A scheduling method and device for a pulverizing system of a thermal power unit

Abstract

The present invention belongs to the field of electric power and discloses a scheduling method and device for a pulverizing system of a thermal power unit, comprising: obtaining a unit load instruction for the thermal power unit at the current moment, and inputting the unit load instruction at the current moment into a pre-established load instruction prediction model, so that the load instruction prediction model predicts the unit load instruction at a future moment based on the unit load instruction at the current moment; inputting the predicted unit load instruction at the future moment into a pre-established start-stop scheduling rule for the pulverizing system to determine the number of pulverizing systems of the thermal power unit in operation at the future moment and the start-stop time of each pulverizing system; and scheduling multiple pulverizing systems in the thermal power unit based on the determined number of operations and start-stop times. The present invention realizes the full autonomous start-stop of the pulverizing system without human intervention, tracks and responds to power grid load demand, and significantly improves the flexible operation capability of the thermal power unit.

Description

Technical Field

[0001] The present invention belongs to the field of electric power, relates to a dispatching method, and in particular to a dispatching method and device for a pulverizing system of a thermal power unit. Background Art

[0002] In the context of building a new power system dominated by renewable energy, new energy sources, primarily wind and photovoltaic power, will become the primary suppliers of electricity to the system. However, due to the inherent randomness, volatility, and intermittency of renewable energy generation, the integration of large-scale renewable energy into the grid will significantly increase the difficulty of maintaining a spatial and temporal balance of power within the power system. Ensuring balanced power supply and demand across various timescales and high-level absorption of renewable energy sources requires enhancing the flexible peak-shaving capabilities of the new power system, which is also the greatest challenge currently facing the construction of this new power system. Currently and for a long time to come, thermal power units will remain the primary source of peak-shaving power in my country's power system, shifting their role from supporting power sources to regulating power sources. Therefore, improving the speed and safety of the start-up and shutdown transitions between various operating conditions and various types of equipment during peak-shaving operations is the primary approach to enhancing the flexible peak-shaving capabilities of the power system.

[0003] As an essential component of the operation of thermal power units, the safe and economical operation of the pulverizing system directly determines the economic and safety of the unit's operation. In the current new power system structure, thermal power units need to frequently make large load changes to achieve peak load regulation. During the rapid peak load regulation process, the output of the pulverizing system needs to match the unit's load tracking capability in real time. The flexible peak load regulation of thermal power units leads to more frequent start and stop of the pulverizing system. The start and stop of the pulverizing system not only includes the start and stop of the pulverizer and coal feeder, but also includes the opening and closing of their related electric and pneumatic doors, the automatic control of related valves, and the automatic setting of the control object set values. During the startup process, it is necessary to complete functions such as warming up the mill, establishing the start-up air volume, and ignition. It is also necessary to control the cold primary air valve and hot primary air valve of the pulverizer, the dynamic separator frequency, and the coal feeder speed. Therefore, the frequent start and stop of the pulverizing system leads to high manual operation intensity, greatly increasing the risk of frequent operation and operator error. At the same time, the start and stop operation of the pulverizing system occupies most of the operator's operation time during the peak regulation process, and it is often difficult for operators to adjust the system operation status quickly and accurately. There are problems such as strong subjective experience in start and stop decisions, and the optimal start and stop timing has not been deeply explored. Summary of the Invention

[0004] In view of this, the present invention discloses a scheduling method and device for a pulverizing system of a thermal power unit, which can solve the deficiencies existing in the related art.

[0005] To achieve the above purpose, the present invention discloses the following technical solutions:

[0006] According to a first aspect of the present invention, a scheduling method for a pulverizing system of a thermal power plant is proposed, the method comprising:

[0007] Obtaining a unit load instruction for a thermal power unit at a current moment, and inputting the unit load instruction at the current moment into a pre-established load instruction prediction model, so that the load instruction prediction model predicts the unit load instruction at a future moment based on the unit load instruction at the current moment;

[0008] The predicted unit load instructions at the future time are input into the pre-established start-stop scheduling rules of the pulverizing system to determine the number of pulverizing systems of the thermal power unit in operation at the future time and the start-stop time of each pulverizing system;

[0009] The multiple pulverizing systems in the thermal power unit are scheduled based on the determined operating quantity and start-stop time.

