Control method and device of thermal power generating unit, medium, equipment and program product

Abstract

The invention relates to a thermal power generating unit control method and device, a medium, equipment and a program product, and relates to the technical field of power generation. The method comprises the following steps: acquiring operation data of a thermal power generating unit, the operation data comprising actual values of first operation data and second operation data, the first operation data comprises a historical power instruction value, a power grid frequency, an active power actual value, boiler thermal efficiency, load response time, a steam turbine heat consumption rate, a thermal power generating unit power generation coal consumption rate, a power supply coal consumption rate, coal feeding system parameters and the like, and the second operation data comprises equipment operation data of a thermal power generating unit; inputting the first operation data into the trained prediction model to obtain a prediction value of the second operation data; when the difference value between the actual value and the predicted value is smaller than a first preset threshold value, determining a current power instruction value according to the actual value and the predicted value; and the coal feeding amount and the air feeding amount are adjusted according to the current power instruction value. The method can improve the response speed of power regulation of the thermal power generating unit.

Description

Technical Field

[0001] The present disclosure relates to the field of power generation technology, and in particular to a control method, device, medium, equipment and program product for a thermal power unit. Background Art

[0002] With the rapid development of renewable energy, the randomness and intermittency of wind and solar power generation have brought severe challenges to the safe operation of the power grid. In order to balance the fluctuations of the power grid, thermal power units need to be transformed for flexibility and improve the rapid response capability of power regulation.

[0003] The traditional "feedforward + PID feedback" control mode can no longer meet the needs of the new power system. During the deep peak regulation process, this control method has a slow response speed and is prone to problems such as water-coal-air ratio imbalance and abnormal furnace temperature fluctuations. In serious cases, it may even cause safety accidents. Summary of the invention

[0004] In order to overcome the problems existing in the related art, the present application provides a control method, device, medium, equipment and program product for a thermal power unit.

[0005] According to a first aspect of the present application, a control method for a thermal power unit is provided, the method comprising: Acquire operation data of the thermal power unit, the operation data including actual values ​​of first operation data and second operation data, the first operation data including one or more of the following data: historical power command value, grid frequency, actual value of active power, boiler thermal efficiency, load response time, steam turbine heat rate, power generation coal consumption rate of the thermal power unit, power supply coal consumption rate, coal feeding system parameters and air supply system parameters, the second operation data including equipment operation data of the thermal power unit; Inputting the first operation data into the trained prediction model to obtain a prediction value of the second operation data; When the difference between the actual value and the predicted value is less than a first preset threshold, determining a current power command value according to the actual value and the predicted value; The coal supply and air supply are adjusted according to the current power command value.

[0006] Optionally, obtaining the operating data of the thermal power unit includes: The operation data collected by the distributed control system is obtained, wherein the distributed control system transmits the operation data to a data collection center through an electronic room server.

[0007] Optionally, the trained prediction model is obtained by the following steps: Obtain historical operation data of thermal power units; Selecting historical operation data in which the difference between the actual value of active power and the historical power command value does not exceed a second preset threshold from the historical operation data as training data; Determine training samples and labels based on the training data, wherein the training samples include one or more of the following data: historical power command value, grid frequency, actual active power value, boiler thermal efficiency, load response time, steam turbine heat rate, thermal power generation coal consumption rate, power supply coal consumption rate, coal feeding system parameters and air supply system parameters, and the labels include equipment operation data of the thermal power unit; Inputting the training samples into a neural network model to obtain a predicted value; Adjusting the parameters of the neural network model according to the difference between the predicted value and the label; Return to the step of inputting the training sample into the neural network model to obtain a prediction value, until the difference between the prediction value and the label is less than a third preset threshold or the number of cycles reaches a preset number, to obtain the trained prediction model.

