CN115013340B - Early warning method and device for adjusting blade failure of axial flow fan in thermal power plant - Google Patents

Abstract

The present application is about an early warning method and device for an axial flow fan blade adjustment failure in a thermal power plant. The specific scheme is: obtaining the real-time operation data of the fan; determining the fan volume flow rate and the fan specific pressure energy based on the real-time operation data of the fan; inputting the fan volume flow rate and the fan specific pressure energy into a pre-trained blade opening prediction model; obtaining the fan blade opening prediction value output by the blade opening prediction model; obtaining the fan blade opening real-time feedback value based on the fan real-time operation data; judging the state of the fan blade based on the fan blade opening real-time feedback value and the fan blade opening prediction value, obtaining a first judgment result, and outputting a warning signal in response to the first judgment result not meeting the first preset requirement. The present application can timely detect the moving blade adjustment failure and issue a warning, effectively improving the early warning efficiency and accuracy.

Description

Early warning method and device for adjusting blade failure of axial flow fan in thermal power plant

Technical Field

The present application relates to the technical field of intelligent diagnosis of equipment failures, and in particular to an early warning method and device for adjusting blade failures of axial flow fans in thermal power plants.

Background technique

In the related technology, axial flow fans with adjustable blades are widely used as induced draft fans, forced draft fans and primary fans in the thermal power industry due to their high efficiency and large flow rate. In order to adapt to the fan output requirements under different loads, axial flow fans with adjustable blades can change the mechanical angle of the fan blades through the blade adjustment mechanism, thereby changing the fan output. Since the blade adjustment mechanism needs to be frequently operated to adapt to different fan output requirements, failures of the blade adjustment mechanism often occur during the operation of the fan. If they are not discovered in time, it may cause negative pressure or air volume fluctuations in the power plant boiler, deterioration of combustion, or even stall and surge of the fan, or even cause the boiler to extinguish.

Summary of the invention

To this end, the present application provides a method and device for early warning of axial flow fan blade adjustment failure in a thermal power plant. The technical solution of the present application is as follows:

According to a first aspect of an embodiment of the present application, a method for early warning of an axial flow fan blade adjustment failure in a thermal power plant is provided, the method comprising:

Obtain real-time operation data of the fan;

Determining the fan volume flow rate and the fan specific pressure energy based on the real-time operation data of the fan;

Inputting the fan volume flow rate and fan specific pressure energy into a pre-trained blade opening prediction model;

Obtain the predicted value of the fan blade opening output by the blade opening prediction model;

Based on the real-time operation data of the fan, a real-time feedback value of the fan blade opening is obtained;

Based on the real-time feedback value of the wind turbine rotor blade opening and the predicted value of the wind turbine rotor blade opening, the state of the wind turbine rotor blade is judged to obtain a first judgment result, and in response to the first judgment result not meeting a first preset requirement, an early warning signal is output.

According to an embodiment of the present application, judging the state of the wind turbine rotor blade based on the real-time feedback value of the wind turbine rotor blade opening and the predicted value of the wind turbine rotor blade opening to obtain a first judgment result, and outputting a warning signal in response to the first judgment result not meeting a first preset requirement, including:

Subtracting the wind turbine blade opening real-time feedback value from the wind turbine blade opening prediction value to obtain a first difference;

Obtaining a preset first threshold;

Compare the first difference with the first threshold to obtain a first comparison result;

In response to the first comparison result being that the first difference is greater than or less than the first threshold, it is determined that the state of the wind turbine rotor blade is a fault state, and an early warning signal is output.

According to an embodiment of the present application, after obtaining the real-time feedback value of the fan blade opening based on the real-time operation data of the fan, the method further includes:

Based on the real-time operation data of the fan, a real-time instruction value of the fan blade opening is obtained;

Based on the real-time feedback value of the wind turbine blade opening and the real-time instruction value of the wind turbine blade opening, the state of the wind turbine blade is judged to obtain a second judgment result, and in response to the second judgment result not meeting the second preset requirement, an early warning signal is output.