[0010] According to a second aspect of the present invention, a scheduling device for a pulverizing system of a thermal power plant is proposed, the device comprising:

[0011] Prediction unit: obtains the unit load instruction for the thermal power unit at the current moment, and inputs the unit load instruction at the current moment into a pre-established load instruction prediction model, so that the load instruction prediction model predicts the unit load instruction at a future moment based on the unit load instruction at the current moment;

[0012] Determination unit: The predicted unit load instruction at a future time is input into the pre-established start-stop scheduling rule of the pulverizing system to determine the number of pulverizing systems of the thermal power unit in operation and the start-stop time of each pulverizing system at a future time;

[0013] Scheduling unit: scheduling multiple pulverizing systems in the thermal power unit based on the determined operating quantity and start and stop time.

[0014] According to a third aspect of the present invention, an electronic device is provided, comprising:

[0015] processor;

[0016] a memory for storing processor-executable instructions;

[0017] The processor implements the steps of the method described in the first aspect by running the executable instructions.

[0018] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which computer instructions are stored. When the instructions are executed by a processor, the steps of the method described in the first aspect are implemented.

[0019] It can be seen from the above technical scheme that the scheduling method for the pulverizing system of a thermal power unit disclosed in the present invention predicts the unit load instructions of the thermal power unit at future times through the unit load instructions obtained through training, and determines the number of operating pulverizing systems of the thermal power unit at future times and the start and stop time of each pulverizing system based on the predicted unit load instructions at future times and the pre-established start and stop scheduling rules of the pulverizing system, thereby realizing the full autonomous start and stop of the pulverizing system without human intervention, tracking and responding to the load demand of the power grid, and significantly improving the flexible operation capability of the thermal power unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a flowchart of a scheduling method for a pulverizing system of a thermal power unit provided by an exemplary embodiment.

[0021] Figure 2 The present invention is a schematic diagram of an automatic start-stop control system of a pulverizing system of a thermal power unit based on load forecasting, provided by an exemplary embodiment.

[0022] Figure 3 It is a flow chart of a warm grinding control method of an automatic start-stop closed-loop control function module of a pulverizing system provided by an exemplary embodiment.

[0023] Figure 4 It is a schematic diagram of an automatic warm grinding control logic provided by an exemplary embodiment.

[0024] Figure 5 It is a flow chart of a method for controlling the grinding of a pulverizing system using an automatic start-stop closed-loop control function module, provided by an exemplary embodiment.

[0025] Figure 6 It is a flow chart of a grinding stop control method of an automatic start-stop closed-loop control function module of a pulverizing system provided by an exemplary embodiment.

[0026] Figure 7 It is a schematic structural diagram of a device provided by an exemplary embodiment.

[0027] Figure 8 This is a block diagram of a scheduling device for a pulverizing system of a thermal power unit provided by an exemplary embodiment. DETAILED DESCRIPTION

[0028] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible implementations consistent with one or more embodiments of the present invention. Rather, they are merely examples of apparatuses and methods consistent with certain aspects of one or more embodiments of the present invention, as detailed in the appended claims.

[0029] It should be noted that in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in the present invention. In some other embodiments, the method may include more or fewer steps than those described in the present invention. In addition, a single step described in the present invention may be broken down into multiple steps for description in other embodiments, and multiple steps described in the present invention may be combined into a single step for description in other embodiments.

