Optimization method and device of control system, electronic equipment and storage medium

Abstract

The invention discloses an optimization method and device of a control system, electronic equipment and a storage medium, and relates to the technical field of power system control. And the coordination control strategy is dynamically adjusted based on the first operation parameter generated through optimization so as to deal with the energy matching unbalance problem of the steam turbine and the boiler, and meanwhile, the non-catalytic reduction system is controlled by combining the first operation parameter and the second operation parameter. The problems that in an existing thermal power generating unit control method, due to the fact that traditional control and a single feedback mechanism are adopted, machine-furnace coordination is poor, the ammonia spraying amount is not adjusted in time, and primary frequency modulation response is delayed can be solved, and the technical effects of improving unit operation stability, economical efficiency and environment-friendly standard reaching capacity are achieved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of power system control, and particularly relates to a control system optimization method and device, electronic equipment and storage medium. BACKGROUND

[0002] As an important regulating unit of the power system, thermal power generating units are widely used in key links such as power grid frequency modulation, load response and environmental protection control. With the transformation of energy structure and increasingly stringent environmental protection requirements, unit control technology gradually evolves towards intelligence and collaboration.

[0003] At present, in the control method of thermal power generating units, the traditional proportional-integral-derivative control and single feedback mechanism are directly used, and the dynamic matching relationship between the boiler and the steam turbine and the measurement lag characteristic of the selective non-catalytic reduction system are not fully considered, which may cause problems such as poor coordination between the boiler and the steam turbine, untimely ammonia injection amount adjustment, and primary frequency modulation response delay, thereby affecting the stability, economy and environmental protection compliance ability of the unit operation. SUMMARY

[0004] The present disclosure provides a control system optimization method, device, electronic equipment and storage medium. The main purpose is to solve the problems of poor coordination between the boiler and the steam turbine, untimely ammonia injection amount adjustment, primary frequency modulation response delay, and the like, thereby affecting the stability, economy and environmental protection compliance ability of the unit operation.

[0005] According to a first aspect of the present disclosure, a control system optimization method is provided, which comprises: In response to a unit load change instruction, the core control logic is optimized by a control logic optimization module to generate a first operating parameter; When the energy matching between the steam turbine and the boiler is unbalanced, the coordinated control strategy is dynamically adjusted based on the first operating parameter to generate a second operating parameter; In the case where it is determined that there is a control demand for the non-catalytic reduction system, the control of the non-catalytic reduction system is performed according to the first operating parameter and the second operating parameter.

[0006] Optionally, the core control logic comprises at least one of main auxiliary unit protection logic, air and flue gas system logic, feedwater system logic, and main reheat temperature control logic; and the first group of operating parameters comprises at least one of main steam pressure, air and flue gas system flow, feedwater flow, and main reheat steam temperature.

[0007] Optionally, in the case where it is determined that there is a control demand for the non-catalytic reduction system, the control of the non-catalytic reduction system is performed according to the first operating parameter and the second operating parameter, which comprises: When the operating parameters of the non-catalytic reduction system change or the ammonia slip increases, it is determined that there is a control requirement for the non-catalytic reduction system. A multi-dimensional predictive control model is constructed based on the first and second operating parameters to generate a basic adjustment command for the amount of urea sprayed. The basic regulation command is corrected based on ammonia slip as a feedforward signal. Adjust the urea injection time and injection capacity based on the aforementioned basic adjustment commands.

[0008] Optionally, the method further includes: In response to the primary frequency regulation command, a primary frequency regulation correction command is generated based on the second operating parameters and the frequency and power signals of the steam turbine; The primary frequency regulation correction command is optimized based on the flow characteristic model, and the turbine is regulated based on the optimized primary frequency regulation correction.

[0009] Optionally, optimizing the primary frequency regulation correction command based on the flow characteristic model further includes: Collect valve characteristic curve data under different operating conditions; The support vector machine algorithm is applied to fit nonlinear flow characteristics; A dynamic correction factor is introduced to compensate for the effects of valve wear.

