CN116562195A - Transformer early warning method and system adopting big data model analysis - Google Patents
Transformer early warning method and system adopting big data model analysis Download PDFInfo
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- G—PHYSICS
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- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
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- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
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Abstract
The invention relates to the technical field of transformer early warning, in particular to a transformer early warning method and system adopting big data model analysis. The method comprises the following steps: determining a plurality of first positions at which the temperature change rate exceeds a first preset temperature change rate threshold value and a plurality of second positions at which the magnetic field strength change rate exceeds the first preset magnetic field strength change rate threshold value by applying different initial conditions to a simulation model of the transformer; acquiring an actual temperature change rate of each first position and an actual magnetic field strength change rate of each second position; when the actual temperature change rate of any first position exceeds a second preset temperature change rate threshold, first early warning information is sent out, and when the actual temperature change rate of any second position exceeds a second preset magnetic field intensity change rate threshold, second early warning information is sent out. Through simulation calculation, the first position sensitive to temperature and the second position sensitive to magnetic field strength can be accurately determined, data can be acquired more pertinently, and the data quantity and cost are reduced.
Description
Technical Field
The invention relates to the technical field of transformer early warning, in particular to a transformer early warning method and system adopting big data model analysis.
Background
The transformer is an important device in the network, the safety of the running state of the transformer directly relates to the safety and stability of the whole power grid, at present, the running state of the power grid is usually determined by carrying out omnibearing data detection on a voltage transformer, but due to the huge quantity of extracted data, a polymorphic server and a storage space are required to carry out data processing when necessary, and the cost is high.
In the prior art, the problem that the transformer is difficult to lock a fault position, long in maintenance time and low in efficiency exists in alarming of the transformer.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a transformer early warning method and a system adopting big data model analysis.
The technical scheme of the transformer early warning method adopting big data model analysis is as follows:
constructing a simulation model of the transformer;
determining a plurality of first positions at which the temperature change rate exceeds a first preset temperature change rate threshold value and a plurality of second positions at which the magnetic field strength change rate exceeds the first preset magnetic field strength change rate threshold value by applying different initial conditions to a simulation model of the transformer;
acquiring an actual temperature change rate of each first position and an actual magnetic field strength change rate of each second position;
when the actual temperature change rate of any first position exceeds a second preset temperature change rate threshold value, first early warning information is sent out, and when the actual temperature change rate of any second position exceeds a second preset magnetic field intensity change rate threshold value, second early warning information is sent out;
checking humidity jump state time in the associated equipment and state jump of a circuit controller in the associated equipment at the same time when the first early warning information and/or the second early warning information appear and the cooling equipment working condition is normal but the cooling equipment stops working;
and outputting the state-hopped circuit controller to a user.
The transformer early warning method adopting the big data model analysis has the following beneficial effects:
the temperature change and the magnetic field intensity change are main factors influencing the running state of the transformer, and the first position sensitive to the temperature and the second position sensitive to the magnetic field intensity can be accurately determined through simulation calculation, so that data can be acquired more pertinently, the data volume is effectively reduced, and the cost is reduced. The condition that part of the circuit is out of order due to over-humidity can be eliminated according to the state of the fan and the humidity. The fan is stopped due to a short circuit caused by humidity of the individual control circuits, resulting in an increase in the temperature of the transformer.
Based on the scheme, the transformer early warning method adopting the big data model analysis can be improved as follows.
Further, the method further comprises the following steps:
determining the time period of the maximum temperature change rate of each first position and the time period of the maximum magnetic field intensity change rate of each second position according to the historical data of a plurality of continuous days;
increasing the acquisition frequency of the temperature within the period of time to which the maximum temperature change rate of each first location belongs, and decreasing the acquisition frequency of the temperature outside the period of time to which the maximum temperature change rate of each first location belongs;
increasing the acquisition frequency of the magnetic field strength within the time period of the maximum magnetic field strength change rate for each second location and decreasing the acquisition frequency of the magnetic field strength outside the time period of the maximum magnetic field strength change rate for each second location.
The beneficial effects of adopting the further scheme are as follows: the data volume of gathering can be further reduced, storage space is further reduced, reduce cost.