According to an embodiment of the present application, judging the state of the wind turbine rotor blade based on the wind turbine rotor blade opening real-time feedback value and the wind turbine rotor blade opening real-time instruction value to obtain a second judgment result, and outputting a warning signal in response to the second judgment result not meeting a second preset requirement, including:

Subtracting the wind turbine blade opening real-time feedback value from the wind turbine blade opening real-time instruction value to obtain a second difference;

Obtaining a preset second threshold;

Compare the second difference with the second threshold to obtain a second comparison result;

In response to the second comparison result being that the second difference is greater than or less than the second threshold, it is determined that the state of the wind turbine rotor blade is a fault state, and an early warning signal is output.

According to a second aspect of an embodiment of the present application, there is provided an early warning device for an axial flow fan rotor blade adjustment failure in a thermal power plant, the device comprising:

The first acquisition module is used to acquire the real-time operation data of the wind turbine;

A determination module, used to determine the fan volume flow rate and the fan specific pressure energy based on the real-time operation data of the fan;

An input module, used for inputting the fan volume flow rate and fan specific pressure energy into a pre-trained blade opening prediction model;

The second acquisition module is used to obtain the predicted value of the wind turbine blade opening output by the blade opening prediction model;

A third acquisition module is used to obtain a real-time feedback value of the fan blade opening based on the real-time operation data of the fan;

The first warning module is used to judge the state of the wind turbine rotor blade based on the real-time feedback value of the wind turbine rotor blade opening and the predicted value of the wind turbine rotor blade opening, obtain a first judgment result, and output a warning signal in response to the first judgment result not meeting a first preset requirement.

According to one embodiment of the present application, the first early warning module includes:

A subtraction submodule, used for subtracting the wind turbine rotor blade opening real-time feedback value from the wind turbine rotor blade opening prediction value to obtain a first difference;

A first acquisition submodule, used to acquire a preset first threshold;

A first comparison submodule, used for comparing the first difference with the first threshold to obtain a first comparison result;

The first warning submodule is used to determine that the state of the wind turbine rotor blade is a fault state and output a warning signal in response to the first comparison result being that the first difference is greater than or less than the first threshold.

According to one embodiment of the present application, the device further includes:

A fourth acquisition module is used to acquire a real-time instruction value of the fan blade opening based on the real-time operation data of the fan;

The second warning module is used to judge the state of the wind turbine blades based on the real-time feedback value of the wind turbine blade opening and the real-time instruction value of the wind turbine blade opening, obtain a second judgment result, and output a warning signal in response to the second judgment result not meeting the second preset requirement.

According to one embodiment of the present application, the second early warning module includes:

A second subtraction submodule is used to subtract the wind turbine blade opening real-time feedback value from the wind turbine blade opening real-time instruction value to obtain a second difference;

A second acquisition submodule, used to acquire a preset second threshold;

A second comparison submodule, used for comparing the second difference with the second threshold to obtain a second comparison result;

The second warning submodule is used to determine that the state of the wind turbine rotor blade is a fault state and output a warning signal in response to the second comparison result being that the second difference is greater than or less than the second threshold.

According to a third aspect of an embodiment of the present application, there is provided an electronic device, including:

at least one processor; and

a memory communicatively connected to the at least one processor; wherein,

The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform any method according to the first aspect.

According to a fourth aspect of an embodiment of the present application, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute any one of the methods described in the first aspect.

According to a fifth aspect of an embodiment of the present application, a computer program product is provided, comprising a computer program, wherein the computer program implements the method as described in any one of the first aspects when executed by a processor.

The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

The fan rotor blade opening is predicted by the blade opening prediction model, and the predicted value of the fan rotor blade opening is compared with the real-time feedback value of the fan rotor blade opening, so that the rotor blade adjustment failure can be discovered in time and early warning can be issued, which effectively improves the early warning efficiency and accuracy.

It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application, and do not constitute improper limitations on the present application.

FIG1 is a flow chart of an early warning method for adjusting blade failure of an axial flow fan in a thermal power plant proposed in an embodiment of the present application;

FIG2 is a performance characteristic diagram of a fan proposed in an embodiment of the present application;

FIG3 is a structural block diagram of an early warning device for adjusting blade failure of an axial flow fan in a thermal power plant proposed in an embodiment of the present application;

FIG. 4 is a block diagram of a computer device proposed in an embodiment of the present application.

Detailed ways

In order to enable ordinary persons in the art to better understand the technical solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims.

FIG1 is a flow chart of an early warning method for adjusting failure of axial flow fan blades in a thermal power plant proposed in an embodiment of the present application.