[0008] Optionally, the method further comprises: When the difference between the actual value and the predicted value of the second operating data is greater than a first preset threshold, outputting an abnormal prompt and obtaining a correction value of the second operating data input by the user; Determining the current power command value according to the actual value and the correction value; The prediction model is updated according to the correction values ​​of the first operation data and the second operation data.

[0009] Optionally, the method further comprises: The current power command value is sent to an automatic power generation control system, so that the automatic power generation control system adjusts its feedforward control parameters and feedback control parameters according to the current power command value.

[0010] Optionally, adjusting the coal supply amount and air supply amount according to the current power command value includes: The current power command value is sent to a distributed control system, and the distributed control system controls the coal feeding system to adjust the coal feeding amount and controls the air supply system to adjust the air supply amount.

[0011] According to a second aspect of the present application, a control device for a thermal power unit is provided, comprising: A data acquisition module, used to acquire operation data of the thermal power unit, the operation data including actual values ​​of first operation data and second operation data, the first operation data including one or more of the following data: historical power command value, grid frequency, actual value of active power, boiler thermal efficiency, load response time, steam turbine heat rate, power generation coal consumption rate of the thermal power unit, power supply coal consumption rate, coal feeding system parameters and air supply system parameters, the second operation data including equipment operation data of the thermal power unit; A data prediction module, used for inputting the first operation data into the trained prediction model to obtain a predicted value of the second operation data; A power command module, configured to determine a power command value according to the actual value and the predicted value when a difference between the actual value and the predicted value is less than a first preset threshold; The thermal power unit adjustment module is used to adjust the coal supply and air supply according to the current power command value.

[0012] According to the third aspect of the present application, a non-temporary computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the methods provided in the first aspect of the present application are implemented.

[0013] According to a fourth aspect of the present application, there is provided an electronic device, including: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the steps of any one of the methods provided in the first aspect of the present application.

[0014] According to a fifth aspect of the present application, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of any one of the methods provided in the first aspect of the present application.

[0015] Through the above technical solution, first of all, the key operating data of the thermal power unit can be comprehensively collected, including power instructions, grid frequency, active power, etc., reflecting the dynamic characteristics of the thermal power unit. Secondly, based on the trained prediction model, the deviations that may occur in the response process of the thermal power unit can be predicted. In addition, this method can compare the predicted value with the actual observed value in real time, and when the deviation between the two is less than the threshold, the optimized power instruction can be determined. Finally, according to the optimized power instruction, the coal supply and air supply are accurately adjusted to ensure the stability of the combustion process and avoid temperature fluctuations, thereby improving the load response speed of the thermal power unit.

[0016] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings: Figure 1 is a flow chart showing a control method for a thermal power unit according to an exemplary embodiment; Figure 2 is a block diagram of a control device for a thermal power unit according to an exemplary embodiment; Figure 3 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0018] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0019] It should be noted that all actions of acquiring signals, information or data in the present disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the device is located and with the authorization given by the owner of the corresponding device.

[0020] Herein, the terms "first", "second", etc. are only used to distinguish one element from another element, and do not require or imply any actual relationship or order between these elements. In fact, the first element may also be called the second element, and vice versa.

[0021] Figure 1 is a flow chart of a control method for a thermal power unit according to an exemplary embodiment. Figure 1 As shown, the method includes the following steps.

[0022] In step 110, the operation data of the thermal power unit is obtained, and the operation data includes the actual values ​​of the first operation data and the second operation data. The first operation data includes one or more of the following data: historical power command value, grid frequency, actual value of active power, boiler thermal efficiency, load response time, turbine heat rate, power generation coal consumption rate of the thermal power unit, power supply coal consumption rate, coal feeding system parameters and air supply system parameters. The second operation data includes the equipment operation data of the thermal power unit.

[0023] In step 120, the first operating data is input into the trained prediction model to obtain a predicted value of the second operating data.

[0024] In step 130, when the difference between the actual value and the predicted value is smaller than a first preset threshold, a power command value is determined according to the actual value and the predicted value.

