Adaptive peak shaving control method and system for circulating water system of double-reheat unit

Abstract

The application relates to the field of peak regulation control, and discloses a circulating water system adaptive peak regulation control method and system for a double-reheat unit, which is used for guaranteeing the safe and stable operation of the unit. The method comprises the following steps: based on real-time operation data, a dynamic disturbance observer is used to generate a dynamic disturbance list, a condenser temperature and vacuum degree optimization target value is generated by combining a unit load instruction and environmental parameters, a differentiated compensation logic is used to calculate a target compensation amount, a safety boundary is determined by considering equipment states and operation limits, and the optimization target value is generated. According to the deviation between the target value and the actual state, the controller gain parameter is dynamically adjusted, the disturbance is divided according to responsibility, the differentiated control parameter update logic is configured, the safety instruction corridor is generated by considering the equipment operation state and the power plant power consumption constraint, and it is ensured that the control instruction is within the equipment safety and power grid allowable range, so that efficient, safe and adaptive control of the circulating water system in the peak regulation process is realized.

Description

Technical Field

[0001] This invention relates to the field of peak shaving control, and more particularly to an adaptive peak shaving control method and system for circulating water systems of secondary reheat units. Background Technology

[0002] In the power industry, double reheat units have become the mainstay of modern large-scale thermal power plants due to their high efficiency and environmental friendliness. The circulating water system, as a crucial component of the double reheat unit, directly affects the unit's thermal economy and safety. The condenser, as the core equipment of the circulating water system, has its temperature and vacuum levels as key parameters influencing the unit's operating efficiency. Accurate control of condenser temperature and vacuum can effectively improve the unit's thermal efficiency, reduce power generation costs, and ensure the unit's safe and stable operation under various conditions.

[0003] With the continuous development of the electricity market and the increasing demands of the power grid on the peak-shaving capacity of generating units, double reheat units need to operate flexibly within a wider load range to adapt to the peak-valley changes in the power grid. However, the circulating water system faces many complex factors in actual operation, such as frequent changes in unit load, changes in environmental parameters, equipment aging, and external disturbances. These factors can lead to changes in condenser thermal balance, circulating water temperature dynamics, and vacuum dynamics, thereby affecting the system's operating performance and economy.

[0004] Traditional circulating water system control methods typically employ fixed-parameter control strategies, making it difficult to adaptively adjust to the system's real-time dynamic characteristics. When faced with complex operating condition changes and external disturbances, traditional methods cannot promptly and accurately identify and compensate for these effects, leading to decreased control accuracy of condenser temperature and vacuum, increased system energy consumption, and potentially even affecting the safe operation of the unit.

[0005] Therefore, we propose an adaptive peak-shaving control method and system for the circulating water system of a double reheat unit to solve the above problems. Summary of the Invention

[0006] This invention provides an adaptive peak-shaving control method and system for the circulating water system of a secondary reheat unit, which is used to ensure the safe and stable operation of the unit.

[0007] The first aspect of this invention provides an adaptive peak-shaving control method for the circulating water system of a double reheat unit. The method includes: generating a dynamic disturbance list based on real-time operating data using a dynamic disturbance observer; generating optimized target values ​​for condenser temperature and vacuum degree based on unit load commands and environmental parameters, combined with the dynamic disturbance list; adjusting control actions in real time according to the deviation between the optimized target values ​​and the actual system state, and using the dynamic disturbance list to generate closed-loop operation commands for the circulating water pumps and vacuum auxiliary equipment; after the closed-loop operation commands are executed, fine-tuning the baseline strategy library for generating the optimized target values ​​online based on a comparison between the actual system response and expected economic indicators, and using the updated strategy library for subsequent control cycles.