[0030] To further illustrate the present invention, the following examples are provided:

[0031] Figure 1 This is a flowchart of a scheduling method for a pulverizing system of a thermal power plant provided by an exemplary embodiment. Figure 1 As shown, the method may include the following steps:

[0032] Step 102: Obtain the unit load instruction for the thermal power unit at the current moment, and input the unit load instruction at the current moment into a pre-established load instruction prediction model, so that the load instruction prediction model predicts the unit load instruction at future moments based on the unit load instruction at the current moment.

[0033] In one embodiment, if Figure 2 As shown, the training process of the load instruction prediction model includes: obtaining an instruction training data set, each training sample in the instruction training data set corresponds to the unit load instruction of the thermal power unit at a certain moment; performing data preprocessing on the instruction training data set through a variable modal decomposition algorithm, and inputting the processed modal components into a to-be-trained model established based on a bidirectional long-short-term memory neural network for training, wherein the bidirectional long-short-term memory neural network is used to mine the dependency relationship between the modal components in the forward and reverse time series.

[0034] Thermal power unit load instruction training sample preprocessing, through variational mode decomposition, the unit load instruction is decomposed into An intrinsic mode function (IMF) with an optimal center frequency and limited bandwidth contains the nonlinear and timing characteristics of the load instruction time series.

[0035] A load instruction prediction model is built based on a bidirectional long short-term memory (BiLSTM) neural network. The IMF obtained after VDM decomposition is input into the BiLSTM network to mine its dependencies in forward and reverse time series. The BiLSTM network can simultaneously consider forward and backward information at each time step in the time series, thereby establishing a high-precision load instruction prediction model.

[0036] Furthermore, the variational problem can be described as: modal components Minimize the center frequency of the modal component under the constraint that the sum is equal to the original signal The optimization goal of the sum of the bandwidth is:

[0037] ;

[0038] ;

[0039] ;

[0040] ;

[0041] ;

[0042] ;

[0043] ;

[0044] ;

[0045] in, 、 represent input and output features respectively, 、 、 Represent the outputs of the forget gate, input gate, and output gate respectively. 、 Represent the candidate and updated memory outputs respectively, 、 Represent the corresponding weight matrix and bias matrix respectively, Output features for the reverse LSTM.

[0046] The bidirectional long-short-term memory neural network selectively retains or deletes information by introducing memory units and three gating mechanisms (forget gate, input gate, and output gate). The variable mode decomposition (VMD) algorithm is used to decompose the unit load command data, and a total of 8 groups of intrinsic mode functions (IMFs) are obtained. After obtaining the IMF features, they are normalized and input into the BiLSTM to construct a load command prediction model with a prediction length of 1 hour and a step length of 1 minute to accurately predict future unit load command trends.

[0047] In this embodiment, the unit load instructions of the training samples are preprocessed based on the VMD algorithm. VMD decomposition can decompose the unit load instruction signal into a series of modal components with specific center frequencies and limited bandwidths. IMF represents unit load instruction information with different frequency characteristics.

[0048] Step 104 , the predicted unit load instructions at future moments are input into the pre-established start-stop scheduling rules of the pulverizing system to determine the number of pulverizing systems of the thermal power unit in operation at future moments and the start-stop times of each pulverizing system.

[0049] Step 106: Scheduling the multiple pulverizing systems in the thermal power unit based on the determined operating quantity and start / stop time.

[0050] In this embodiment, the unit load instructions obtained through training are used to predict the unit load instructions of the thermal power units at future times, and the number of running pulverizing systems of the thermal power units at future times and the start and stop times of each pulverizing system are determined based on the predicted unit load instructions at future times and the pre-established start and stop scheduling rules of the pulverizing system. In this way, the pulverizing system can be started and stopped autonomously throughout the process without human intervention, and the load demand of the power grid can be tracked and responded to, thereby significantly improving the flexible operation capability of the thermal power units.