[0025] In step 140, the coal supply rate and air supply rate are adjusted according to the current power command value.

[0026] Here, the first operation data may include a single data or any combination of data sets among the current power command value, grid frequency, actual active power value, boiler thermal efficiency, load response time, turbine heat rate, thermal power unit power generation coal consumption rate, power supply coal consumption rate, coal feeding system parameters, and air supply system parameters. The first operation data can characterize the dynamic operation performance and efficiency characteristics of the thermal power unit, and is an object that the thermal power unit needs to respond quickly.

[0027] The second operation data may include equipment operation data of the thermal power unit, specifically, output parameters of the thermal power unit, boiler operation data, turbine operation data, and auxiliary system parameters. The second operation data may reflect the equipment operation status inside the thermal power unit, and directly determine the actual response capability and operation status of the thermal power unit.

[0028] The first preset threshold can be set according to the actual situation of the thermal power unit to set a reasonable threshold of difference between the predicted value and the actual value, which should generally be as small as possible to improve control accuracy. The historical power command value is obtained from the Automatic Generation Control (AGC) system, which issues power generation adjustment instructions to the thermal power unit based on the changes in the grid load to maintain the grid frequency and power balance.

[0029] It should be understood that step 110 provides basic data input for subsequent prediction and optimization control by obtaining the operating data of the thermal power unit. There is an inherent correlation between the first operating data and the second operating data. Changes in demand on the power grid side will drive the fuel supply and combustion process inside the thermal power unit to make corresponding adjustments. The actual response inside the thermal power unit will in turn affect the power supply on the power grid side. This mutually coupled dynamic characteristic means that the connection between the first operating data and the second operating data can be fully utilized, and the current power command value can be used to ultimately achieve coordination between the power grid and the thermal power unit.

[0030] Step 120 uses the trained prediction model to predict the theoretical value of the second operation data according to the acquired first operation data. The prediction model can be a model obtained by using a training method of a neural network model to learn the intrinsic relationship between the first operation data and the second operation data of the thermal power unit through historical operation data. Through the predicted theoretical value of the second parameter, the deviation that may occur in the response process of the thermal power unit can be identified in time, laying a foundation for subsequent control optimization.

[0031] Step 130 compares the predicted value of the second parameter with the actual value in real time. When the difference between the two is less than the first preset threshold, it means that there is no abnormality in the prediction. There is no large fluctuation between the predicted value and the actual value of the second parameter, and the current power command value can be determined based on this. Specifically, the power command value can be adjusted to the current power command value so that the predicted value of the second parameter is consistent with the actual value.

[0032] Step 140 adjusts the coal feed and air supply according to the current power command value determined above. Specifically, the coal feed and air supply can be adjusted in real time according to the current power command value so that the former changes in direct proportion to the latter. When the current power command value rises, the coal feed and air supply are increased, and when it falls, they are reduced accordingly. This can stabilize the combustion process, avoid temperature fluctuations caused by imbalance between supply and demand, and thus improve the load response speed of the thermal power unit.

[0033] Through the above technical solution, first of all, the key operating data of the thermal power unit can be comprehensively collected, including power instructions, grid frequency, active power, etc., reflecting the dynamic characteristics of the thermal power unit. Secondly, based on the trained prediction model, the deviations that may occur in the response process of the thermal power unit can be predicted. In addition, this method can compare the predicted value with the actual observed value in real time, and when the deviation between the two is less than the threshold, the optimized power instruction can be determined. Finally, according to the optimized power instruction, the coal supply and air supply are accurately adjusted to ensure the stability of the combustion process and avoid temperature fluctuations, thereby improving the load response speed of the thermal power unit.

[0034] In one embodiment, the obtaining of the operating data of the thermal power unit includes: obtaining the operating data collected by a distributed control system, wherein the distributed control system transmits the operating data to a data collection center via an electronic room server.