[0010] According to a second aspect of this disclosure, an optimization apparatus for a control system is provided, comprising: The optimization unit is used to respond to the unit load change command by optimizing the core control logic through the control logic optimization module to generate the first operating parameters; The first generation unit is used to dynamically adjust the coordination control strategy based on the first operating parameters and generate the second operating parameters when the energy matching between the steam turbine and the boiler is unbalanced. An execution unit is configured to, when it is determined that there is a control requirement for the non-catalytic reduction system, perform control on the non-catalytic reduction system according to the first operating parameters and the second operating parameters.

[0011] Optionally, the core control logic includes at least one of the following: main and auxiliary machine protection logic, flue gas system logic, feedwater system logic, and main reheat temperature control logic; the first set of operating parameters includes at least one of the following: main steam pressure, flue gas system flow rate, feedwater flow rate, and main reheat steam temperature.

[0012] Optionally, the execution unit is further configured to: When the operating parameters of the non-catalytic reduction system change or the ammonia slip increases, it is determined that there is a control requirement for the non-catalytic reduction system. A multi-dimensional predictive control model is constructed based on the first and second operating parameters to generate a basic adjustment command for the amount of urea sprayed. The basic regulation command is corrected based on ammonia slip as a feedforward signal. Adjust the urea injection time and injection capacity based on the aforementioned basic adjustment commands.

[0013] Optionally, the device further includes: The second generation unit is used to generate a primary frequency regulation correction command in response to the primary frequency regulation command, based on the second operating parameters and the frequency and power signals of the steam turbine. The regulating unit is used to optimize the primary frequency regulation correction command based on the flow characteristic model, and to perform regulation on the steam turbine based on the optimized primary frequency regulation correction.

[0014] Optionally, the execution unit is further configured to: Collect valve characteristic curve data under different operating conditions; The support vector machine algorithm is applied to fit nonlinear flow characteristics; A dynamic correction factor is introduced to compensate for the effects of valve wear.

[0015] According to a third aspect of this disclosure, an electronic device is provided, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect above.

[0016] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect above.

[0017] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0018] The optimization method, apparatus, electronic equipment, and storage medium of the control system disclosed herein mainly include the following technical solutions: responding to unit load change commands, optimizing the core control logic through a control logic optimization module to generate first operating parameters; when there is an energy mismatch between the turbine and boiler, dynamically adjusting the coordinated control strategy based on the first operating parameters to generate second operating parameters; and, when it is determined that there is a control requirement for the non-catalytic reduction system, executing control of the non-catalytic reduction system according to the first and second operating parameters. Through this application, by using a control logic optimization module to optimize the core control logic and dynamically adjusting the coordinated control strategy based on the optimized first operating parameters to address the energy mismatch between the turbine and boiler, and simultaneously controlling the non-catalytic reduction system in conjunction with the first and second operating parameters, the problems of poor turbine-boiler coordination, untimely ammonia injection adjustment, and delayed primary frequency regulation response caused by the use of traditional control and single feedback mechanisms in existing thermal power unit control methods can be solved, achieving the technical effect of improving unit operating stability, economy, and environmental compliance.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0020] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein: Figure 1 A flowchart illustrating a method for optimizing a control system provided in an embodiment of this disclosure; Figure 2 A schematic diagram of the structure of an optimization device for a control system provided in an embodiment of this disclosure; Figure 3 A schematic diagram of the structure of another optimization device for a control system provided in an embodiment of this disclosure; Figure 4 A schematic block diagram of an example electronic device provided for embodiments of this disclosure. Detailed Implementation

[0021] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0022] The following description, with reference to the accompanying drawings, outlines an optimization method, apparatus, electronic device, and storage medium for a control system according to embodiments of the present disclosure.

[0023] Figure 1 This is a flowchart illustrating a method for optimizing a control system provided in an embodiment of this disclosure.

[0024] like Figure 1 As shown, the method includes the following steps: Step 101: In response to the unit load change command, the core control logic is optimized through the control logic optimization module to generate the first operating parameters; When the system receives a load change command from the unit, the control logic optimization module is triggered. This module performs targeted optimizations on the core control logic during boiler operation. This core control logic encompasses several key components, including protection logic, flue gas system logic, feedwater system logic, main and reheat temperature control logic, main and auxiliary unit protection logic, and other major regulating loop logic. Optimizing these logics allows the system to better adapt to load changes, thereby generating initial operating parameters. Optimizing the protection logic ensures safe operation of the unit during load fluctuations, preventing false protection triggers due to parameter fluctuations. Optimizing the flue gas system logic ensures the required airflow for combustion, maintaining reasonable oxygen levels and combustion efficiency. Optimizing the feedwater system allows for timely adjustment of feedwater flow based on load changes, maintaining the balance of the steam-water system. Optimizing the main and reheat temperature control system ensures that the steam temperature remains stable within a reasonable range during load changes. These optimized logics work together to generate initial operating parameters that are derived from a comprehensive consideration of the operating status of each system and the demands of load changes. This provides an accurate basis for subsequent unit operation adjustments, achieving the goal of automatic control.