[0051] In one embodiment, the method further includes: obtaining thermal power unit parameters and establishing a mathematical model of the pulverizing system; and determining the safety margin of the pulverizing system according to the mathematical model of the pulverizing system and the abnormality of the pulverizing system. , Time margin for equipment start and stop time deviation compensation , confidence vector of future load forecast correction Establish the start-stop scheduling rules.

[0052] To establish a mathematical model for the milling system, it is necessary to consider the mill inlet primary air temperature, mill air-powder mixing temperature, warm mill heating rate setting and its mill start time. , grinding downtime Related coal type heat, instantaneous coal quantity of coal feeder and output capacity , unit energy consumption Formulate the autonomous decision-making control rules for the start and stop of the pulverizing system, and introduce a safety margin considering the abnormality of the unit pulverizing system based on the load instruction prediction model to predict the future unit load instruction trend and the total output capacity of the pulverizing system of the current unit operation. , Time margin for equipment start and stop time deviation compensation and confidence vector for future load forecast correction , establish autonomous decision-making control rules for starting and stopping the flour-making system.

[0053] Furthermore, the thermal power unit parameters include: mill inlet primary air temperature, mill air powder mixing temperature, warm mill heating rate setting, mill start time , grinding downtime , coal type heat, instantaneous coal quantity of coal feeder, output capacity , unit energy consumption ;

[0054] The mathematical model of the flour milling system Described as:

[0055] ;

[0056] in, is the actual coal calorific value, To design the heat of coal, 、 、 They are respectively the instantaneous coal feeding amount of the coal feeder and its upper and lower limits of instruction, is the unit energy consumption curve of coal mill, The primary air temperature at the mill inlet is is the mixing temperature of the grinding air powder, 、 、 are the warm grinding heating rate setting value and its upper and lower limits respectively. 、 They are the sum of the action time of related equipment and valves during the start-up and shutdown processes of the grinding mill.

[0057] The start-stop scheduling rules include start-up scheduling rules and stop-operation scheduling rules.

[0058] The start-up of the pulverizing system is mainly determined by whether the future grid load demand and the total output capacity of the pulverizing system currently in operation are matched. The unit needs to start the next pulverizing system in advance before the current output capacity does not meet the grid load demand. On this basis, a safety margin is introduced to consider the abnormality of the unit's pulverizing system. , Time margin for equipment start and stop time deviation compensation and confidence vector for future load forecast correction ,exist At this moment, the startup scheduling rule is described as:

[0059] ;

[0060] in, is the number of milling system operations, is the total load capacity of the pulverizing system, For load instruction prediction output, is the startup time after time margin compensation, For the grinding time, It is the time margin for starting grinding.

[0061] The shutdown of the pulverizing system is mainly determined by whether the future grid load demand matches the total output capacity of the pulverizing system currently running the unit. When the future grid load demand is less than the total output capacity of the pulverizing system running after the mill is shut down, a pulverizing system can be shut down.

[0062] exist At this moment, the outage scheduling rules are described as follows:

[0063] ;

[0064] in, is the stop time after time margin compensation, is the grinding downtime, It is the grinding downtime margin.

[0065] In one embodiment, the multiple powder making systems in the thermal power unit are scheduled based on the determined operating quantity and start and stop time, including: generating a start and stop scheduling instruction according to the determined operating quantity and start and stop time, and sending the start and stop scheduling instruction to the automatic start and stop closed-loop control function module of the powder making system; giving a start or stop token to the next preset powder making system, and calling the lower-level warm grinding, start grinding, and stop grinding sub-function groups according to the start or stop token.

[0066] The automatic start-stop closed-loop control functional module of the pulverizing system is used to realize the automatic start-stop control of the pulverizing system of the coal-fired unit. It has developed from a single, decentralized and local control technology to autonomous control of the process flow and coordinated control of multiple process flows under multiple working conditions. The functional module configuration is used to encapsulate professional knowledge, operating experience and process mechanisms. Combined with autonomous control technology, the functional module steps of warm-up, startup and shutdown of the medium-speed direct-blowing pulverizing system are designed, which realizes the full autonomous control of the start-stop process of this type of pulverizing system, reducing the manual start-stop operation of the pulverizing system of the coal-fired unit for flexible peak regulation of the power grid.