[0035] Here, the distributed control system (DCS) is composed of multiple distributed control units, each of which controls a subsystem or device of the thermal power unit. Each control unit of the distributed control system can collect the first operation data and the second operation data of the thermal power unit in real time through each sensor connected to it. Each control unit of the distributed control system transmits the collected operation data to the electronic room server through the electronic data bus. The electronic room server then transmits the data to a higher-level data collection center.

[0036] The electronic room server is located between the distributed control system and the data acquisition center. It can centrally receive the operating data from each distributed control unit and centralize the scattered data streams onto a unified data platform, namely the data acquisition center. The electronic room server also has the function of data caching. It can temporarily store some key operating data to prevent data loss caused by communication interruption.

[0037] The data collection center can be specifically an Internet Gateway (IGate) data collection center. The data collection center receives various operating data from the electronic room server and centrally manages and stores them. The data collection center will screen and process the massive data collected. Specifically, the data collection center can remove some redundant or noisy data to improve the quality and effectiveness of the data. The data collection center distributes the screened operating data to the execution subject of the method. The execution subject of the method can be specifically an Advanced Integrated Intelligent Computing System (AICS).

[0038] This hierarchical data collection and transmission through distributed control systems, electronic room servers and data acquisition centers can obtain comprehensive, real-time and high-quality operating data of thermal power units.

[0039] In one embodiment, the trained prediction model is obtained by the following steps: acquiring historical operation data of the thermal power unit; selecting historical operation data in which the difference between the actual active power value and the historical power command value does not exceed a second preset threshold from the historical operation data as training data; determining training samples and labels based on the training data, the training samples include one or more of the following data: historical power command value, grid frequency, actual active power value, boiler thermal efficiency, load response time, turbine heat rate, thermal power generation coal consumption rate, power supply coal consumption rate, coal feeding system parameters and air supply system parameters, and the labels include equipment operation data of the thermal power unit; inputting the training samples into the neural network model to obtain a predicted value; adjusting the parameters of the neural network model according to the difference between the predicted value and the label; returning to execute the step of inputting the training samples into the neural network model to obtain a predicted value, until the difference between the predicted value and the label is less than the third preset threshold or the number of cycles reaches the preset number, to obtain the trained prediction model.

[0040] Here, historical operation data refers to the operation data of thermal power units in the past period of time.

[0041] The second preset threshold refers to the maximum allowable value of the difference between the actual value of active power and the historical power command value. Only historical data whose difference between the actual value and the command value is within this threshold range will be selected as training data. The setting of the second preset threshold can be combined with the response characteristics and control requirements of the actual thermal power unit. The second preset threshold is often set to a relatively small value so that the actual response of the thermal power unit can better match the command value.

[0042] The training sample includes data elements consistent with the first operating parameter. The label includes data elements consistent with the second operating parameter. Each training sample has a corresponding label. The label can reflect the actual operating status of each device of the thermal power unit under the given training sample conditions.

[0043] You can choose a common neural network model, such as a deep neural network (DNN) model, as the initial model of the prediction model. The neural network model consists of multiple hidden layers and can learn complex nonlinear relationships.

[0044] The input layer of the neural network model receives training samples. The hidden layer learns the complex mapping relationship between input features and output through nonlinear transformation of multiple layers of neurons. The output layer gives the predicted equipment operation data. Using optimization algorithms such as gradient descent, the parameters of each layer of the model are adjusted according to the difference between the predicted output and the label. The goal is to minimize the error between the predicted value and the label so that the model can learn a more accurate mapping relationship.

[0045] Specifically, a back propagation algorithm can be used to back propagate the prediction error of the output layer to each hidden layer. According to the error gradient, an optimization algorithm such as the Adam optimizer is used to update the weights and bias parameters of each layer. The adjusted model is applied to the training sample again to obtain a new prediction output. The difference between the predicted value and the label is continued to be calculated, and the above parameter adjustment process is repeated. The model training is completed until the difference between the predicted value and the label is less than the third preset threshold, or the number of cycles reaches the preset number. By continuously iteratively adjusting the parameters, the prediction result gradually approaches the label.