As shown in FIG1 , the early warning method for the axial flow fan blade adjustment failure in the thermal power plant includes:

Step 101, obtaining real-time operation data of the wind turbine.

Optionally, the real-time operation data of the fan may include fan inlet temperature, fan outlet temperature, fan outlet pressure, fan inlet pressure, fan mass flow rate, fan blade opening instruction value, and fan blade opening feedback value.

Step 102, determining the fan volume flow rate and the fan specific pressure energy based on the real-time operation data of the fan.

As a possible example, the calculation method of fan volume flow and fan specific pressure energy includes:

Step 1021, calculate the density of the flowing medium under certain temperature and pressure conditions, wherein the flowing medium may be air or flue gas:

Wherein: i=1 represents the fan inlet, i=2 represents the fan outlet; _ρi is the fan inlet/outlet medium density, in kg/ ^m3 ; _Ti is the fan inlet/outlet medium temperature, in °C; _Pi is the fan inlet/outlet medium static pressure, in Pa; _P0 is the ambient atmospheric pressure, in Pa.

Step 1022, calculate the fan inlet and outlet volume flow:

Where: Vi _is the air volume flow rate at the fan inlet/outlet, in ^m3 /s; q is the fan medium mass flow rate, in kg/s.

Step 1023, calculate the flow rate at the fan inlet and outlet:

Where: _vi is the fan inlet/outlet flow rate, in m/s; _Si is the fan inlet/outlet air duct cross-sectional area, in ^m2 ;

Step 1024, calculate the dynamic pressure energy at the fan inlet and outlet:

Where: Pd _,i is the fan inlet/outlet dynamic pressure, unit is Pa.

Step 1025, calculate the total pressure rise of the fan:

Ap＝P ₂ +P _{d, 2} -P ₁ -P _{d, 1} (5)

Where: ΔP is the total pressure rise of the fan, in Pa.

Step 1026, calculate the specific pressure energy of the fan:

Where: Y is the fan specific pressure energy, unit is J/kg.

Step 103, inputting the fan volume flow rate and the fan specific pressure energy into a pre-trained blade opening prediction model.

Step 104, obtaining the predicted value of the wind turbine blade opening output by the blade opening prediction model.

As an example of a possible implementation, the fan volume flow and fan specific pressure can be input into a pre-trained blade opening prediction model. The blade opening prediction model predicts the fan rotor blade opening through the fan volume flow and fan specific pressure to obtain a predicted value of the fan rotor blade opening.

Step 105, based on the real-time operation data of the wind turbine, obtain the real-time feedback value of the fan blade opening.

As an example of a possible implementation, a real-time feedback value of a fan blade opening is obtained from the real-time operation data of the fan.

Step 106, judging the state of the wind turbine blades based on the real-time feedback value of the wind turbine blade opening and the predicted value of the wind turbine blade opening, obtaining a first judgment result, and outputting a warning signal in response to the first judgment result not meeting a first preset requirement.

In some embodiments of the present application, step 106 includes:

Step 1061, subtracting the wind turbine blade opening real-time feedback value from the wind turbine blade opening prediction value to obtain a first difference.

Step 1062, obtaining a preset first threshold.

It can be understood that the above-mentioned first threshold is a threshold preset according to actual conditions, and the first threshold can be a specific value or a range of values.

Step 1063: compare the first difference with the first threshold to obtain a first comparison result.

Step 1064, in response to the first comparison result being that the first difference is greater than or less than the first threshold value, and the wind turbine stall alarm is not triggered, determining that the state of the wind turbine rotor blades is a fault state, and outputting a warning signal.

As an example of possible implementation, the first difference is compared with the first threshold value. If the first difference is greater than or less than the first threshold value, that is, the first difference does not fall within the range of the first threshold value, and the fan stall alarm is not triggered, it means that the deviation between the real-time feedback value of the fan blade opening and the predicted value of the fan blade opening exceeds the preset acceptable deviation range, and the above situation is not caused by the fan stall. Therefore, it can be determined that the adjustment mechanism of the fan blade is faulty, and an early warning signal is output to warn of the axial flow fan blade adjustment failure of the thermal power plant. If the first difference is equal to the first threshold value, that is, the first difference falls within the range of the first threshold value, it means that the deviation between the real-time feedback value of the fan blade opening and the predicted value of the fan blade opening does not exceed the preset acceptable deviation range, so it can be determined that the adjustment mechanism of the fan blade is not faulty.

