A dynamic thermal inertia prediction and partition spray desuperheating control method for a deep peak regulation large power station boiler

Abstract

The present application relates to the technical field of intelligent control of large power station boilers, and discloses a dynamic thermal inertia prediction and partition spray desuperheating control method for large power station boilers for deep peak shaving, comprising the following steps: operation data acquisition and fusion; dynamic thermal inertia prediction modeling; multi-level compensation control calculation; spatial partition spray regulation; self-evolution parameter optimization mechanism; execution and closed-loop correction, through the establishment of a steam thermal inertia dynamic prediction model, the target water injection amount is calculated in advance in combination with the load change trend, and the nozzle is adaptively adjusted according to the steam temperature spatial distribution, so that the steam section where the desuperheater is located and the downstream steam temperature are rapidly, stably and accurately regulated, and the subsequent heat transfer process of the heated surface produces a coordinating effect on the final steam temperature change trend, which can improve the accuracy and response speed of steam temperature regulation under deep peak shaving conditions, reduce the spray water consumption, and reduce the thermal stress fluctuation of the heated surface.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for large power plant boilers, specifically a dynamic thermal inertia prediction and zoned spray desuperheating control method for large power plant boilers with deep peak shaving. Background Technology

[0002] As the proportion of new energy power generation continues to increase, large thermal power units are gradually undertaking the tasks of deep peak shaving and frequent load changes. Supercritical and ultra-supercritical boilers experience continuous changes in fuel quantity, feedwater quantity, steam flow rate, heat absorption of heating surfaces, and metal heat storage state during low load, rapid load increase and decrease, especially during load increase. The dynamic response speed of each link is different, which makes the steam temperature exhibit strong nonlinearity, time-varying and lag, which puts forward higher requirements for the dynamic adjustment capability of the spray desuperheating system.

[0003] Existing boilers typically install water spray desuperheaters in the middle of the steam flow. By injecting desuperheating water into the high-temperature steam, the temperature and specific enthalpy of the steam at the desuperheater outlet or its downstream middle position are directly changed. The final steam temperature is indirectly affected by the continued heat absorption process of the subsequent heating surfaces. Therefore, the direct target of the desuperheating water regulation is not the final main steam or reheat steam temperature, but the steam temperature state of the desuperheater location and its downstream related steam section.

[0004] Traditional proportional-integral-derivative (PID) control or fixed-parameter control is mostly based on conventional load conditions and is difficult to adapt to the dynamic thermal inertia and heat transfer characteristics changes during deep peak shaving. Especially during the load increase stage, due to the combined effects of enhanced combustion heat release, changes in heat storage on the heating surface, and increased steam flow, there is a significant transmission lag between the steam temperature before and after the desuperheater and the subsequent steam temperature. Adjusting the water spray based solely on the current temperature deviation can easily lead to a mismatch between the water spray command and the actual heat load change, resulting in delayed, premature, or excessive water spraying. Improper use of desuperheating water can reduce local steam temperature and specific enthalpy, increase the reheating burden on the subsequent heating surface, and cause steam temperature fluctuations, increased local temperature differences, and fluctuations in thermal stress on the heating surface metal, which are detrimental to the safe, stable, and economical operation of the unit.

[0005] Furthermore, due to the influence of combustion organization, flue gas flow, and uneven heat absorption of the heating surface, there are differences in steam temperature in different areas. The existing unified water spray control method is difficult to achieve reasonable allocation of each spray area. Therefore, a method is needed that can predict dynamic thermal inertia in real time and coordinately optimize the total water spray volume and the water spray volume of each zone to improve the accuracy and response speed of steam temperature regulation during deep peak shaving. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a dynamic thermal inertia prediction and zoned spray desuperheating control method for large power plant boilers with deep peak shaving, solving the problems mentioned in the background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a dynamic thermal inertia prediction and zoned spray desuperheating control method for large power plant boilers with deep peak shaving. The control method is embedded in the existing boiler distributed control system or independent control unit and operates to predict and regulate the steam temperature state of the steam section where the desuperheater is located and its downstream steam temperature.

[0008] Includes the following steps:

[0009] Step 1: Run data acquisition and fusion;

[0010] Step 2: Dynamic thermal inertia prediction modeling;

[0011] Step 3: Multi-level compensation control calculation;

[0012] Step 4: Spatial zone spray control;

[0013] Step 5: Self-evolutionary parameter optimization mechanism;

[0014] Step six: Execution and closed-loop correction.