[0046] The preset number is the maximum number of cycles to prevent overfitting of the prediction model. The third preset threshold refers to the maximum allowable difference between the predicted value and the label. When the difference between the predicted value and the label is less than this threshold, the model training is considered complete.

[0047] By screening historical operating data, building a neural network-based prediction model and continuously optimizing the training, a model can be established that can accurately predict the operating parameters of each device in a thermal power unit.

[0048] In one embodiment, the method also includes: when the difference between the actual value and the predicted value of the second operating data is greater than a first preset threshold, outputting an abnormal prompt and obtaining a correction value of the second operating data input by the user; determining the current power instruction value based on the actual value and the correction value; and updating the prediction model based on the first operating data and the correction value of the second operating data.

[0049] Here, the situation where the difference between the actual value and the predicted value of the second operating data is greater than the first preset threshold value means that the prediction result given by the prediction model has a large deviation from the actual second operating data. In this case, if the current power command value is determined based on the predicted value, the final adjusted air supply and coal supply will fluctuate to a large extent, which will reduce the life of the thermal power unit. In the event of a large prediction deviation, the user needs to be reminded. At the same time, the user is prompted to manually correct the predicted value of the second operating data. By comparing the actual value and the corrected value of the second operating data, a more accurate current power command value can be obtained. The first operating data and the corrected second operating data are used together to update the prediction model. This allows the model to adapt to changes in actual operating conditions and improves the accuracy and reliability of the prediction.

[0050] By correcting prediction deviations through manual intervention and using the corrected data to continuously optimize the prediction model, the prediction accuracy of the model in actual operation can be continuously improved.

[0051] In one embodiment, the method further includes: sending the current power command value to an automatic power generation control system, so that the automatic power generation control system adjusts its feedforward control parameters and feedback control parameters according to the current power command value.

[0052] It should be understood that although the historical power command value in the first operation data is obtained from the automatic power generation control system, a more accurate current power command value is obtained through the previous prediction model optimization. Therefore, it is necessary to feed back this optimized and more accurate current power command value to the automatic power generation control system.

[0053] The feedforward control parameter is the parameter that the automatic power generation control system uses to adjust the power command value in advance according to its predicted grid load information. The feedback control parameter is the parameter that the automatic power generation control system uses to adjust the power command value according to the deviation between the actual active power value and the power command value. After receiving the optimized current power command value, the automatic power generation control system can adjust these two types of control parameters accordingly to achieve more accurate power output control. By optimizing the current power command value through the prediction model and feeding it back to the automatic power generation control system, the automatic power generation control system can perform feedforward and feedback control according to more accurate command values. Ultimately, the power output control accuracy of the entire thermal power unit is improved.

[0054] In one embodiment, adjusting the coal supply amount and the air supply amount according to the current power command value includes: The current power command value is sent to a distributed control system, and the distributed control system controls the coal feeding system to adjust the coal feeding amount and controls the air supply system to adjust the air supply amount.

[0055] Here, the optimized current power command value is sent to the distributed control system, so that the distributed control system can adjust the parameters of related subsystems according to this command value. The coal feeding system controls the fuel supply of the boiler, which directly affects the power output of the thermal power unit. The distributed control system will adjust the relevant parameters of the coal feeding system, such as the speed and coal supply of the coal feeder, according to the current power command value to meet the power demand. The air supply system controls the air intake to the boiler, which also affects the power output of the thermal power unit. The distributed control system will adjust the speed, air supply and other parameters of the air blower according to the current power command value to cooperate with the adjustment of the coal feeding system to jointly ensure the accurate output of the thermal power unit power.