[0025] Step 102: When the energy matching between the steam turbine and the boiler is unbalanced, the coordination control strategy is dynamically adjusted based on the first operating parameters to generate the second operating parameters; When an energy mismatch occurs between the turbine and boiler during unit operation (usually caused by factors such as unit load changes and AGC command adjustments), the system will initiate a dynamic coordination control strategy adjustment mechanism based on the first operating parameters (i.e., the set of parameters output after the control logic optimization module optimizes the core control logic such as protection logic, flue gas system logic, and feedwater system logic, including the basic parameter values ​​for coordinated operation of each system). Specifically, this mechanism will analyze the energy deviation between the current turbine steam intake and the boiler heat load output in real time, combine the optimization results of steam and water parameters (such as main steam pressure, temperature, and flow rate) in the first operating parameters, and the dynamic feedback data of equipment heat exchange characteristics under the current operating conditions, and use an adaptive control algorithm to correct the turbine-boiler coordinated control strategy in real time. For example, when the turbine needs to increase steam intake due to increased load, if the boiler-side heat load response lags, leading to energy imbalance, the system will synchronously adjust the boiler fuel supply, feedwater flow, and flue gas flow ratio based on the feedwater system optimization parameters and main / reheat temperature control parameters in the first operating parameters. By enhancing the adaptive adjustment capability of the coordinated control strategy, the system enables the boiler heat load output to quickly match the turbine energy demand. During this process, the system will comprehensively calculate and generate a second set of operating parameters. This parameter set includes real-time correction values ​​for key adjustment quantities such as turbine valve opening, boiler fuel quantity, feedwater flow, and secondary air ratio. Through a closed-loop control loop, the system gradually reduces parameter deviations under energy imbalance conditions, ultimately achieving dynamic energy balance between the boiler and turbine. This ensures that the unit can maintain stable operation under load fluctuations and enhances the adaptability of the coordinated control strategy to complex operating conditions.

[0026] Step 103: If it is determined that there is a control requirement for the non-catalytic reduction system, control of the non-catalytic reduction system is performed according to the first operating parameter and the second operating parameter.

[0027] When a control requirement is identified for the Selective Non-Catalytic Reduction (SNCR) system (such as changes in unit load, adjustments to environmental emission standards, or fluctuations in inlet NOx concentration), the system will initiate a composite control process for the SNCR system based on a first set of operating parameters (including protection parameters, flue gas system parameters, and feedwater parameters optimized by control logic) and a second set of operating parameters (dynamically adjusted boiler-turbine coordination control parameters, such as turbine valve opening and boiler fuel quantity correction values). Specifically, firstly, based on the main in-furnace operating parameters in the first set of operating parameters, such as unit load, total air volume, total coal volume, secondary damper opening, bed temperature, and oxygen content, an SNCR inlet NOx concentration prediction loop is constructed. This loop dynamically predicts NOx generation using real-time collected operating data, achieving an advanced response for denitrification regulation. Simultaneously, a full-scale feedforward loop is constructed using the same parameters, enabling the control loop to sense changes in unit operating conditions in real time. For example, when the total coal volume increases, the feedforward loop immediately triggers a pre-adjustment of the urea injection quantity, reducing control lag.

[0028] Secondly, ammonia slip is introduced as a feedforward signal into the control logic. When an increase in ammonia slip is detected, the system automatically reduces the amount of urea injected to prevent catalyst poisoning or equipment corrosion caused by excessive ammonia injection. Furthermore, by combining optimized steam-water signals (such as main steam pressure and temperature) from the first operating parameter and load feedback data after boiler-turbine energy matching from the second operating parameter, and using field step test data on urea injection valve and urea injection amount versus outlet NOx, a mathematical transfer model of the SNCR system at different load segments is constructed. Inertia time and delay time parameters are obtained, and an adaptive Smith compensation loop is then constructed to overcome the control delay problem caused by system measurement lag.