[0015] As a preferred technical solution of the dynamic thermal inertia prediction and zoned spray desuperheating control method for large power plant boilers with deep peak shaving according to the present invention, the first step is to collect the operating parameters of the boiler desuperheating system and related steam processes, including data such as steam temperature, steam flow rate, boiler load, combustion rate, desuperheating water temperature and pressure.

[0016] Among them, steam temperature is used to characterize the steam temperature state of the steam section where the desuperheater is located and its downstream related locations. After processing, each parameter forms a comprehensive operating state vector, which provides the input basis for thermal inertia prediction.

[0017] As a preferred technical solution of the dynamic thermal inertia prediction and zoned spray desuperheating control method for large power plant boilers with deep peak shaving according to the present invention, in step two, a variable time-delay nonlinear thermal inertia prediction model is established based on the instantaneous steam enthalpy change rate and load acceleration. The variable time-delay nonlinear thermal inertia prediction model is adjusted in real time according to the operating conditions to correct the time lag coefficient. Automatically calculates the temperature response lag time during the downstream transmission of the cooling effect. And predict the relevant steam section temperature at future times. This allows for the early estimation of steam temperature change trends.

[0018] As a preferred technical solution of the dynamic thermal inertia prediction and zoned spray de-cooling control method for large power plant boilers with deep peak shaving, the variable time delay nonlinear thermal inertia prediction model adopts a variable time delay calculation method that can be updated online. By introducing the specific enthalpy change rate and load acceleration, the adaptive correction of the prediction lag time is realized.

[0019] Adjust the temperature response hysteresis time in real time according to the load acceleration. It is used to characterize the dynamic thermal inertia and temperature transfer hysteresis between the steam section where the desuperheater is located and its downstream target monitoring position.

[0020] As a preferred technical solution of the dynamic thermal inertia prediction and zoned spray desuperheating control method for large power plant boilers with deep peak shaving according to the present invention, step three involves calculating the theoretical desuperheating water volume based on the predicted temperature and the target steam temperature. Temperature deviation correction amount Stable suppression correction amount Synthesized control signals To regulate the steam temperature in the steam section where the desuperheater is located and its downstream steam temperature;

[0021] Specifically, a three-layer collaborative control structure is adopted, consisting of a prediction feedforward layer, an interpolation correction layer, and a stabilization suppression layer.

[0022] Predictive feedforward layer: Based on the predicted temperature of the steam section where the desuperheater is located or the target monitoring location downstream. Calculate the basic spray volume ;

[0023] Difference correction layer: Based on the deviation between the target steam temperature and the predicted temperature Calculate the correction amount ;

[0024] Stabilizing suppression layer: Adjust the correction amount based on spray execution lag and waterway inertia. ;

[0025] The final control command is obtained by synthesis. It is used to regulate the steam temperature in the steam section where the desuperheater is located and its downstream steam temperature.

[0026] As a preferred technical solution of the dynamic thermal inertia prediction and zoned spray desuperheating control method for large power plant boilers with deep peak shaving according to the present invention, in step four, the nozzle array is divided into multiple independent control zones according to the steam flow field, and temperature detection points are set in each zone. , , ... The controller calculates weights based on the temperature deviations of each zone. The water volume for each zone is allocated according to the following formula:

[0027] ;

[0028] Based on the temperature detection array of the relevant steam section downstream of the desuperheater , , ... Based on the distribution results, a regional weighted algorithm is used to independently adjust the spray volume of each nozzle zone in order to achieve a uniform distribution of the temperature field in the relevant steam section.

[0029] It independently controls the spray volume of each nozzle zone through a multi-channel proportional valve, and automatically optimizes the zone weights based on the steam outlet temperature difference to achieve temperature field uniformity and water-saving control.

[0030] As a preferred technical solution of the dynamic thermal inertia prediction and zoned spray desuperheating control method for large power plant boilers with deep peak shaving according to the present invention, step five involves correcting the prediction model coefficients based on long-term operating data. And partition weight model Perform self-learning and optimization updates;

[0031] When boiler load, coal characteristics, or environmental conditions change, the system automatically migrates model parameters without requiring manual readjustment.

[0032] As a preferred technical solution of the dynamic thermal inertia prediction and zoned spray desuperheating control method for large power plant boilers with deep peak shaving according to the present invention, step six involves transmitting the final control command. The output is sent to the zone proportional valve and nozzle actuator, and the execution feedback signal and related steam outlet temperature feedback signal are collected. The prediction model and control parameters are corrected in real time, forming a closed-loop dynamic update mechanism of prediction-control-feedback-correction.