[0008] Optionally, in a first implementation of the first aspect of the present invention, the method includes: based on real-time collected condenser temperature, circulating water flow rate, and vacuum level, combined with unit heat load and circulating water inlet temperature, using a preset basic differential equation, constructing an equivalent dynamic model reflecting the dynamic characteristics of the system at the current moment, and outputting a current parameter set; driving a nonlinear state observer to operate based on the current parameter set and real-time operating data; estimating unknown disturbance terms acting on condenser heat balance, circulating water temperature dynamics, and vacuum level dynamics by comparing and correcting the model calculation state with the measured state, and outputting an initial disturbance estimation list; using the model mismatch information reflected in the initial disturbance estimation list, adaptively fine-tuning the current parameter set to generate a system model parameter set, and using this parameter set for constructing the dynamic model in the next calculation cycle, and outputting a dynamic disturbance list.

[0009] Optionally, in the second implementation of the first aspect of the present invention, the method includes: querying pre-stored optimal condenser terminal differential-load relationship curves and optimal back pressure-load relationship curves based on the current unit load command and ambient temperature parameters, and generating a reference temperature target value and a reference vacuum target value; analyzing the estimation information on model uncertainty and external disturbances in the dynamic disturbance list, and calculating the condenser temperature target compensation amount and vacuum target compensation amount required to offset the disturbance effect through a preset compensation rule library; obtaining the current equipment status and operating limits of the circulating water system, and calculating the safe upper and lower boundaries of instantaneous allowable changes for the target values ​​of condenser temperature and vacuum based on the system dynamic characteristics; comparing and limiting the compensated target values ​​with the safe boundaries to generate optimized condenser temperature target values ​​and optimized vacuum target values.

[0010] Optionally, in a third implementation of the first aspect of the present invention, the method includes: generating a disturbance classification list based on the disturbance estimation information of each disturbance in the dynamic disturbance list; loading specific compensation logic applicable to different types of disturbances from the compensation rule base according to the disturbance classification list to form a differentiated compensation rule set; obtaining the peak-shaving operation mode instruction of the current unit, determining whether it is in deep load reduction, rapid load increase or steady-state operation, and outputting the condenser temperature target compensation amount and vacuum degree target compensation amount for generating optimized target values.

[0011] Optionally, in the fourth implementation of the first aspect of the present invention, the method includes: acquiring the target value of optimized condenser temperature and the target value of optimized vacuum in real time, and comparing them with the synchronously acquired actual condenser temperature and actual vacuum values ​​of the system to calculate a real-time multivariable tracking deviation vector; parsing the amplitude and direction information of the disturbance items in the dynamic disturbance list, and dynamically adjusting the proportional, integral, and derivative action strength of the controller used to process the multivariable tracking deviation vector according to a preset gain scheduling rule to generate adaptive controller gain parameters; applying the updated adaptive controller gain parameters to the multivariable tracking deviation vector, performing comprehensive calculations, and generating closed-loop flow control commands and closed-loop vacuum control commands respectively.

[0012] Optionally, in the fifth implementation of the first aspect of the present invention, the method includes: outputting a real-time coupling degree matrix based on a dynamic coupling relationship model of the current circulating water flow rate, condenser temperature, and vacuum degree; designing a dynamic feedforward compensator using the real-time coupling degree matrix to generate dynamic decoupling feedforward commands acting on the circulating water loop and the vacuum loop respectively; and outputting closed-loop flow control commands and closed-loop vacuum control commands with the goal of minimizing the total system energy consumption, while simultaneously accepting the multivariate tracking deviation vector, the updated adaptive controller gain parameters, the dynamic decoupling feedforward commands, and the external dynamic constraints.

[0013] Optionally, in the sixth implementation of the first aspect of the present invention, the method further includes: based on the content of the dynamic disturbance list and the system topology, classifying and allocating the identified disturbance items to the condenser sub-region, the circulating water loop sub-region, and the vacuum system sub-region according to their physical source and scope of influence, thereby generating a disturbance responsibility partition list; configuring differentiated control parameter update logic for the three control subsystems of condenser temperature control, circulating water flow control, and vacuum control according to the disturbance responsibility partition list, thereby forming a coordinated global control strategy; collecting the operating status, health indicators, and allowable power fluctuation range of key auxiliary equipment and the plant power bus in real time, and converting them into external dynamic constraints of the control system; and when processing the multivariable tracking deviation vector and generating equipment-level control commands, using the external dynamic constraints as mandatory boundaries for online optimization and solving to ensure that the output closed-loop operation commands are always within the operating envelope allowed by the equipment safety and the power grid.