Further, the method further comprises the following steps:
arranging a plurality of display windows on a display interface;
and updating the electric quantity parameters of the transformers corresponding to each display window in real time according to the time period of the maximum temperature change rate of each first position of each transformer and the time period of the maximum magnetic field intensity change rate of each second position of each transformer.
The beneficial effects of adopting the further scheme are as follows: at different moments, the display is more targeted so as to process the abnormal situation.
Further, the method further comprises the following steps:
expanding a blank area of the intercepted screenshot of the user on the display interface, identifying the number of each transformer in the screenshot, calling the product information and the installation position of each transformer according to the number of each transformer, and adding the product information and the installation position to the blank area to obtain the processed screenshot.
The beneficial effects of adopting the further scheme are as follows: in the prior art, when a user propagates a screenshot, the user also needs to edit a text to explain product information and installation positions, so that the screenshot and the text are separated, the screenshot is complex and can be easily missed by others.
Further, the method further comprises the following steps:
and collecting vibration signals of the transformer in any time period, continuously collecting vibration signals in a plurality of time periods when the amplitude larger than the preset amplitude exists in the sound signals in the time period, and sending out abnormal reminding when the amplitude larger than the preset amplitude exists in all the time periods.
The beneficial effects of adopting the further scheme are as follows: in the prior art, when one or two amplitudes larger than a preset amplitude appear in one time period, an alarm is sent out, but due to the fact that the number of accidental factors is large, the alarm is too sensitive, the alarm accuracy is reduced.
The technical scheme of the transformer early warning system adopting big data model analysis is as follows:
the system comprises a construction module, a position determination module, an acquisition module and an early warning module;
the construction module is used for: constructing a simulation model of the transformer;
the position determining module is used for: determining a plurality of first positions at which the temperature change rate exceeds a first preset temperature change rate threshold value and a plurality of second positions at which the magnetic field strength change rate exceeds the first preset magnetic field strength change rate threshold value by applying different initial conditions to a simulation model of the transformer;
the acquisition module is used for: acquiring an actual temperature change rate of each first position and an actual magnetic field strength change rate of each second position;
the early warning module is used for: when the actual temperature change rate of any first position exceeds a second preset temperature change rate threshold value, first early warning information is sent out, and when the actual temperature change rate of any second position exceeds a second preset magnetic field intensity change rate threshold value, second early warning information is sent out;
checking humidity jump state time in the associated equipment and state jump of a circuit controller in the associated equipment at the same time when the first early warning information and/or the second early warning information appear and the cooling equipment working condition is normal but the cooling equipment stops working;
and outputting the state-hopped circuit controller to a user.
The transformer early warning system adopting the big data model analysis has the following beneficial effects:
the temperature change and the magnetic field intensity change are main factors influencing the running state of the transformer, and the first position sensitive to the temperature and the second position sensitive to the magnetic field intensity can be accurately determined through simulation calculation, so that data can be acquired more pertinently, the data volume is effectively reduced, and the cost is reduced.
Based on the scheme, the transformer early warning system adopting the big data model analysis can be improved as follows.
Further, the device comprises a time period determining module and a frequency correcting module;
the time period determining module is used for:
determining the time period of the maximum temperature change rate of each first position and the time period of the maximum magnetic field intensity change rate of each second position according to the historical data of a plurality of continuous days;
the frequency correction module is used for:
increasing the acquisition frequency of the temperature within the period of time to which the maximum temperature change rate of each first location belongs, and decreasing the acquisition frequency of the temperature outside the period of time to which the maximum temperature change rate of each first location belongs;
increasing the acquisition frequency of the magnetic field strength within the time period of the maximum magnetic field strength change rate for each second location and decreasing the acquisition frequency of the magnetic field strength outside the time period of the maximum magnetic field strength change rate for each second location.
The beneficial effects of adopting the further scheme are as follows: the data volume of gathering can be further reduced, storage space is further reduced, reduce cost.
Further, the display module is also included;
the display module is used for:
arranging a plurality of display windows on a display interface;
and updating the electric quantity parameters of the transformers corresponding to each display window in real time according to the time period of the maximum temperature change rate of each first position of each transformer and the time period of the maximum magnetic field intensity change rate of each second position of each transformer.