[0067] like Figure 3 As shown, the operation process of the warm grinding function module of the powder making system is as follows:

[0068] Step 1, open the sealed air duct of the grinding group; Step 2, open the coal feeder and the coal mill outlet door; Step 3, start the coal mill dynamic separator, and the frequency converter of the dynamic separator is automatically turned on; Step 4, open the cold air quick-closing door of the coal mill; Step 5, set the cold air damper of the coal mill to a certain opening for ventilation, and at the same time open the hot air quick-closing door of the coal mill; Step 6, the cold air damper of the coal mill is automatically turned on to control the primary air volume of the mill inlet, and the hot air damper of the coal mill is automatically turned on to control the air temperature at the mill outlet.

[0069] like Figure 4 As shown in the figure, in the pulverizing system warm grinding function module, the cold air adjustment door of the coal mill controls the primary air volume of the mill inlet, and the hot air adjustment door of the coal mill controls the mill outlet temperature as follows:

[0070] In the automatic mill warming control logic, controller selector switches have been added to the mill cold and hot air damper controls. During the warming process, the mill cold air damper control switches to PID 4, automatically controlling the mill inlet primary air volume. By adjusting the cold air damper opening, the mill inlet primary air volume reaches the warming mill air volume. The mill hot air damper control switches to PID 2, automatically controlling the mill outlet temperature. The mill outlet temperature setpoint is automatically increased based on the warming mill temperature rise rate preset by the operator. When the mill outlet air temperature rise rate exceeds the setpoint, the hot air damper is closed; otherwise, the hot air damper is opened. This allows the warming rate to be controlled by adjusting the hot air damper opening until the mill outlet temperature reaches the warming mill temperature. After the warming process is completed and the coal feeder is started, the mill cold air damper control switches to the conventional control PID 3, automatically controlling the mill outlet air temperature. The mill hot air damper control switches to the conventional control PID 1, automatically controlling the mill inlet primary air volume.

[0071] like Figure 5 As shown in the figure, the operation process of the grinding function module of the pulverizing system is as follows:

[0072] Step 1, open the isolation door of the coal feeder inlet; Step 2, automatically adjust the secondary air box of this layer; Step 3, start the dynamic separator of the coal mill, and automatically adjust the frequency converter of the dynamic separator of the coal mill; Step 4, set the air volume for starting the mill, and the conditions for allowing the coal mill to start are met; Step 5, start the coal mill; Step 6, start the coal feeder, and set the minimum coal quantity instruction of the coal feeder; Step 7, automatically control the coal feeder, and level the coal quantity instruction of the coal feeder.

[0073] like Figure 6 As shown, the operation process of the grinding stop function module of the pulverizing system is as follows:

[0074] Step 1: slowly lower the outlet temperature setting value of the coal mill to the first stage temperature; Step 2: release the automatic control of the coal feeder; Step 3: set the coal quantity instruction of the coal feeder to the minimum coal quantity; Step 4: close the coal feeder inlet isolation door; Step 5: wait for the coal quantity feedback of the coal feeder to be less than 3t / h; Step 6: stop the coal feeder, and slowly lower the outlet temperature setting value of the coal mill to the second stage temperature; Step 7: purge the coal mill; Step 8: stop the coal mill.