[0014] Optionally, in the seventh implementation of the first aspect of the present invention, the method includes: obtaining an equipment operating capability map based on the bearing temperature, vibration amplitude, current, and cumulative operating time of key auxiliary equipment collected in real time, combined with pre-stored equipment design performance curves and aging models; based on the equipment operating capability map and combined with the allowable instantaneous power change rate of the plant power system, predicting the impact on the equipment status and plant power for the future several-step change sequence of the closed-loop flow control command and the closed-loop vacuum control command, and generating a safety command corridor; in the process of solving for the generation of the closed-loop flow control command and the closed-loop vacuum control command, the safety command corridor is used as an insurmountable hard boundary, and the command combination with the lowest energy consumption is found within the boundary of the safety command corridor, and a safety control command is output.

[0015] A second aspect of the present invention provides an adaptive peak-shaving control system for the circulating water system of a double reheat unit. The adaptive peak-shaving control system for the circulating water system of a double reheat unit includes: an acquisition module for generating a dynamic disturbance list based on real-time operating data using a dynamic disturbance observer; an optimization module for generating optimized target values ​​for condenser temperature and vacuum degree from unit load commands and environmental parameters, combined with the dynamic disturbance list; an operation module for adjusting control actions in real time based on the deviation between the optimized target values ​​and the actual system state, and using the dynamic disturbance list to generate closed-loop operation commands for the circulating water pumps and vacuum auxiliary equipment; and an allocation module for, after the execution of the closed-loop operation commands, fine-tuning the baseline strategy library for generating the optimized target values ​​online based on a comparison between the actual system response and expected economic indicators, and using the updated strategy library for subsequent control cycles.

[0016] Beneficial effects: The system classifies the various disturbance estimation information in the dynamic disturbance list and loads specific compensation logic applicable to different types of disturbances from the compensation rule base to form a differentiated compensation rule set. This can more accurately offset the impact of different types of disturbances, further improve the adaptability and control effect of the control system, and enable the condenser temperature and vacuum degree to more accurately track the optimized target value. After the closed-loop operation command is executed, the baseline strategy library for generating the optimized target value is fine-tuned online based on the comparison between the actual system response and the expected economic indicators. The updated strategy library is then used in subsequent control loops, which can be continuously optimized and improved as the system runs, always maintaining the best control effect and improving the long-term operating economy of the system. Based on the dynamic coupling relationship model of circulating water flow, condenser temperature and vacuum degree, a real-time coupling degree matrix is ​​output, and a dynamic feedforward compensator is designed using this matrix to generate dynamic decoupling feedforward commands that act on the circulating water loop and the vacuum loop respectively. This can effectively handle the coupling effects between multiple variables, avoid control interference caused by coupling, enable each control loop to operate independently and stably, and improve the overall performance of the control system. Based on the equipment operating capacity map and the allowable instantaneous power change rate of the plant power system, the impact of the future several-step change sequence of closed-loop flow control command and closed-loop vacuum control command on the equipment status and plant power is predicted. A safety command corridor is generated and used as an insurmountable hard boundary in the process of generating control commands. Within the boundary, the command combination with the lowest energy consumption is found, which provides additional safety guarantee for the operation of the control system and further reduces the risk of equipment failure and power grid fluctuations. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of an embodiment of the adaptive peak-shaving control method for the circulating water system of a secondary reheat unit according to the present invention.