[0033] Compared with existing technologies, this invention provides a dynamic thermal inertia prediction and zoned spray desuperheating control method for large power plant boilers with deep peak shaving capabilities, which has the following beneficial effects:

[0034] 1. By establishing a dynamic thermal inertia prediction model based on the steam specific enthalpy change rate and load acceleration, adaptive correction of steam temperature response lag time is achieved, enabling spray desuperheating control to have advance prediction and active compensation functions. Furthermore, by combining prediction feedforward, difference correction and stability suppression through a multi-layer collaborative control structure, the target spray volume can be quickly calculated and corrected when the unit load changes, reducing overshoot and undershoot of the steam temperature in the steam section where the desuperheater is located and its downstream. The final steam temperature fluctuation is reduced through the heat transfer process of the subsequent heating surface. By adopting a zoned spray control algorithm, the spray volume of each nozzle zone is automatically adjusted according to the steam temperature field distribution to achieve a uniform temperature field distribution and prevent local overcooling or overheating.

[0035] 2. By introducing a self-evolutionary learning module, the control system can automatically optimize control parameters based on historical operating data, thereby improving the system's adaptability to different loads and combustion conditions. This can be implemented without changing the original hardware structure of the spray desuperheater, and has the advantages of simple algorithm, fast response, high control accuracy, and good structural compatibility. It can significantly improve the stability and economy of steam temperature regulation during the deep peak shaving process of supercritical boilers.

[0036] In summary, by establishing a dynamic prediction model of steam thermal inertia, calculating the target spray water volume in advance based on the load change trend, and realizing adaptive adjustment of nozzle zones according to the spatial distribution of steam temperature, the steam temperature of the steam section where the desuperheater is located and its downstream steam temperature can be rapidly, stably, and accurately adjusted. Furthermore, by coordinating the final steam temperature change trend through the heat transfer process of the subsequent heating surface, the accuracy and response speed of steam temperature regulation under deep peak shaving conditions can be improved, spray water consumption can be reduced, and thermal stress fluctuations of the heating surface can be reduced. It has the advantages of simple algorithm, strong adaptability, and direct embedding into existing boiler control systems. Attached Figure Description

[0037] Figure 1 This is a flowchart of the control method of the present invention.

[0038] Figure 2 This is a block diagram of the structural composition of the boiler spray desuperheating control system of the present invention.

[0039] Figure 3 This is a schematic diagram illustrating the principle of the dynamic thermal inertia prediction model of this invention.

[0040] Figure 4 This is a control principle diagram of the multi-layer compensation control module of the present invention.

[0041] Figure 5 This is a structural block diagram of the zoned spray control module of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. However, it should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0043] In the description of this invention, it should be noted that when an element is referred to as being "fixed to" or "set on" another element, it can be directly on or indirectly on the other element. When an element is referred to as being "connected to" another element, it can be directly connected to or indirectly connected to the other element.

[0044] In the description of this invention, it should be noted that the terms "center," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of this invention is in use. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. "Several" means one or more, unless otherwise explicitly specified.

[0045] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0046] Example: Please refer to Figures 1-5 The present invention provides the following technical solution: a dynamic thermal inertia prediction and zoned spray desuperheating control method for large power plant boilers with deep peak shaving. The control method is embedded in the existing boiler distributed control system (DCS) and implemented through software algorithms. It can be implemented without changing the original spray desuperheater hardware structure. It has the advantages of simple structure, low modification cost and engineering deployment. The method consists of three parts: a data acquisition module, a central control unit and a desuperheating system execution module.

[0047] The central control unit includes a dynamic thermal inertia prediction module, a multi-layer compensation control module, a zoned spray control module, and a self-evolutionary optimization module. The modules interact with each other via a data bus for signal exchange and logical operations.

[0048] Dynamic thermal inertia prediction module: used for variable time delay nonlinear prediction; Multi-layer compensation control module: used for three-layer collaborative control calculation; Partitioned spray control module: used for spatial partition weight allocation; Self-evolution optimization module: used for parameter self-learning and updating.

[0049] By predicting and regulating the steam temperature in the steam section where the desuperheater is located and its downstream steam temperature, the steam temperature regulation response speed, stability and compatibility with existing systems during deep peak shaving can be improved.

[0050] Includes the following steps:

[0051] Step 1: Run data acquisition and fusion;

[0052] Step 2: Dynamic thermal inertia prediction modeling;

[0053] Step 3: Multi-level compensation control calculation;

[0054] Step 4: Spatial zone spray control;

[0055] Step 5: Self-evolutionary parameter optimization mechanism;

[0056] Step six: Execution and closed-loop correction.

