm to make the plant power load matching error ≤5%.

7. A multi-module high-temperature gas-cooled reactor power coordination control device, characterized in that, include: The hierarchical control architecture construction module is used to build a hierarchical three-level collaborative control architecture based on the system layout and coupling mechanism of multi-module high-temperature gas-cooled reactors. The control system is decomposed into an intra-module coordination control layer, an inter-module intelligent decoupling control layer, and a load coordination control layer. The feedforward-cascade control structure design module is used to design the control system of each nuclear steam supply system module with a feedforward-cascade control structure to address the strong coupling characteristics within the module. The control parameters of reactor power, hot helium temperature, feedwater flow rate and steam temperature are linearized and tuned using classical control theory. The n-dimensional fuzzy controller construction module is used to construct an n-dimensional fuzzy controller based on fuzzy logic and neural networks. It uses the controlled variable of the current module as the main input and the representational state parameters of other modules as auxiliary inputs, and formulates fuzzy logic rules to achieve decoupling of dynamic characteristics between modules. The power distribution control layer design module is used to design the power distribution control layer based on load forecast results and equipment status evolution patterns, dynamically adjust the thermal power commands of each nuclear steam supply system module, and achieve global coordination between the unit and the load.

8. The apparatus as claimed in claim 7, characterized in that, Also includes: The status and load error monitoring module is used to start the islanded operation mode when the tripping of the main transformer high-voltage side circuit breaker is detected. It adjusts the thermal power command of each nuclear steam supply system module through the plant power load matching algorithm to make the plant power load matching error ≤5%.

9. An electronic device, comprising: processor; The memory stores executable instructions; when the processor executes the instructions, it implements the power coordination control method for a multi-module high-temperature gas-cooled reactor as described in any one of claims 1-6.

10. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements a power coordination control method for a multi-module high-temperature gas-cooled reactor as claimed in any one of claims 1-6.

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