[0018] Figure 2 This is a schematic diagram of an embodiment of the adaptive peak-shaving control system for the circulating water system of a secondary reheat unit in this invention. Detailed Implementation

[0019] This invention provides an adaptive peak-shaving control method and system for the circulating water system of a double reheat unit to ensure the safe and stable operation of the unit. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0020] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the adaptive peak-shaving control method for the circulating water system of a secondary reheat unit in this invention includes: 101. Based on real-time operational data, generate a dynamic disturbance list using a dynamic disturbance observer; It is understood that the executing entity of this invention can be an adaptive peak-shaving control system for the circulating water system of a secondary reheat unit, or it can be a terminal or a server; the specific implementation is not limited here. This embodiment of the invention will be described using a server as an example.

[0021] Specifically, a dimensionless dynamic model under the current operating conditions is established: based on the real-time collected condenser temperature, circulating water flow rate and vacuum degree, combined with the unit heat load and circulating water inlet temperature, an equivalent dynamic model reflecting the dynamic characteristics of the system at the current moment is constructed using a set of preset dimensionless basic differential equations, and the current parameter set of the model is output. Generate an initial disturbance estimate list: Using the current parameter set and real-time operating data as input, drive a nonlinear state observer to run; This observer estimates the unknown disturbance terms acting on the condenser thermal balance, circulating water temperature dynamics and vacuum dynamics by comparing the model calculated state with the measured state and feedback correction, and outputs an initial disturbance estimate list containing the magnitude and direction of disturbances in each subprocess. Perform online model parameter calibration: Using the model mismatch information reflected in the initial perturbation estimation list, adaptively fine-tune the current parameter set to reduce the structural bias of the model, generate an updated and more accurate system model parameter set, and use this parameter set to construct the dynamic model in the next calculation cycle. At the same time, output a final dynamic perturbation list after model calibration.

[0022] 102. Based on the unit load command and environmental parameters, and combined with the dynamic disturbance list, generate optimized target values ​​for condenser temperature and vacuum. Specifically, determine the benchmark target curve based on steady-state operating conditions: based on the current unit load command and ambient temperature parameters, query the pre-stored condenser optimal terminal difference-load relationship curve and optimal back pressure-load relationship curve, and generate a set of undisturbed benchmark temperature target values ​​and benchmark vacuum target values ​​corresponding to the current operating conditions. Calculate dynamic disturbance compensation: Analyze the estimation information on model uncertainty and external disturbance in the dynamic disturbance list, and calculate the target compensation for condenser temperature and vacuum degree required to offset the disturbance effect through a preset compensation rule library based on the type, magnitude and direction of the disturbance. Apply safe operating boundary constraints: Obtain the current equipment status and operating limits of the circulating water system, and combine them with the dynamic characteristics of the system to calculate the safe upper and lower boundaries for the instantaneous allowable changes of the target values ​​of condenser temperature and vacuum. Compare and limit the compensated target values ​​with the safe boundaries to generate a set of final optimized condenser temperature target values ​​and optimized vacuum target values ​​that meet the safety constraints, which are used for subsequent control command generation.

[0023] Furthermore, the dynamic disturbance compensation amount is calculated based on a preset compensation rule base, including: Perform multi-source disturbance classification and qualitative analysis: Analyze the disturbance estimation information in the dynamic disturbance list according to its source, time scale and direction of action, classify it into modelable slow-changing disturbances, sudden fast-changing disturbances or equipment performance degradation disturbances, and generate a disturbance classification list with qualitative descriptions. Match and load a set of differentiated compensation rules: Based on the disturbance classification list, load specific compensation logic applicable to different types of disturbances from the compensation rule library. Specifically, a feedforward steady-state offset compensation rule is used for modelable slow-changing disturbances, a dynamic suppression rule of amplitude limiting and inertial filtering is used for sudden fast-changing disturbances, and an incremental compensation rule based on an efficiency decay model is used for equipment performance degradation disturbances, thus forming a set of combined differentiated compensation rules. Based on the adaptive synthetic compensation amount of the operating mode: obtain the peak-shaving operating mode instruction of the current unit and determine whether it is in deep load reduction, rapid load increase or steady-state operation; according to the peak-shaving operating mode instruction, set the activation weight and response speed of each rule in the combined differentiated compensation rule set; finally, apply the weighted rule set to process the dynamic disturbance list, synthesize and output the condenser temperature target compensation amount and vacuum degree target compensation amount, which are used to generate the optimized target value.