[0075] Figure 7 This is a schematic structural diagram of a device provided by an exemplary embodiment. Figure 7 At the hardware level, the device includes a processor 702, an internal bus 704, a network interface 706, a memory 708, and a non-volatile memory 710. Of course, it may also include hardware required for other functions. One or more embodiments of the present invention can be implemented based on software, such as the processor 702 reading the corresponding computer program from the non-volatile memory 710 into the memory 708 and then running it. Of course, in addition to software implementation, one or more embodiments of the present invention do not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0076] Please refer to Figure 8 A scheduling device for the pulverizing system of a thermal power unit can be applied to Figure 8 In the device shown, to implement the technical solution of the present invention, the device may include:

[0077] The prediction unit 802 is configured to obtain a unit load instruction for the thermal power unit at a current moment, and input the unit load instruction at the current moment into a pre-established load instruction prediction model, so that the load instruction prediction model predicts the unit load instruction at a future moment based on the unit load instruction at the current moment;

[0078] The determination unit 804 is configured to input the predicted unit load instructions at a future time into a pre-established start-stop scheduling rule for the pulverizing system to determine the number of pulverizing systems of the thermal power unit in operation and the start-stop time of each pulverizing system at a future time;

[0079] The scheduling unit 806 is used to schedule the multiple pulverizing systems in the thermal power unit based on the determined operating quantity and start and stop time.

[0080] Optionally, the method further includes:

[0081] The first construction unit 808 is used to obtain the parameters of the thermal power unit and establish a mathematical model of the pulverizing system;

[0082] The second construction unit 810 is used to determine the safety margin of the milling system according to the milling system mathematical model and the milling system abnormality. , Time margin for equipment start and stop time deviation compensation , confidence vector of future load forecast correction Establish the start-stop scheduling rules.

[0083] Optional,

[0084] The thermal power unit parameters include: mill inlet primary air temperature, mill air powder mixing temperature, warm mill heating rate setting, mill start time , grinding downtime , coal type heat, instantaneous coal quantity of coal feeder, output capacity , unit energy consumption ;

[0085] The mathematical model of the flour milling system Described as:

[0086] ;

[0087] in, is the actual coal calorific value, To design the heat of coal, 、 、 They are respectively the instantaneous coal feeding amount of the coal feeder and its upper and lower limits of instruction, is the unit energy consumption curve of coal mill, The primary air temperature at the mill inlet is is the mixing temperature of the grinding air powder, 、 、 are the warm grinding heating rate setting value and its upper and lower limits respectively. 、 They are the sum of the action time of related equipment and valves during the start-up and shutdown processes of the grinding mill.

[0088] Optionally, the start-stop scheduling rule includes a start-up scheduling rule and a stop-operation scheduling rule;

[0089] The startup scheduling rule is described as:

[0090] ;

[0091] in, is the number of milling system operations, is the total load capacity of the pulverizing system, For load instruction prediction output, is the startup time after time margin compensation, For the grinding time, It is the time margin for starting grinding;

[0092] The outage scheduling rules are described as follows:

[0093] ;

[0094] in, is the stop time after time margin compensation, is the grinding downtime, It is the grinding downtime margin.

[0095] Optionally, the scheduling of multiple pulverizing systems in the thermal power unit based on the determined operating quantity and start / stop time includes:

[0096] Generate a start-stop scheduling instruction according to the determined running quantity and start-stop time, and send the start-stop scheduling instruction to the automatic start-stop closed-loop control function module of the milling system;

[0097] The next preset milling system is given a start or stop token, and the lower-level warm grinding, grinding start, and grinding stop sub-function groups are called according to the start or stop token.

[0098] Optionally, the training process of the load instruction prediction model includes:

[0099] An acquisition unit 812 is configured to acquire an instruction training data set, wherein each training sample in the instruction training data set corresponds to a unit load instruction of a thermal power unit at a certain moment;

[0100] The training unit 814 is used to perform data preprocessing on the instruction training data set through a variable modal decomposition algorithm, and input the processed modal components into a to-be-trained model established based on a bidirectional long-short-term memory neural network for training. The bidirectional long-short-term memory neural network is used to mine the dependency relationship between the modal components in the forward and reverse time sequences.