According to the early warning method for the adjustment failure of the axial flow fan in a thermal power plant in the embodiment of the present application, the real-time operation data of the fan is obtained; based on the real-time operation data of the fan, the fan volume flow rate and the fan specific pressure energy are determined; the fan volume flow rate and the fan specific pressure energy are input into a pre-trained blade opening prediction model; the fan blade opening prediction value output by the blade opening prediction model is obtained; based on the real-time operation data of the fan, the fan blade opening real-time feedback value is obtained; based on the real-time operation data of the fan, the fan blade opening real-time feedback value is obtained; based on the fan blade opening real-time feedback value and the fan blade opening prediction value, the state of the fan blade is judged to obtain a first judgment result, and in response to the first judgment result not meeting the first preset requirement, a warning signal is output, thereby using the blade opening prediction model to predict the fan blade opening, comparing the fan blade opening prediction value with the fan blade opening real-time feedback value, timely discovering the adjustment failure of the fan blade and issuing a warning, and effectively improving the early warning efficiency and the accuracy of the early warning.

Another early warning method for adjusting the rotor blades of an axial flow fan in a thermal power plant proposed in the embodiment of the present application includes:

Step 201, obtaining real-time operation data of the wind turbine.

In the embodiments of the present application, step 201 can be implemented in any of the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

Step 202, determining the fan volume flow rate and the fan specific pressure energy based on the real-time operation data of the fan.

In the embodiments of the present application, step 202 may be implemented in any of the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

Step 203, inputting the fan volume flow rate and the fan specific pressure energy into a pre-trained blade opening prediction model.

In the embodiments of the present application, step 203 may be implemented in any of the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

Step 204: Obtain the predicted value of the wind turbine blade opening output by the blade opening prediction model.

In the embodiments of the present application, step 204 may be implemented in any of the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

Step 205, based on the real-time operation data of the wind turbine, obtain the real-time feedback value of the fan blade opening.

In the embodiments of the present application, step 205 can be implemented in any of the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

Step 206, based on the real-time feedback value of the wind turbine blade opening and the predicted value of the wind turbine blade opening, the state of the wind turbine blade is judged to obtain a first judgment result, and in response to the first judgment result not meeting the first preset requirement, an early warning signal is output.

In the embodiments of the present application, step 206 can be implemented in any of the embodiments of the present application, and the embodiments of the present application do not limit this and will not be described in detail.

Step 207, based on the real-time operation data of the wind turbine, obtain the real-time instruction value of the fan blade opening.

As an example of a possible implementation, a real-time instruction value of a fan blade opening is obtained from the real-time operation data of the fan.

Step 208, based on the real-time feedback value of the fan blade opening and the real-time command value of the fan blade opening, the state of the fan blade is judged to obtain a second judgment result, and in response to the second judgment result not meeting the second preset requirement, an early warning signal is output.

In some embodiments of the present application, step 208 includes:

Step 2081, subtract the wind turbine blade opening real-time feedback value from the wind turbine blade opening real-time instruction value to obtain a second difference.

Step 2082, obtaining a preset second threshold.

It can be understood that the second threshold is a threshold preset according to actual conditions, and the second threshold can be a specific value or a range of values.

Step 2083: compare the second difference with the second threshold to obtain a second comparison result.

Step 2084, in response to the second comparison result being that the second difference is greater than or less than the second threshold, determining that the state of the wind turbine rotor blade is a fault state, and outputting a warning signal.

As an example of possible implementation, the second difference is compared with the second threshold value. If the second difference is greater than or less than the second threshold value, that is, the second difference does not fall within the range of the second threshold value, it means that the deviation between the real-time feedback value of the fan blade opening and the real-time command value of the fan blade opening exceeds the preset acceptable deviation range. Therefore, it can be determined that the adjustment mechanism of the fan blade has a fault, and an early warning signal is output to warn of the axial flow fan blade adjustment fault in the thermal power plant. If the second difference is equal to the second threshold value, that is, the second difference falls within the range of the second threshold value, it means that the deviation between the real-time feedback value of the fan blade opening and the real-time command value of the fan blade opening does not exceed the preset acceptable deviation range. Therefore, it can be determined that the adjustment mechanism of the fan blade has not suffered the above-mentioned fault.