[0024] 103. Based on the deviation between the optimization target value and the actual state of the system, and using the dynamic disturbance list to adjust the control action in real time, a closed-loop operation command for the circulating water pump and vacuum auxiliary equipment is generated. Specifically, the multivariate tracking deviation vector is calculated by: acquiring the target values ​​of optimized condenser temperature and optimized vacuum in real time, and comparing them with the actual condenser temperature and actual vacuum values ​​of the system collected synchronously, and calculating the real-time multivariate tracking deviation vector containing the temperature tracking deviation component and the vacuum tracking deviation component. Adaptive controller gain adjustment based on disturbance feedback: The magnitude and direction information of disturbance items in the dynamic disturbance list are analyzed, and the proportional, integral and derivative action strength of the controller used to process the multivariable tracking deviation vector is dynamically adjusted according to the preset gain scheduling rules to generate a set of updated adaptive controller gain parameters that match the current disturbance level. Synthesizing and outputting device-level control commands: The updated adaptive controller gain parameters are applied to the multivariable tracking deviation vector, and a multi-input multi-output coupled solver is used for comprehensive calculation to generate closed-loop flow control commands with anti-surge and flow smoothing constraints for adjusting the speed of the circulating water pump, and closed-loop vacuum control commands with energy-saving logic for adjusting the working state of the vacuum pump or ejector. These two commands are then sent to the corresponding field execution devices.

[0025] Furthermore, the multi-input multi-output coupled solver performs comprehensive calculations, including: Online evaluation of the coupling degree of the execution control command: Based on the dynamic coupling relationship model of the current circulating water flow, condenser temperature and vacuum degree, the mutual influence strength and direction between the closed-loop flow control command and the closed-loop vacuum control command are analyzed in real time, and a real-time coupling degree matrix representing the dynamic coupling degree between the two is calculated and output. Dynamic decoupling commands are generated based on the coupling degree matrix: A dynamic feedforward compensator is designed using the real-time coupling degree matrix; the compensator calculates in real time the amount of compensation required to offset the coupling interference caused by one of the control commands to the other controlled variable based on the change of one of the control commands, thereby generating dynamic decoupling feedforward commands that act on the circulating water loop and the vacuum loop respectively. Implement coordinated optimization and command fusion: Introduce a coordinated optimizer with the goal of minimizing the total energy consumption of the system. It accepts multivariate tracking deviation vector, updated adaptive controller gain parameters, dynamic decoupling feedforward commands, and external dynamic constraints as inputs. Under the condition of satisfying all constraints, the coordinated optimizer performs multi-objective optimization and fusion of basic feedback commands and feedforward decoupling commands, and finally outputs mutually coordinated and energy-optimized closed-loop flow control commands and closed-loop vacuum control commands.

[0026] 104. After the closed-loop operation command is executed, the baseline strategy library for generating the optimized target value is fine-tuned online based on the comparison between the actual system response and the expected economic indicators, and the updated strategy library is used for subsequent control loops.