[0029] Finally, a neural network PID controller is adopted, using the measured NOx concentration at the environmental protection outlet as the primary and controlled variable. Referring to the expected environmental indicators in the first operating parameter and the load dynamic change rate in the second operating parameter, reasonable control parameter boundaries and evaluation mechanisms are set. The PID parameters are self-tuned through a neural network algorithm. When the load is relatively stable, economical ammonia injection operation based on the optimal ammonia-nitrogen molar ratio curve is achieved (for example, by constructing a new denitrification efficiency-ammonia-nitrogen molar ratio curve to determine the optimal value for coarse urea adjustment). When the load fluctuates significantly, the synergistic effect of feedforward and Smith compensation ensures that the outlet NOx concentration does not exceed the standard, ultimately achieving efficient and precise control of the SNCR system over a wide load range.

[0030] In some embodiments, the core control logic includes at least one of the main and auxiliary machine protection logic, flue gas system logic, feedwater system logic, and main reheat temperature control logic; the first set of operating parameters includes at least one of the main steam pressure, flue gas system flow rate, feedwater flow rate, and main reheat steam temperature.

[0031] The main and auxiliary equipment protection logic is used to ensure the safe operation of the unit's main and auxiliary equipment. By setting reasonable protection thresholds and action conditions, it can trigger protection actions in a timely manner when equipment malfunctions, thus preventing equipment damage. The flue gas system logic mainly involves controlling the amount of air required for boiler combustion, the amount of flue gas emitted, and the opening of each damper to maintain stable combustion conditions and a reasonable oxygen distribution. The feedwater system logic precisely adjusts the feedwater flow based on changes in unit load and steam parameters to ensure that the boiler drum water level is within the normal range and to maintain the balance of the steam-water system. The main and reheat temperature control logic is used to control the temperature of main steam and reheat steam. By adjusting combustion conditions and spraying water for desuperheating, it stabilizes the steam temperature near the design value, ensuring the safe and economical operation of the turbine.

[0032] The first set of operating parameters includes at least one of the following: main steam pressure, flue gas system flow rate, feedwater flow rate, and main reheat steam temperature. Main steam pressure is a key parameter reflecting the balance between boiler energy output and turbine energy demand; its stability directly affects the unit's load response capability and operational safety. Flue gas system flow rate, including forced draft and induced draft, directly relates to boiler combustion efficiency and stability; a reasonable flue gas system flow rate ensures complete fuel combustion and reduces pollutant emissions. Feedwater flow rate is an important parameter for maintaining boiler drum water level and steam output, and needs to be adjusted in real time according to unit load and steam flow rate. Main reheat steam temperature is a key indicator affecting turbine efficiency and lifespan; a stable main reheat steam temperature ensures the turbine operates under optimal conditions, improving the unit's economy and reliability.

[0033] In some embodiments, when it is determined that there is a control requirement for the non-catalytic reduction system, performing control of the non-catalytic reduction system according to the first operating parameter and the second operating parameter includes: When the operating parameters of the non-catalytic reduction system change or the ammonia slip increases, it is determined that there is a control requirement for the non-catalytic reduction system. A multi-dimensional predictive control model is constructed based on the first and second operating parameters to generate a basic adjustment command for the amount of urea sprayed. The basic regulation command is corrected based on ammonia slip as a feedforward signal. Adjust the urea injection time and injection capacity based on the aforementioned basic adjustment commands.

[0034] In some embodiments, when the operating parameters of the non-catalytic reduction system (SNCR) (such as unit load, total air volume, total coal volume, secondary damper opening, bed temperature, oxygen content, etc.) change, or when an increase in ammonia slip is detected, the system determines that there is a control requirement for SNCR. At this time, a multi-dimensional predictive control model is constructed based on the first operating parameters (including parameters optimized by the core control logic such as main steam pressure, flue gas system flow rate, feedwater flow rate, and main reheat steam temperature) and the second operating parameters (dynamically adjusted turbine control damper opening and boiler fuel quantity correction value when the energy matching between the turbine and boiler is unbalanced).