[0057] Step 1: Collect operating parameters of the boiler desuperheating system and related steam processes in real time through the data acquisition module, including data such as steam temperature, steam flow rate, boiler load, combustion rate, desuperheating water temperature and pressure.

[0058] Among them, steam temperature is used to characterize the steam temperature state of the steam section where the desuperheater is located and its downstream related locations. After processing by the signal fusion module, each parameter forms a comprehensive operating state vector, which provides the input basis for thermal inertia prediction.

[0059] Step two: Establish a variable time-delay nonlinear thermal inertia prediction model based on the instantaneous steam enthalpy change rate and load acceleration. The variable time-delay nonlinear thermal inertia prediction model is then adjusted in real time according to the operating conditions to correct the time lag coefficient. Automatically calculates the temperature response lag time during the downstream transmission of the cooling effect. And predict the relevant steam section temperature at future times. This allows for the early estimation of steam temperature change trends.

[0060] The variable time-delay nonlinear thermal inertia prediction model adopts an online-updable variable time-delay calculation method. By introducing the rate of change of specific enthalpy and load acceleration, it achieves adaptive correction of the prediction lag time.

[0061] Adjust the temperature response hysteresis time in real time according to the load acceleration. It is used to characterize the dynamic thermal inertia and temperature transfer hysteresis between the steam section where the desuperheater is located and its downstream target monitoring position.

[0062] Step 3: Calculate the theoretical desuperheating water volume based on the predicted temperature and the target steam temperature. Temperature deviation correction amount Stable suppression correction amount Synthesized control signals To regulate the steam temperature in the steam section where the desuperheater is located and its downstream steam temperature;

[0063] Specifically, a three-layer collaborative control structure is adopted, consisting of a prediction feedforward layer, an interpolation correction layer, and a stabilization suppression layer.

[0064] Predictive feedforward layer: Based on the predicted temperature of the steam section where the desuperheater is located or the target monitoring location downstream. Calculate the basic spray volume ;

[0065] Difference correction layer: Based on the deviation between the target steam temperature and the predicted temperature Calculate the correction amount ;

[0066] Stabilizing suppression layer: Adjust the correction amount based on spray execution lag and waterway inertia. This reduces the mismatch between the water spray command and the dynamic response of steam temperature during load increases and decreases, and the stabilization and suppression layer dynamically adjusts the spray execution delay compensation coefficient to ensure that the system maintains a stable steam temperature during load changes, especially during load increases.

[0067] The final control command is obtained by synthesis. It is used to regulate the steam temperature in the steam section where the desuperheater is located and its downstream steam temperature.

[0068] Step four: Divide the nozzle array into multiple independent control zones according to the steam flow field, specifically into upper, middle, and lower zones, and set up temperature detection points in each zone. , , ... The controller calculates weights based on the temperature deviations of each zone. The water volume for each zone is allocated according to the following formula:

[0069] ;

[0070] By independently controlling the nozzle opening in each zone, the spatial uniformity of the steam temperature field is achieved, preventing local overcooling or overheating.

[0071] Based on the temperature detection array of the relevant steam section downstream of the desuperheater , , ... Based on the distribution results, a regional weighted algorithm is used to independently adjust the spray volume of each nozzle zone in order to achieve a uniform distribution of the temperature field in the relevant steam section.

[0072] It independently controls the spray volume of each nozzle zone through a multi-channel proportional valve, and can automatically optimize the zone weight according to the steam outlet temperature difference to achieve temperature field uniformity and water-saving control.

[0073] Step 5: The control system is equipped with a self-evolving learning module, which includes a feature extraction unit, a parameter optimization unit, and a model update unit. Based on long-term operating data, the prediction model coefficients are adjusted. And partition weight model Perform self-learning and optimization updates;

[0074] When boiler load, coal characteristics, or environmental conditions change, the system automatically migrates model parameters without requiring manual readjustment, thus achieving adaptive control.

[0075] Step six, send the final control command. The output is sent to the zone proportional valve and nozzle actuator, and the execution feedback signal and related steam outlet temperature feedback signal are collected. The prediction model and control parameters are corrected in real time, forming a closed-loop dynamic update mechanism of prediction-control-feedback-correction to ensure rapid response and long-term stability of the desuperheating process.

[0076] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for dynamic thermal inertia prediction and zoned spray desuperheating control of large power plant boilers for deep peak shaving, characterized in that, The control method is embedded in the existing boiler distributed control system or independent control unit to predict and regulate the steam temperature state of the steam section where the desuperheater is located and its downstream steam. Includes the following steps: Step 1: Run data acquisition and fusion; Step 2: Dynamic thermal inertia prediction modeling; Step 3: Multi-level compensation control calculation; Step 4: Spatial zone spray control; Step 5: Self-evolutionary parameter optimization mechanism; Step six: Execution and closed-loop correction.