[0027] Specifically, the calculation of the periodic performance evaluation report is as follows: After a complete control command response cycle, the actual condenser temperature, vacuum degree, circulating water pump power consumption and unit heat rate of the system are collected and compared with the expected economic indicators set before the execution of the closed-loop operation command. The results are then quantified and a periodic performance evaluation report containing the target tracking accuracy and the actual energy consumption deviation is output. Analysis of perturbation attribution for performance deviations: The performance deviation information in the periodic performance evaluation report is correlated with the dynamic perturbation list used when generating the optimization target value. The deviations caused by insufficient compensation for modelable perturbations and those caused by unforeseen new types of perturbations are distinguished, and a perturbation attribution analysis result containing the classification and weight of deviation causes is formed. Generate and apply strategy correction amount: Based on the disturbance attribution analysis results, through a preset set of empirical correction rules without machine learning parameters, calculate the fine adjustment amount for the corresponding load point or ambient temperature range in the benchmark target curve; use this fine adjustment amount as the strategy correction amount to update the benchmark strategy library used to query the optimal terminal difference-load relationship curve and the optimal back pressure-load relationship curve of the condenser online and incrementally. Locking and applying the updated strategy: Perform consistency verification and smoothing on the updated baseline strategy library, and lock the updated strategy library that passes the verification as the official version used to generate the optimization target value in the next control cycle, thereby completing the adaptive rolling optimization closed loop of the control strategy.

[0028] 105. Establish a system-level disturbance responsibility partition list: Based on the content of the dynamic disturbance list and the system topology, the identified disturbances are classified and assigned to the condenser sub-region, the circulating water loop sub-region, and the vacuum system sub-region according to their physical source and scope of influence, generating a disturbance responsibility partition list that clearly defines the responsibility boundaries of each subsystem. Implement cross-subsystem adaptive coordinated control: Based on the disturbance responsibility zone list, configure differentiated control parameter update logic for the three control subsystems of condenser temperature control, circulating water flow control and vacuum control, and introduce a central coordinator to dynamically adjust the priority and response speed of the control commands of each subsystem according to the disturbance severity of each sub-region in the list, forming a coordinated global control strategy. External equipment operating boundary conditions are introduced and processed: the operating status and health indicators of key auxiliary equipment such as circulating water pumps and vacuum pumps, as well as the allowable power fluctuation range of the plant power bus, are collected in real time and transformed into external dynamic constraints of the control system; when processing multivariable tracking deviation vectors and generating equipment-level control commands, the external dynamic constraints are used as mandatory boundaries for online optimization and solution to ensure that the output closed-loop operation commands are always within the operating envelope allowed by equipment safety and the power grid.

[0029] Specifically, external dynamic constraints are used as mandatory boundaries for online optimization, including: constructing a dynamic equipment operation capability map: based on real-time collected bearing temperature, vibration amplitude, current, and cumulative operating time of key auxiliary equipment, combined with pre-stored equipment design performance curves and aging models, the maximum allowable speed, maximum allowable power, and shortest safe start-stop interval of the circulating water pump and vacuum pump in the next control cycle are calculated and output in real time, forming a dynamically updated equipment operation capability map; generating a rolling time domain safety command corridor: based on the equipment operation capability map and combined with the allowable instantaneous power change rate of the plant power system, for several future steps of the closed-loop flow control command and closed-loop vacuum control command... The algorithm generates a sequence of control commands to predict their impact on equipment status and power supply. Through forward recursion, it defines a range of command variations that are safe for equipment to execute and acceptable to the power grid within the rolling time domain, generating a multi-dimensional safety command corridor. Within this corridor, constraint optimization is performed: the safety command corridor is treated as an insurmountable hard boundary during the generation of closed-loop flow control and vacuum control commands. The algorithm prioritizes the rapid convergence of the multivariable tracking deviation vector, searching for the command combination with the lowest energy consumption within the boundary of the safety command corridor. This results in a set of safety control commands that meet both tracking performance requirements and strictly comply with all real-time constraints of equipment and the power grid.

[0030] Please see Figure 2 Another embodiment of the adaptive peak-shaving control method for the circulating water system of a secondary reheat unit in this invention includes: an acquisition module 201, used to generate a dynamic disturbance list based on real-time operating data through a dynamic disturbance observer; an optimization module 202, used to generate optimized target values ​​for condenser temperature and vacuum degree from the unit load command and environmental parameters, combined with the dynamic disturbance list; an operation module 203, used to adjust the control action in real time based on the deviation between the optimized target value and the actual system state, and using the dynamic disturbance list, to generate closed-loop operation commands for the circulating water pump and vacuum auxiliary equipment; and an allocation module 204, used to fine-tune the benchmark strategy library for generating optimized target values ​​online after the closed-loop operation commands are executed, based on the comparison between the actual system response and expected economic indicators, and use the updated strategy library for subsequent control cycles.