[0035] This model integrates furnace operating parameters and boiler-generator coordination parameters. By analyzing multi-dimensional data such as the impact of unit load changes on NOx generation, the correlation between flue gas system flow and combustion state, and the effect of main steam pressure on steam-water energy distribution, it generates basic adjustment commands for urea injection.

[0036] Ammonia slip is used as a feedforward signal in the control logic. When ammonia slip increases, the system automatically corrects the basic adjustment command negatively, reducing the amount of urea injected to prevent excessive ammonia injection. If the ammonia slip is within a reasonable range, the basic command is maintained or fine-tuned. The corrected command is used to adjust the urea injection time and injection capacity: during the stable load phase, the injection time is extended and the capacity is precisely controlled according to the optimal ammonia-nitrogen molar ratio curve to achieve economical ammonia injection; when rapid load changes cause fluctuations in operating parameters, the denitrification response speed is improved by shortening the injection interval and increasing the instantaneous injection capacity to ensure that the outlet NOx concentration does not exceed the standard, thereby achieving precise control and optimized operation of the SNCR system within a wide load range.

[0037] In some embodiments, the method further includes: In response to the primary frequency regulation command, a primary frequency regulation correction command is generated based on the second operating parameters and the frequency and power signals of the steam turbine; The primary frequency regulation correction command is optimized based on the flow characteristic model, and the turbine is regulated based on the optimized primary frequency regulation correction.

[0038] When the system receives a primary frequency regulation command from the power grid (i.e., the power grid frequency deviates from the rated value, triggering the unit's automatic frequency regulation requirement), it will initiate a primary frequency regulation correction command generation mechanism based on the second operating parameters (including parameters such as the turbine valve opening and boiler fuel quantity correction value after dynamic adjustment when the turbine and boiler energy matching is unbalanced), and the frequency and power signals collected in real time on the turbine side (obtained through a co-source device installed at the power plant's PMU, which can achieve co-source acquisition and transmission of frequency and power signals). Specifically, the system will perform co-source correction on the high-precision frequency signal uploaded by the PMU device and the load feedback signal in the DCS system. By comparing the deviation values ​​of the two and combining them with the turbine-boiler coordination status data in the second operating parameters, the system calculates the load correction amount used to compensate for frequency fluctuations, and then generates a primary frequency regulation correction command. The generation process of this command needs to address issues such as insufficient reliability and large signal deviation of the PMU signal. By setting multiple filtering algorithms and disturbance-free switching logic, it ensures that the control loop can still maintain safe and stable operation when the signal is abnormal.

[0039] After generating the correction command, the system optimizes the command based on a pre-built turbine regulating valve flow characteristic model. Since the regulating valve flow rate depends not only on the valve opening but also on the upstream main steam pressure (for example, when the main steam pressure fluctuates, the steam flow rate at the same opening will change, causing the actual regulating effect of the valve to differ from the command expectation), the regulating valve opening command in the correction command needs to be dynamically corrected using the flow characteristic model. This model establishes a nonlinear mapping relationship by collecting regulating valve opening-flow data under different main steam pressure conditions. When a primary frequency regulation correction command is received, the model reads the current main steam pressure value in real time and performs compensation calculations on the original opening command based on the pressure-flow characteristic curve to obtain the optimized regulating valve opening command. Finally, the system drives the turbine regulating valve based on the optimized primary frequency regulation correction command, rapidly responding to grid frequency changes by precisely adjusting the steam inlet flow. This not only improves the primary frequency regulation qualification rate but also improves the primary frequency regulation index and AGC (Automatic Generation Control) index under large disturbance conditions, achieving coordinated optimization of grid frequency stability and unit load regulation.

[0040] In some embodiments, optimizing the primary frequency modulation correction instruction based on the flow characteristic model further includes: Collect valve characteristic curve data under different operating conditions; The support vector machine algorithm is applied to fit nonlinear flow characteristics; A dynamic correction factor is introduced to compensate for the effects of valve wear.