2. The method for dynamic thermal inertia prediction and zoned spray desuperheating control of a large power plant boiler for deep peak shaving, as described in claim 1, is characterized in that... Step one involves collecting operating parameters of the boiler desuperheating system and related steam processes, including data on steam temperature, steam flow rate, boiler load, combustion rate, desuperheating water temperature, and pressure. Among them, steam temperature is used to characterize the steam temperature state of the steam section where the desuperheater is located and its downstream related locations. After processing, each parameter forms a comprehensive operating state vector, which provides the input basis for thermal inertia prediction.

3. The method for dynamic thermal inertia prediction and zoned spray desuperheating control of a large power plant boiler for deep peak shaving, as described in claim 1, is characterized in that... In step two, a variable time-delay nonlinear thermal inertia prediction model is established based on the instantaneous rate of change of steam specific enthalpy and load acceleration. The variable time-delay nonlinear thermal inertia prediction model is then adjusted in real time according to the operating conditions to correct the time lag coefficient. Automatically calculates the temperature response lag time during the downstream transmission of the cooling effect. And predict the relevant steam section temperature at future times. This allows for the early estimation of steam temperature change trends.

4. The method for dynamic thermal inertia prediction and zoned spray desuperheating control of a large power plant boiler for deep peak shaving, as described in claim 3, is characterized in that... The variable time-delay nonlinear thermal inertia prediction model adopts an online-updable variable time-delay calculation method, and achieves adaptive correction of the prediction lag time by introducing the specific enthalpy change rate and load acceleration. Adjust the temperature response hysteresis time in real time according to the load acceleration. It is used to characterize the dynamic thermal inertia and temperature transfer hysteresis between the steam section where the desuperheater is located and its downstream target monitoring position.

5. The method for dynamic thermal inertia prediction and zoned spray desuperheating control of a large power plant boiler for deep peak shaving, as described in claim 1, is characterized in that... In step three, the theoretical desuperheating water volume is calculated based on the predicted temperature and the target steam temperature. Temperature deviation correction amount Stable suppression correction amount Synthesized control signals To regulate the steam temperature in the steam section where the desuperheater is located and its downstream steam temperature; Specifically, a three-layer collaborative control structure is adopted, consisting of a prediction feedforward layer, an interpolation correction layer, and a stabilization suppression layer. Predictive feedforward layer: Based on the predicted temperature of the steam section where the desuperheater is located or the target monitoring location downstream. Calculate the basic spray volume ; Difference correction layer: Based on the deviation between the target steam temperature and the predicted temperature Calculate the correction amount ; Stabilizing suppression layer: Adjust the correction amount based on spray execution lag and waterway inertia. ; The final control command is obtained by synthesis. It is used to regulate the steam temperature in the steam section where the desuperheater is located and its downstream steam temperature.

6. The method for dynamic thermal inertia prediction and zoned spray desuperheating control of a large power plant boiler for deep peak shaving, as described in claim 1, is characterized in that... In step four, the nozzle array is divided into multiple independent control zones according to the steam flow field, and temperature detection points are set in each zone. , , ... The controller calculates weights based on the temperature deviations of each zone. The water volume for each zone is allocated according to the following formula: ； Based on the temperature detection array of the relevant steam section downstream of the desuperheater , , ... Based on the distribution results, a regional weighted algorithm is used to independently adjust the spray volume of each nozzle zone in order to achieve a uniform distribution of the temperature field in the relevant steam section. It independently controls the spray volume of each nozzle zone through a multi-channel proportional valve, and automatically optimizes the zone weight based on the steam outlet temperature difference to achieve temperature field uniformity and water-saving control.

7. The method for dynamic thermal inertia prediction and zoned spray desuperheating control of a large power plant boiler for deep peak shaving, as described in claim 1, is characterized in that... Step five involves adjusting the prediction model coefficients based on long-term operational data. And partition weight model Perform self-learning and optimization updates; When boiler load, coal characteristics, or environmental conditions change, the system automatically migrates model parameters without requiring manual readjustment.

8. The method for dynamic thermal inertia prediction and zoned spray desuperheating control of a large power plant boiler for deep peak shaving, as described in claim 1, is characterized in that... Step six will transmit the final control command. The output is sent to the zone proportional valve and nozzle actuator, and the execution feedback signal and related steam outlet temperature feedback signal are collected. The prediction model and control parameters are corrected in real time, forming a closed-loop dynamic update mechanism of prediction-control-feedback-correction.

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