In some embodiments, the method for constructing the blade opening prediction model includes:

Step a, obtaining historical operation data of the fan.

Step b: preprocessing the historical operation data of the fan.

As an example of a possible implementation method, whether the fan is running is judged by the fan current value, and the historical data of the fan not running is eliminated; by consulting the operation log and operation experience, the abnormal operating condition data such as blade adjustment failure and fan stall are eliminated in the historical data, so as to retain normal data and form a fan operation history database.

For example, the data of the fan current less than 10A, that is, the data of the fan not running, is eliminated from the historical operation data of the fan; by consulting the historical operation logs and operation experience of the power plant, the abnormal operating condition data such as moving blade adjustment failure and fan stall are eliminated from the historical data, and the normal data is retained.

Step c, using the preprocessed historical operation data of the fan, calculate the fan volume flow rate and fan specific pressure energy under different working conditions.

In the embodiments of the present application, step c can be implemented in any of the ways in the embodiments of the present application, and the embodiments of the present application do not limit this and will not be elaborated on.

Step d: constructing a blade opening prediction model according to the corresponding relationship between the fan volume flow, the fan specific pressure energy and the fan blade opening reflected in the fan performance characteristic diagram.

It should be noted that, as shown in FIG2 , it can be seen from the fan performance characteristic diagram that the horizontal axis of the fan performance characteristic diagram is the fan volume flow rate, the vertical axis is the fan specific pressure energy, and the fan performance characteristic diagram also includes a fan rotor blade opening equivalent curve. Under a certain fan rotor blade opening, the fan volume flow rate and specific pressure energy are in a one-to-one correspondence, that is, when the fan specific pressure energy and volume flow rate are determined, the actual fan rotor blade opening can be inferred. Therefore, the blade opening prediction model can predict the fan rotor blade opening based on the fan volume flow rate and fan specific pressure energy.

Optionally, a mathematical model for predicting the opening of moving blades using the fan volume flow and specific pressure can be constructed based on a mixture of any one or more algorithms such as convolutional neural networks, deep neural networks and genetic algorithms.

FIG3 is a structural block diagram of an early warning device for axial flow fan blade adjustment failure in a thermal power plant proposed in an embodiment of the present application.

As shown in FIG3 , the early warning device for the axial flow fan blade adjustment failure of the thermal power plant includes:

The first acquisition module 301 is used to acquire real-time operation data of the wind turbine;

A determination module 302 is used to determine the fan volume flow rate and the fan specific pressure energy based on the real-time operation data of the fan;

An input module 303, for inputting the fan volume flow rate and the fan specific pressure energy into a pre-trained blade opening prediction model;

The second acquisition module 304 is used to acquire the predicted value of the wind turbine blade opening output by the blade opening prediction model;

The third acquisition module 305 is used to obtain the real-time feedback value of the fan blade opening based on the real-time operation data of the fan;

The first warning module 306 is used to judge the state of the wind turbine blades based on the real-time feedback value of the wind turbine blade opening and the predicted value of the wind turbine blade opening, obtain a first judgment result, and output a warning signal in response to the first judgment result not meeting the first preset requirement.

In some embodiments of the present application, the first warning module 301 includes:

A subtraction submodule, used for subtracting the wind turbine blade opening real-time feedback value from the wind turbine blade opening prediction value to obtain a first difference;

A first acquisition submodule, used to acquire a preset first threshold;

A first comparison submodule, used for comparing the first difference with a first threshold value to obtain a first comparison result;

The first warning submodule is used to determine that the state of the wind turbine rotor blade is a fault state in response to the first comparison result being that the first difference is greater than or less than the first threshold, and output a warning signal.

In some embodiments of the present application, the device further includes:

A fourth acquisition module is used to acquire a real-time instruction value of the fan blade opening based on the real-time operation data of the fan;

The second warning module is used to judge the state of the wind turbine blades based on the real-time feedback value of the wind turbine blade opening and the real-time instruction value of the wind turbine blade opening, obtain a second judgment result, and output a warning signal in response to the second judgment result not meeting the second preset requirement.