[0041] During the actual operation of the turbine control valves, the DCS system is used to collect valve flow data in real time under different main steam pressures (e.g., 8MPa to 16MPa) and different valve openings (0% to 100%) across the entire load range (e.g., 30% BMCR to 100% BMCR). During data collection, related parameters such as unit load, main steam temperature, and pressure difference across the control valves are recorded simultaneously to form a multi-dimensional operational dataset. For example, under high load conditions, the focus is on collecting the flow-opening curve at the large valve opening; under low load conditions, the nonlinear characteristics at the small opening are considered, ensuring that the data covers the entire operating range of the unit and providing reliable foundational data for subsequent model construction.

[0042] Because the relationship between the flow rate and opening degree of the control valve is highly nonlinear due to the influence of parameters such as main steam pressure and temperature (e.g., the flow rate change is greater for the same opening degree under high pressure conditions), traditional linear models are difficult to accurately describe. This paper describes a nonlinear flow characteristic prediction model constructed by training the collected multidimensional data using the SVM algorithm and employing the radial basis function (RBF) as the kernel function to handle high-dimensional nonlinear mapping problems. The model uses valve opening degree, main steam pressure, and temperature as input variables and actual flow rate as the output variable. This model can predict the actual flow rate of the control valve in real time based on current operating parameters. For example, when the main steam pressure increases from 10 MPa to 12 MPa, the model can automatically correct the flow rate calculation value under the same opening degree, resolving the flow rate calculation deviation caused by pressure fluctuations and making the valve opening command in the primary frequency regulation correction command more closely match the actual flow rate requirement.

[0043] Wear and tear on valves over long-term operation can lead to deviations in flow characteristics (e.g., internal leakage increases flow rate at the same valve opening). Therefore, real-time online monitoring is necessary to assess the wear status. Specifically, a wear assessment model is established by collecting vibration signals from the valve actuator and characteristic parameters such as pressure fluctuations before and after the valve, generating a dynamic correction factor. When valve wear exceeds a threshold (e.g., by identifying wear characteristic frequencies on the sealing surface through vibration spectrum analysis), the correction factor automatically compensates for the predictions of the SVM model. For example, a correction coefficient proportional to the wear amount is superimposed on the valve opening command, ensuring that even with valve wear, the flow characteristic model accurately reflects the actual regulation effect. This avoids over- or under-regulation of the primary frequency response due to wear, improving the long-term stability and accuracy of the control strategy.

[0044] Corresponding to the above-described optimization method for the control system, this invention also proposes an optimization device for the control system. Since the device embodiments of this invention correspond to the above-described method embodiments, details not disclosed in the device embodiments can be referred to the above-described method embodiments, and will not be repeated here.

[0045] Figure 2This is a schematic diagram of the structure of an optimization device for a control system provided in an embodiment of the present disclosure, as shown below. Figure 2 As shown, it includes: Optimization unit 21 is used to respond to unit load change commands by optimizing the core control logic through the control logic optimization module to generate the first operating parameters; The first generation unit 22 is used to dynamically adjust the coordination control strategy based on the first operating parameters and generate the second operating parameters when the energy matching between the steam turbine and the boiler is unbalanced. The execution unit 23 is used to control the non-catalytic reduction system according to the first operating parameters and the second operating parameters when it is determined that there is a control requirement for the non-catalytic reduction system.

[0046] Furthermore, in one possible implementation of this disclosure embodiment, the core control logic includes at least one of the following: main and auxiliary machine protection logic, flue gas system logic, feedwater system logic, and main reheat temperature control logic; the first set of operating parameters includes at least one of the following: main steam pressure, flue gas system flow rate, feedwater flow rate, and main reheat steam temperature.

[0047] Furthermore, in one possible implementation of this disclosure, the execution unit 23 is further configured to: When the operating parameters of the non-catalytic reduction system change or the ammonia slip increases, it is determined that there is a control requirement for the non-catalytic reduction system. A multi-dimensional predictive control model is constructed based on the first and second operating parameters to generate a basic adjustment command for the amount of urea sprayed. The basic regulation command is corrected based on ammonia slip as a feedforward signal. Adjust the urea injection time and injection capacity based on the aforementioned basic adjustment commands.

[0048] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 3 As shown, the device further includes: The second generation unit 24 is used to generate a primary frequency regulation correction command in response to the primary frequency regulation command, based on the second operating parameters and the frequency and power signals of the steam turbine. The regulating unit 25 is used to optimize the primary frequency regulation correction command based on the flow characteristic model, and to perform regulation on the steam turbine based on the optimized primary frequency regulation correction.