[0101] Optional, if satisfied modal components Minimize the center frequency of the modal component under the constraint that the sum is equal to the original signal The optimization goal of the sum of the bandwidth is:

[0102] ;

[0103] ;

[0104] ;

[0105] ;

[0106] ;

[0107] ;

[0108] ;

[0109] ;

[0110] in, 、 represent input and output features respectively, 、 、 Represent the outputs of the forget gate, input gate, and output gate respectively. 、 Represent the candidate and updated memory outputs respectively, 、 Represent the corresponding weight matrix and bias matrix respectively, Output features for the reverse LSTM.

[0111] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer, which may be in the form of a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email transceiver, game console, tablet computer, wearable device, or any combination of these devices.

[0112] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0113] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0114] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0115] For the computer-readable medium (or computer-readable storage medium) as described above or in any other form, computer instructions may be stored thereon, which, when executed by a processor, implement one or more of the above-mentioned embodiments, thereby realizing the technical solution of the present invention.

[0116] The present invention further provides a computer program that, when executed by a processor, implements one or more of the aforementioned embodiments, thereby realizing the technical solution of the present invention. The computer program may be recorded on the aforementioned or any other form of computer-readable medium, and the present invention is not limited thereto.

[0117] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0118] The foregoing description describes specific embodiments of the present invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0119] The terms used in one or more embodiments of the present invention are for the purpose of describing specific embodiments only and are not intended to limit one or more embodiments of the present invention. The singular forms "a", "an", "the" and "the" used in one or more embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0120] It should be understood that although the terms first, second, third, etc. may be used to describe various information in one or more embodiments of the present invention, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of one or more embodiments of the present invention, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining."

[0121] The above description is merely a preferred embodiment of one or more embodiments of the present invention and is not intended to limit one or more embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of the present invention shall be included in the scope of protection of one or more embodiments of the present invention.

Claims

1. A scheduling method for a pulverizing system of a thermal power unit, characterized in that: The method comprises: Obtaining a unit load instruction for a thermal power unit at a current moment, and inputting the unit load instruction at the current moment into a pre-established load instruction prediction model, so that the load instruction prediction model predicts the unit load instruction at a future moment based on the unit load instruction at the current moment; Obtain the parameters of the thermal power unit and establish a mathematical model of the pulverizing system; According to the mathematical model of the milling system and the safety margin of the milling system abnormality , Time margin for equipment start and stop time deviation compensation , confidence vector of future load forecast correction Establish the start-stop scheduling rules of the pulverizing system; wherein the thermal power unit parameters include: mill inlet primary air temperature, mill air-powder mixing temperature, warm mill heating rate setting, mill start time , grinding downtime , coal type heat, instantaneous coal quantity of coal feeder, output capacity , unit energy consumption ; The mathematical model of the flour milling system Described as: ； in, is the actual coal calorific value, To design the heat of coal, 、 、 They are respectively the instantaneous coal feeding amount of the coal feeder and its upper and lower limits of instruction, is the unit energy consumption curve of coal mill, The primary air temperature at the mill inlet is is the mixing temperature of the grinding air powder, 、 、 are the warm grinding heating rate setting value and its upper and lower limits respectively. 、 The sum of the action time of related equipment and valves during the start-up and shutdown process of the grinding mill respectively; The start-stop scheduling rules include start-up scheduling rules and stop-operation scheduling rules; The startup scheduling rule is described as: ； in, is the number of milling system operations, is the total load capacity of the pulverizing system, For load instruction prediction output, is the startup time after time margin compensation, For the grinding time, It is the time margin for starting grinding; The outage scheduling rule is described as follows: ； in, is the stop time after time margin compensation, is the grinding downtime, is the grinding downtime margin; The predicted unit load instructions at the future time are input into the pre-established start-stop scheduling rules of the pulverizing system to determine the number of pulverizing systems of the thermal power unit in operation at the future time and the start-stop time of each pulverizing system; The multiple pulverizing systems in the thermal power unit are scheduled based on the determined operating quantity and start-stop time.