[0031] The present invention also provides an adaptive peak-shaving control device for the circulating water system of a double reheat unit. The adaptive peak-shaving control device for the circulating water system of a double reheat unit includes a memory and a processor. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor performs the steps of the adaptive peak-shaving control method for the circulating water system of a double reheat unit described in the above embodiments.

[0032] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the adaptive peak-shaving control method for the circulating water system of a secondary reheat unit.

[0033] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0034] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0035] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An adaptive peak-shaving control method for the circulating water system of a double reheat unit, characterized in that, include: A dynamic disturbance list is generated based on real-time operational data through a dynamic disturbance observer; Based on the unit load command and environmental parameters, and in conjunction with the dynamic disturbance list, optimized target values ​​for condenser temperature and vacuum are generated. Based on the deviation between the optimized target value and the actual state of the system, and using the dynamic disturbance list to adjust the control action in real time, closed-loop operation commands for the circulating water pump and vacuum auxiliary equipment are generated. After the closed-loop operation command is executed, the baseline strategy library for generating the optimized target value is fine-tuned online based on the comparison between the actual system response and the expected economic indicators, and the updated strategy library is used for subsequent control loops.

2. The adaptive peak-shaving control method for the circulating water system of a double reheat unit according to claim 1, characterized in that, include: Based on real-time collected condenser temperature, circulating water flow rate and vacuum degree, combined with unit heat load and circulating water inlet temperature, an equivalent dynamic model reflecting the dynamic characteristics of the system at the current moment is constructed using preset basic differential equations, and the current parameter set is output. Based on the current parameter set and real-time operating data, a nonlinear state observer is driven to run; by comparing and correcting the calculated state with the measured state, the unknown disturbance terms acting on the condenser thermal balance, circulating water temperature dynamics and vacuum dynamics are estimated, and an initial disturbance estimate list is output. Using the model mismatch information reflected in the initial disturbance estimation list, the current parameter set is adaptively fine-tuned to generate a system model parameter set. This parameter set is then used to construct the dynamic model in the next calculation cycle, and a dynamic disturbance list is output.

3. The adaptive peak-shaving control method for the circulating water system of a double reheat unit according to claim 2, characterized in that, include: Based on the current unit load command and ambient temperature parameters, query the pre-stored condenser optimal terminal differential-load relationship curve and optimal back pressure-load relationship curve, and generate the reference temperature target value and reference vacuum target value. Analyze the estimation information about model uncertainty and external disturbances in the dynamic disturbance list, and calculate the target compensation amount of condenser temperature and vacuum degree required to offset the influence of the disturbances using a preset compensation rule library; The current equipment status and operating limits of the circulating water system are obtained. Combined with the dynamic characteristics of the system, the safe upper and lower boundaries for instantaneous allowable changes in the target values ​​of condenser temperature and vacuum are calculated. The compensated target values ​​are compared with the safe boundaries and subjected to amplitude limiting to generate optimized condenser temperature target values ​​and optimized vacuum target values.

4. The adaptive peak-shaving control method for the circulating water system of a double reheat unit according to claim 3, characterized in that, include: A disturbance classification list is generated based on the disturbance estimation information for each item in the dynamic disturbance list; Based on the disturbance classification list, specific compensation logic applicable to different types of disturbances is loaded from the compensation rule base to form a differentiated compensation rule set; Obtain the peak-shaving operation mode command of the current unit, determine whether it is in deep load reduction, rapid load increase or steady-state operation, and output the target compensation amount of condenser temperature and vacuum degree to generate optimized target values.