The beneficial effects of adopting the further scheme are as follows: at different moments, the display is more targeted so as to process the abnormal situation.
Further, the system further comprises an expansion adding module, wherein the expansion adding module is used for:
expanding a blank area of the intercepted screenshot of the user on the display interface, identifying the number of each transformer in the screenshot, calling the product information and the installation position of each transformer according to the number of each transformer, and adding the product information and the installation position to the blank area to obtain the processed screenshot.
The beneficial effects of adopting the further scheme are as follows: in the prior art, when a user propagates a screenshot, the user also needs to edit a text to explain product information and installation positions, so that the screenshot and the text are separated, the screenshot is complex and can be easily missed by others.
Further, the early warning module is further used for:
and collecting vibration signals of the transformer in any time period, continuously collecting vibration signals in a plurality of time periods when the amplitude larger than the preset amplitude exists in the sound signals in the time period, and sending out abnormal reminding when the amplitude larger than the preset amplitude exists in all the time periods.
The beneficial effects of adopting the further scheme are as follows: in the prior art, when one or two amplitudes larger than a preset amplitude appear in one time period, an alarm is sent out, but due to the fact that the number of accidental factors is large, the alarm is too sensitive, the alarm accuracy is reduced.
Drawings
FIG. 1 is a schematic flow chart of a transformer early warning method adopting big data model analysis according to an embodiment of the invention;
fig. 2 is a schematic structural diagram of a transformer pre-alarm method using big data model analysis according to an embodiment of the present invention.
Detailed Description
In order to clearly illustrate the aspects of the present invention, preferred embodiments are described below in detail with reference to the accompanying drawings.
As shown in fig. 1, a transformer early warning method adopting big data model analysis in the embodiment of the invention includes the following steps:
s1, constructing a simulation model of a transformer, and specifically:
a simulation model of the transformer is built by means of simulation software such as ANSYS and the like, specifically, a three-dimensional model of the transformer is built, and material parameters and the like are given to different parts in the three-dimensional model.
S2, determining a plurality of first positions of which the temperature change rate exceeds a first preset temperature change rate threshold value and a plurality of second positions of which the magnetic field strength change rate exceeds the first preset magnetic field strength change rate threshold value by applying different initial conditions on a simulation model of the transformer;
in the implementation process, operation monitoring data of transformers in each city can be added to apply various monitoring data to the transformer simulation model. And matching and machine learning are carried out with the recorded operation data, so that the model is closer to a real scene.
Wherein, the boundary condition of the transformer is set according to the actual situation, and the boundary condition can be considered as unchanged.
The initial conditions include input voltage or electric current values which change within a certain range, initial temperature values of various parts of the transformer and the like, dividing cells and solving the cells to obtain the temperature and the magnetic field intensity of each preset position under different voltage values, wherein the preset positions can be arranged on the parts of the transformer and can also be arranged in the internal space or the external space of the transformer, so that the temperature change rate and the magnetic field intensity change rate of each preset position can be calculated, the preset position with the temperature change rate exceeding a first preset temperature change rate threshold value is determined as a first position, and the preset position with the magnetic field intensity change rate exceeding the first preset magnetic field intensity change rate threshold value is determined as a second position.
It should be noted that:
1) Multiple preset positions can be determined according to actual experience, and the method is more targeted.
2) The initial conditions include the input voltage or current values that vary over a range for the following reasons: although the input voltage is indicated as 30KV, the input voltage actually fluctuates up and down at 30KV, and thus the input voltage value can be changed in a certain step within the fluctuation range of 30 KV.
The first preset temperature change rate threshold, the first preset magnetic field intensity change rate threshold, the second preset temperature change rate threshold and the second preset magnetic field intensity change rate threshold can be set according to actual conditions.
S3, acquiring the actual temperature change rate of each first position, and acquiring the actual magnetic field intensity change rate of each second position.
S4, when the actual temperature change rate of any first position exceeds a second preset temperature change rate threshold, first early warning information is sent out, and when the actual temperature change rate of any second position exceeds a second preset magnetic field intensity change rate threshold, second early warning information is sent out.