2. The method according to claim 1, characterized in that The scheduling of the multiple pulverizing systems in the thermal power unit based on the determined operating quantity and start / stop time includes: Generate a start-stop scheduling instruction according to the determined running quantity and start-stop time, and send the start-stop scheduling instruction to the automatic start-stop closed-loop control function module of the milling system; The next preset milling system is given a start or stop token, and the lower-level warm grinding, grinding start, and grinding stop sub-function groups are called according to the start or stop token.

3. The method according to claim 1, characterized in that The training process of the load instruction prediction model includes: Acquire an instruction training data set, where each training sample in the instruction training data set corresponds to a unit load instruction of a thermal power unit at a certain moment; The instruction training data set is preprocessed by a variable modal decomposition algorithm, and the modal components obtained by the processing are input into a training model established based on a bidirectional long-short-term memory neural network for training. The bidirectional long-short-term memory neural network is used to mine the dependency relationship between modal components in forward and reverse time sequences.

4. The method according to claim 3, characterized in that In satisfaction modal components Minimize the center frequency of the modal component under the constraint that the sum is equal to the original signal The optimization goal of the sum of the bandwidth is: ； ； ； ； ； ； ； ； in, 、 represent input and output features respectively, 、 、 Represent the outputs of the forget gate, input gate, and output gate respectively. 、 Represent the candidate and updated memory outputs respectively, 、 Represent the corresponding weight matrix and bias matrix respectively, Output features for the reverse LSTM.

5. A scheduling device for a pulverizing system of a thermal power unit, characterized in that: The device comprises: Prediction unit: obtains the unit load instruction for the thermal power unit at the current moment, and inputs the unit load instruction at the current moment into a pre-established load instruction prediction model, so that the load instruction prediction model predicts the unit load instruction at a future moment based on the unit load instruction at the current moment; The first construction unit: obtain the parameters of the thermal power unit and establish the mathematical model of the pulverizing system; The second construction unit: according to the mathematical model of the milling system and the safety margin of the milling system abnormality , Time margin for equipment start and stop time deviation compensation , confidence vector of future load forecast correction Establish the start-stop scheduling rules of the pulverizing system; wherein the thermal power unit parameters include: mill inlet primary air temperature, mill air-powder mixing temperature, warm mill heating rate setting, mill start time , grinding downtime , coal type heat, instantaneous coal quantity of coal feeder, output capacity , unit energy consumption ; The mathematical model of the flour milling system Described as: ； in, is the actual coal calorific value, To design the heat of coal, 、 、 They are respectively the instantaneous coal feeding amount of the coal feeder and its upper and lower limits of instruction, is the unit energy consumption curve of coal mill, The primary air temperature at the mill inlet is is the mixing temperature of the grinding air powder, 、 、 are the warm grinding heating rate setting value and its upper and lower limits respectively. 、 The sum of the action time of related equipment and valves during the start-up and shutdown process of the grinding mill respectively; The start-stop scheduling rules include start-up scheduling rules and stop-operation scheduling rules; The startup scheduling rule is described as: ； in, is the number of milling system operations, is the total load capacity of the pulverizing system, For load instruction prediction output, is the startup time after time margin compensation, For the grinding time, It is the time margin for starting grinding; The outage scheduling rule is described as follows: ； in, is the stop time after time margin compensation, is the grinding downtime, is the grinding downtime margin; Determination unit: The predicted unit load instruction at a future time is input into the pre-established start-stop scheduling rule of the pulverizing system to determine the number of pulverizing systems of the thermal power unit in operation and the start-stop time of each pulverizing system at a future time; Scheduling unit: scheduling multiple pulverizing systems in the thermal power unit based on the determined operating quantity and start and stop time.

6. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor implements the steps of the method according to any one of claims 1 to 4 by running the executable instructions.

7. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the instruction is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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