[0056] Figure 2 is a block diagram of a control device 200 for a thermal power unit according to an exemplary embodiment. Figure 2 As shown, the control device 200 of the thermal power unit includes: The data acquisition module 210 is used to acquire the operation data of the thermal power unit, wherein the operation data includes the actual values ​​of the first operation data and the second operation data, wherein the first operation data includes one or more of the following data: historical power command value, grid frequency, actual value of active power, boiler thermal efficiency, load response time, steam turbine heat rate, power generation coal consumption rate of the thermal power unit, power supply coal consumption rate, coal feeding system parameters and air supply system parameters, and the second operation data includes the equipment operation data of the thermal power unit; A data prediction module 220, configured to input the first operation data into a trained prediction model to obtain a predicted value of the second operation data; A power command module 230, configured to determine a current power command value according to the actual value and the predicted value when the difference between the actual value and the predicted value is less than a first preset threshold; The thermal power unit adjustment module 240 is used to adjust the coal supply and air supply according to the current power command value.

[0057] Optionally, the data prediction module 220 is specifically configured to: The operation data collected by the distributed control system is obtained, wherein the distributed control system transmits the operation data to a data collection center through an electronic room server.

[0058] Optionally, the device includes a prediction model training module, which is used to: Obtain historical operation data of thermal power units; Selecting historical operation data in which the difference between the actual value of active power and the historical power command value does not exceed a second preset threshold from the historical operation data as training data; Determine training samples and labels based on the training data, wherein the training samples include one or more of the following data: historical power command value, grid frequency, actual active power value, boiler thermal efficiency, load response time, steam turbine heat rate, thermal power generation coal consumption rate, power supply coal consumption rate, coal feeding system parameters and air supply system parameters, and the labels include equipment operation data of the thermal power unit; Inputting the training samples into a neural network model to obtain a predicted value; Adjusting the parameters of the neural network model according to the difference between the predicted value and the label; Return to the step of inputting the training sample into the neural network model to obtain a prediction value, until the difference between the prediction value and the label is less than a third preset threshold or the number of cycles reaches a preset number, to obtain the trained prediction model.

[0059] Optionally, the device further comprises a correction module, configured to: When the difference between the actual value and the predicted value of the second operating data is greater than a first preset threshold, outputting an abnormal prompt and obtaining a correction value of the second operating data input by the user; Determining the current power command value according to the actual value and the correction value; The prediction model is updated according to the correction values ​​of the first operation data and the second operation data.

[0060] Optionally, the device further comprises an output module, configured to: The current power command value is sent to an automatic power generation control system, so that the automatic power generation control system adjusts its feedforward control parameters and feedback control parameters according to the current power command value.

[0061] Optionally, the thermal power unit adjustment module 240 is specifically used for: The current power command value is sent to a distributed control system, and the distributed control system controls the coal feeding system to adjust the coal feeding amount and controls the air supply system to adjust the air supply amount.

[0062] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0063] Figure 3 FIG. 1 is a block diagram of an electronic device 300 according to an exemplary embodiment. Figure 3 As shown, the electronic device 300 may include: a processor 301 , a memory 302 . The electronic device 300 may also include one or more of a multimedia component 303 , an input / output (I / O) interface 304 , and a communication component 305 .

[0064] The processor 301 is used to control the overall operation of the electronic device 300 to complete all or part of the steps in the control method of the thermal power unit. The memory 302 is used to store various types of data to support the operation of the electronic device 300. For example, these data may include instructions for any application or method used to operate on the electronic device 300, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 303 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 302 or sent through the communication component 305. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 304 provides an interface between the processor 301 and other interface modules, and the other interface modules may be keyboards, mice, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 305 is used for wired or wireless communication between the electronic device 300 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 305 may include: Wi-Fi module, Bluetooth module, NFC module, etc.

[0065] In an exemplary embodiment, the electronic device 300 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned control method of the thermal power unit.