5. The adaptive peak-shaving control method for the circulating water system of a double reheat unit according to claim 3, characterized in that, include: The system acquires the target values ​​of optimized condenser temperature and optimized vacuum in real time, and compares them with the actual condenser temperature and actual vacuum values ​​collected synchronously to calculate the real-time multivariate tracking deviation vector. The amplitude and direction information of the disturbance items in the dynamic disturbance list are analyzed, and the proportional, integral and derivative action strength of the controller used to process the multivariable tracking deviation vector is dynamically adjusted according to the preset gain scheduling rules to generate adaptive controller gain parameters. The updated adaptive controller gain parameters are applied to the multivariable tracking deviation vector and a comprehensive calculation is performed to generate closed-loop flow control commands and closed-loop vacuum control commands, respectively.

6. The adaptive peak-shaving control method for the circulating water system of a double reheat unit according to claim 5, characterized in that, include: Based on the dynamic coupling relationship model of the current circulating water flow rate, condenser temperature and vacuum degree, output the real-time coupling degree matrix; Using the real-time coupling matrix, a dynamic feedforward compensator is designed to generate dynamic decoupling feedforward commands that act on the circulating water loop and the vacuum loop respectively. With the goal of minimizing total system energy consumption, the system simultaneously accepts the multivariable tracking deviation vector, the updated adaptive controller gain parameters, the dynamic decoupling feedforward command, and the external dynamic constraints, and outputs closed-loop flow control commands and closed-loop vacuum control commands.

7. The adaptive peak-shaving control method for the circulating water system of a double reheat unit according to claim 1, characterized in that, Also includes: Based on the content of the dynamic disturbance list and the system topology, the identified disturbances are classified and assigned to the condenser sub-region, the circulating water loop sub-region, and the vacuum system sub-region according to their physical source and scope of influence, thus generating a disturbance responsibility zone list. Based on the disturbance responsibility zone list, differentiated control parameter update logic is configured for the three control subsystems of condenser temperature control, circulating water flow control and vacuum control to form a coordinated global control strategy. The system collects the operating status and health indicators of key auxiliary equipment and the allowable power fluctuation range of the plant power bus in real time, and converts them into external dynamic constraints for the control system. When processing the multivariable tracking deviation vector and generating device-level control commands, the external dynamic constraints are used as mandatory boundaries for online optimization to ensure that the output closed-loop operation commands are always within the operating envelope that is safe for the device and permissible by the power grid.

8. The adaptive peak-shaving control method for the circulating water system of a double reheat unit according to claim 7, characterized in that, include: Based on the real-time collected bearing temperature, vibration amplitude, current and cumulative operating time of key auxiliary equipment, combined with the pre-stored equipment design performance curves and aging models, an equipment operating capacity map is obtained. Based on the equipment operation capability map and combined with the allowable instantaneous power change rate of the plant power system, the impact of the closed-loop flow control command and closed-loop vacuum control command on the equipment status and plant power is predicted for the future several-step change sequence, and a safety command corridor is generated. In the process of generating the closed-loop flow control command and the closed-loop vacuum control command, the safety command corridor is regarded as an insurmountable hard boundary. Within the boundary of the safety command corridor, the command combination with the lowest energy consumption is searched, and the safety control command is output.

9. An adaptive peak-shaving control system for the circulating water system of a double reheat unit, characterized in that, The adaptive peak-shaving control system for the circulating water system of the secondary reheat unit includes: The acquisition module is used to generate a dynamic disturbance list based on real-time runtime data through a dynamic disturbance observer; The optimization module is used to generate optimized target values ​​for condenser temperature and vacuum based on unit load commands and environmental parameters, combined with the dynamic disturbance list. The operation module is used to adjust the control action in real time based on the deviation between the optimization target value and the actual state of the system, and to generate closed-loop operation instructions for the circulating water pump and vacuum auxiliary equipment using the dynamic disturbance list. The allocation module is used to fine-tune the baseline strategy library for generating optimized target values ​​online based on the comparison between the actual system response and the expected economic indicators after the closed-loop operation command is executed, and to use the updated strategy library for subsequent control loops.

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