[0049] Furthermore, in one possible implementation of this disclosure, the execution unit 23 is further configured to: Collect valve characteristic curve data under different operating conditions; The support vector machine algorithm is applied to fit nonlinear flow characteristics; A dynamic correction factor is introduced to compensate for the effects of valve wear.

[0050] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of the embodiments of this disclosure, and the principle is the same. Therefore, the embodiments of this disclosure are not limited thereto.

[0051] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0052] Figure 4 A schematic block diagram of an example electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0053] like Figure 4 As shown, device 300 includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 302 or a computer program loaded from storage unit 308 into RAM (Random Access Memory) 303. RAM 303 may also store various programs and data required for the operation of device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via bus 304. I / O (Input / Output) interface 305 is also connected to bus 304.

[0054] Multiple components in device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of monitors, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0055] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as optimization methods for control systems. For example, in some embodiments, the optimization methods for control systems may be implemented as computer software programs tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the methods described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform the aforementioned optimization method of the control system by any other suitable means (e.g., by means of firmware).

[0056] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0057] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0058] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0059] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0060] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0061] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system that addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0062] It's important to note that artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0063] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0064] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. An optimization method for a control system, characterized in that, include: In response to the unit load change command, the core control logic is optimized through the control logic optimization module to generate the first operating parameters; When the energy matching between the steam turbine and the boiler is unbalanced, the coordination control strategy is dynamically adjusted based on the first operating parameter to generate the second operating parameter; If it is determined that there is a control requirement for the non-catalytic reduction system, control of the non-catalytic reduction system is performed according to the first operating parameter and the second operating parameter.

2. The optimization method for the control system according to claim 1, characterized in that, The core control logic includes at least one of the following: main and auxiliary machine protection logic, flue gas system logic, feedwater system logic, and main reheat temperature control logic; the first set of operating parameters includes at least one of the following: main steam pressure, flue gas system flow rate, feedwater flow rate, and main reheat steam temperature.

3. The optimization method for the control system according to claim 1, characterized in that, When it is determined that there is a control requirement for the non-catalytic reduction system, the step of controlling the non-catalytic reduction system according to the first operating parameter and the second operating parameter includes: When the operating parameters of the non-catalytic reduction system change or the ammonia slip increases, it is determined that there is a control requirement for the non-catalytic reduction system. A multi-dimensional predictive control model is constructed based on the first and second operating parameters to generate a basic adjustment command for the amount of urea sprayed. The basic regulation command is corrected based on ammonia slip as a feedforward signal. Adjust the urea injection time and injection capacity based on the aforementioned basic adjustment commands.

4. The optimization method for the control system according to claim 1, characterized in that, The method further includes: In response to the primary frequency regulation command, a primary frequency regulation correction command is generated based on the second operating parameters and the frequency and power signals of the steam turbine; The primary frequency regulation correction command is optimized based on the flow characteristic model, and the turbine is regulated based on the optimized primary frequency regulation correction.

5. The optimization method for the control system according to claim 3, characterized in that, The optimization of the primary frequency modulation correction command based on the flow characteristic model also includes: Collect valve characteristic curve data under different operating conditions; The support vector machine algorithm is applied to fit nonlinear flow characteristics; A dynamic correction factor is introduced to compensate for the effects of valve wear.

6. An optimization device for a control system, characterized in that, include: The optimization unit is used to respond to the unit load change command by optimizing the core control logic through the control logic optimization module to generate the first operating parameters; The first generation unit is used to dynamically adjust the coordination control strategy based on the first operating parameters and generate the second operating parameters when the energy matching between the steam turbine and the boiler is unbalanced. An execution unit is configured to, when it is determined that there is a control requirement for the non-catalytic reduction system, perform control on the non-catalytic reduction system according to the first operating parameters and the second operating parameters.

7. The optimization device for the control system according to claim 6, characterized in that, The core control logic includes at least one of the following: main and auxiliary machine protection logic, flue gas system logic, feedwater system logic, and main reheat temperature control logic; the first set of operating parameters includes at least one of the following: main steam pressure, flue gas system flow rate, feedwater flow rate, and main reheat steam temperature.

8. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-5.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-5.

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