Among them, in some embodiments of the present application, the second warning module includes:

A second subtraction submodule is used to subtract the wind turbine blade opening real-time feedback value from the wind turbine blade opening real-time instruction value to obtain a second difference;

A second acquisition submodule, used to acquire a preset second threshold;

A second comparison submodule, used for comparing the second difference with a second threshold value to obtain a second comparison result;

The second warning submodule is used to determine that the state of the wind turbine rotor blade is a fault state in response to the second comparison result being that the second difference is greater than or less than the second threshold, and output a warning signal.

According to an embodiment of the present application, the present application also provides an electronic device and a readable storage medium.

As shown in Figure 4, it is a block diagram of an electronic device according to an early warning method for adjusting the rotor blades of an axial flow fan in a thermal power plant according to an embodiment of the present application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and/or required herein.

As shown in Figure 4, the electronic device includes: one or more processors 401, memory 402, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the electronic device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input/output device (such as a display device coupled to the interface). In other embodiments, if necessary, multiple processors and/or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). In Figure 4, a processor 401 is taken as an example.

The memory 402 is a non-transitory computer-readable storage medium provided in the present application. The memory stores instructions executable by at least one processor, so that at least one processor executes the early warning method for adjusting the rotor blades of an axial flow fan in a thermal power plant provided in the present application. The non-transitory computer-readable storage medium of the present application stores computer instructions, which are used to cause a computer to execute the early warning method for adjusting the rotor blades of an axial flow fan in a thermal power plant provided in the present application.

The memory 402, as a non-transient computer-readable storage medium, can be used to store non-transient software programs, non-transient computer executable programs and modules, such as the program instructions/modules corresponding to the early warning method for adjusting the rotor blades of the axial flow fan in the thermal power plant in the embodiment of the present application (for example, the first acquisition module 301, the determination module 302, the input module 303 and the second acquisition module 304 shown in FIG. 3). The processor 401 executes various functional applications and data processing of the server by running the non-transient software programs, instructions and modules stored in the memory 402, that is, realizes the early warning method for adjusting the rotor blades of the axial flow fan in the thermal power plant in the above method embodiment.

The memory 402 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function; the data storage area may store data created according to the use of the electronic device trained by the pre-trained model for the reading task, etc. In addition, the memory 402 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 402 may optionally include a memory remotely arranged relative to the processor 401, and these remote memories may be connected to the electronic device trained by the pre-trained model for the reading task via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

The electronic device of the early warning method for axial flow fan blade adjustment failure in thermal power plant may also include: input device 403 and output device 404. Processor 401, memory 402, input device 403 and output device 404 may be connected via a bus or other means, and FIG4 takes the bus connection as an example.

The input device 403 can receive input digital or character information, and generate key signal input related to user settings and function control of the electronic device trained by the pre-trained model for the reading task, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator rod, one or more mouse buttons, a trackball, a joystick and other input devices. The output device 404 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The display device may include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display and a plasma display. In some embodiments, the display device may be a touch screen.

Various implementations of the systems and techniques described herein can be realized in digital electronic circuit systems, integrated circuit systems, dedicated ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and/or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

These computer programs (also referred to as programs, software, software applications, or code) include machine instructions for programmable processors and can be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and/or means (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and/or data to a programmable processor, including: a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and/or data to a programmable processor.

To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communications network). Examples of communications networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short).

It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution disclosed in this application can be achieved, and this document is not limited here.

The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

In the present invention, the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" etc. mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims (7)

1. The early warning method for the adjustment faults of the movable blades of the axial flow fan of the thermal power plant is characterized by comprising the following steps:

acquiring real-time operation data of a fan;

based on the real-time operation data of the fan, determining the volume flow of the fan and the specific pressure energy of the fan, wherein the determination formula of the volume flow of the fan is as follows:

wherein V _i is the inlet/outlet air volume flow of the fan, and the unit is m ³/s; q is the mass flow of the medium of the fan, and the unit is kg/s; ρ _i is the fan inlet/outlet medium density in kg/m ³;

Inputting the fan volume flow and the fan specific pressure energy into a pre-trained blade opening prediction model;

obtaining a fan movable blade opening prediction value output by a blade opening prediction model;

acquiring a real-time feedback value of the opening degree of a movable blade of the fan based on the real-time operation data of the fan;