[0066] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the control method of the thermal power unit described above are implemented. For example, the computer-readable storage medium may be the memory 302 including the program instructions, and the program instructions may be executed by the processor 301 of the electronic device 300 to complete the control method of the thermal power unit described above.

[0067] In another exemplary embodiment, a computer program product is also provided. The computer program product includes a computer program that can be executed by a programmable device. The computer program has a code portion for executing the above-mentioned control method of the thermal power unit when executed by the programmable device.

[0068] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0069] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0070] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. A control method for a thermal power unit, characterized in that: The method comprises: Acquire operation data of the thermal power unit, the operation data including actual values ​​of first operation data and second operation data, the first operation data including one or more of the following data: historical power command value, grid frequency, actual value of active power, boiler thermal efficiency, load response time, steam turbine heat rate, power generation coal consumption rate of the thermal power unit, power supply coal consumption rate, coal feeding system parameters and air supply system parameters, the second operation data including equipment operation data of the thermal power unit; Inputting the first operation data into the trained prediction model to obtain a prediction value of the second operation data; When the difference between the actual value and the predicted value is less than a first preset threshold, determining a current power command value according to the actual value and the predicted value; The coal supply and air supply are adjusted according to the current power command value.

2. The method according to claim 1, characterized in that The obtaining of the operating data of the thermal power unit comprises: The operation data collected by the distributed control system is obtained, wherein the distributed control system transmits the operation data to a data collection center through an electronic room server.

3. The method according to claim 1, characterized in that The trained prediction model is obtained by the following steps: Obtain historical operation data of thermal power units; Selecting historical operation data in which the difference between the actual value of active power and the historical power command value does not exceed a second preset threshold from the historical operation data as training data; Determine training samples and labels based on the training data, wherein the training samples include one or more of the following data: historical power command value, grid frequency, actual active power value, boiler thermal efficiency, load response time, steam turbine heat rate, thermal power generation coal consumption rate, power supply coal consumption rate, coal feeding system parameters and air supply system parameters, and the labels include equipment operation data of the thermal power unit; Inputting the training samples into a neural network model to obtain a predicted value; Adjusting the parameters of the neural network model according to the difference between the predicted value and the label; Return to the step of inputting the training sample into the neural network model to obtain a prediction value, until the difference between the prediction value and the label is less than a third preset threshold or the number of cycles reaches a preset number, to obtain the trained prediction model.

4. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: When the difference between the actual value and the predicted value of the second operating data is greater than a first preset threshold, outputting an abnormal prompt and obtaining a correction value of the second operating data input by the user; Determining the current power command value according to the actual value and the correction value; The prediction model is updated according to the correction values ​​of the first operation data and the second operation data.

5. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: The current power command value is sent to an automatic power generation control system, so that the automatic power generation control system adjusts its feedforward control parameters and feedback control parameters according to the current power command value.

6. The method according to any one of claims 1 to 3, characterized in that: The step of adjusting the coal supply amount and the air supply amount according to the current power command value comprises: The current power command value is sent to a distributed control system, and the distributed control system controls the coal feeding system to adjust the coal feeding amount and controls the air supply system to adjust the air supply amount.

7. A control device for a thermal power unit, characterized in that: include: A data acquisition module, used for acquiring operation data of the thermal power unit, the operation data including actual values ​​of first operation data and second operation data, the first operation data including one or more of the following data: historical power command value, grid frequency, actual value of active power, boiler thermal efficiency, load response time, steam turbine heat rate, power generation coal consumption rate of the thermal power unit, power supply coal consumption rate, coal feeding system parameters and air supply system parameters, the second operation data including equipment operation data of the thermal power unit; A data prediction module, used for inputting the first operation data into the trained prediction model to obtain a predicted value of the second operation data; A power command module, configured to determine a current power command value according to the actual value and the predicted value when a difference between the actual value and the predicted value is less than a first preset threshold; The thermal power unit adjustment module is used to adjust the coal supply and air supply according to the current power command value.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

9. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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