Based on the real-time feedback value of the opening degree of the fan movable blade and the predicted value of the opening degree of the fan movable blade, judging the state of the fan movable blade to obtain a first judging result, and outputting an early warning signal in response to the first judging result not meeting a first preset requirement;

After a real-time feedback value of the opening degree of the movable blade of the fan is obtained, a real-time instruction value of the opening degree of the movable blade of the fan is obtained based on the real-time operation data of the fan;

And judging the state of the fan movable blade based on the fan movable blade opening real-time feedback value and the fan movable blade opening real-time instruction value to obtain a second judging result, and outputting an early warning signal in response to the second judging result not meeting a second preset requirement, wherein the fan movable blade opening real-time feedback value and the fan movable blade opening real-time instruction value are subtracted to obtain a second difference value, the second difference value is compared with a second threshold value, and when the second difference value is larger than or smaller than the second threshold value, the state of the fan movable blade is determined to be a fault state.

2. The method of claim 1, wherein the determining the state of the fan blade based on the fan blade opening real-time feedback value and the fan blade opening predicted value to obtain a first determination result, and outputting an early warning signal in response to the first determination result not meeting a first preset requirement, includes:

subtracting the real-time feedback value of the opening degree of the fan movable blade from the predicted value of the opening degree of the fan movable blade to obtain a first difference value;

acquiring a preset first threshold value;

Comparing the first difference value with the first threshold value to obtain a first comparison result;

and responding to the first comparison result that the first difference value is larger than or smaller than the first threshold value, determining that the state of the fan movable blade is a fault state, and outputting an early warning signal.

3. An early warning device of axial fan movable vane adjustment trouble of thermal power plant, its characterized in that, the device includes:

The first acquisition module is used for acquiring real-time operation data of the fan;

The determining module is used for determining the volume flow of the fan and the specific pressure energy of the fan based on the real-time operation data of the fan, wherein the determining formula of the volume flow of the fan is as follows:

wherein V _i is the inlet/outlet air volume flow of the fan, and the unit is m ³/s; q is the mass flow of the medium of the fan, and the unit is kg/s; ρ _i is the fan inlet/outlet medium density in kg/m ³;

The input module is used for inputting the volume flow and the specific pressure energy of the fan into a pre-trained blade opening prediction model;

The second acquisition module is used for acquiring a fan movable blade opening predicted value output by the blade opening prediction model;

the third acquisition module is used for acquiring a real-time feedback value of the opening degree of the movable blade of the fan based on the real-time operation data of the fan;

The first early warning module is used for judging the state of the fan movable blade based on the fan movable blade opening real-time feedback value and the fan movable blade opening predicted value to obtain a first judgment result, and outputting an early warning signal in response to the first judgment result not meeting a first preset requirement;

the fourth acquisition module is used for acquiring a real-time instruction value of the opening degree of the movable blade of the fan based on the real-time operation data of the fan after acquiring the real-time feedback value of the opening degree of the movable blade of the fan;

And the second early warning module is used for judging the state of the fan movable blade based on the fan movable blade opening real-time feedback value and the fan movable blade opening real-time instruction value to obtain a second judging result, and outputting an early warning signal in response to the second judging result not meeting a second preset requirement, wherein the fan movable blade opening real-time feedback value and the fan movable blade opening real-time instruction value are subtracted to obtain a second difference value, the second difference value is compared with a second threshold value, and when the second difference value is larger than or smaller than the second threshold value, the state of the fan movable blade is determined to be a fault state.

4. The apparatus of claim 3, wherein the first pre-warning module comprises:

the first subtraction submodule is used for subtracting the real-time feedback value of the opening degree of the fan movable blade from the predicted value of the opening degree of the fan movable blade to obtain a first difference value;

The first acquisition submodule is used for acquiring a preset first threshold value;

The first comparison submodule is used for comparing the first difference value with the first threshold value to obtain a first comparison result;

and the first early warning sub-module is used for responding to the first comparison result that the first difference value is larger than or smaller than the first threshold value, determining the state of the fan movable blade to be a fault state and outputting an early warning signal.

5. An electronic device, comprising:

At least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of claim 1 or 2.

6. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of claim 1 or 2.

7. A computer program product comprising a computer program which, when executed by a processor, implements the method of claim 1 or 2.