The audible and visual alarm can send out first early warning information and second early warning information;
checking humidity jump state time in the associated equipment and state jump of a circuit controller in the associated equipment at the same time when the first early warning information and/or the second early warning information appear and the cooling equipment working condition is normal but the cooling equipment stops working;
and outputting the state-hopped circuit controller to a user.
The temperature change and the magnetic field intensity change are main factors influencing the running state of the transformer, and the first position sensitive to the temperature and the second position sensitive to the magnetic field intensity can be accurately determined through simulation calculation, so that data can be acquired more pertinently, the data volume is effectively reduced, and the cost is reduced.
Wherein, there may be a situation that the first position and the second position overlap, and when the actual temperature change rate of the same position exceeds the second preset temperature change rate threshold value and the actual temperature change rate exceeds the second preset magnetic field strength change rate threshold value, the first early warning information and the second early warning information are sent at the same time. The detection of the magnetic field can be achieved by CT energy capturing devices placed at different positions.
In addition, the temperature rise may also be due to the cooling device stopping. No abnormality occurred before the stop. Usually, the stopping of the operation is mainly caused by voltage fluctuation, and also is caused by abnormality of part of control circuits or controllers; for example, in the case of a high humidity, this causes a short circuit caused by the humidity in the individual control circuits, which causes voltage fluctuations, thus causing the fan to stop and the temperature of the transformer to rise.
In the embodiment, the 330kV transformer substation No. 2 main transformer reports the complete stop of air cooling. After the temperature of the transformer rises and the remote inquiry is carried out, the abnormal state of the temperature and humidity controller in the control box at the same time is found, after the temperature and humidity controller is checked, the failure of the alternating current contactor KMS1 is found due to over-humidity, the corresponding control power supply also fails, the air cooling is stopped, and the temperature rise of the transformer is further caused.
After the remote output is given to a user and the humidity jump and the state of the temperature and humidity controller are abnormal, an maintainer rapidly locks the fault position, replaces a new temperature and humidity controller and returns the cooling equipment fan to be normal.
Optionally, in the above technical solution, the method further includes:
s5, determining a time period of the maximum temperature change rate of each first position and a time period of the maximum magnetic field intensity change rate of each second position according to the historical data of a plurality of continuous days;
s6, increasing the temperature acquisition frequency in the time period of the maximum temperature change rate of each first position, and reducing the temperature acquisition frequency outside the time period of the maximum temperature change rate of each first position;
s7, increasing the acquisition frequency of the magnetic field intensity in the time period of the maximum magnetic field intensity change rate of each second position, and decreasing the acquisition frequency of the magnetic field intensity outside the time period of the maximum magnetic field intensity change rate of each second position. The data volume of gathering can be further reduced, storage space is further reduced, reduce cost.
Optionally, in the above technical solution, the method further includes:
s8, arranging a plurality of display windows on a display interface;
and S9, updating the electric quantity parameters of the transformers corresponding to each display window in real time according to the time period of the maximum temperature change rate of each first position of each transformer and the time period of the maximum magnetic field intensity change rate of each second position. At different moments, the display is more targeted so as to process the abnormal situation.
For example, the first voltage device has two first positions and two second positions, the time period of the maximum temperature change rate of the first position of the first voltage device is 10:00-10:05, the time period of the maximum temperature change rate of the first position of the first voltage device is 11:00-11:05, the time period of the maximum magnetic field strength change rate of the first second position of the first voltage device is 12:00-12:05, the time period of the maximum magnetic field strength change rate of the second position of the first voltage device is 13:00-13:05, and the time period corresponding to the first voltage device comprises: 10:00-10:05, 12:00-12:05 and 13:00-13:05. Similarly, the obtaining the time period corresponding to the second voltage device includes: 10:10-10:15, 11:10-11:15, 12:10-12:15 and 13:10-13:15, the time period corresponding to the third voltage device comprises: 10:20-10:25, 11:20-11:25, 12:20-12:25 and 13:05-13:15, the time period corresponding to the fourth voltage device comprises: 10:02-10:03, 11:02-11:03, 12:02-12:03 and 13:05-13:03, the time period corresponding to the fifth voltage transformer comprises: 10:10 to 10:15, 11:10 to 11:15, 12:10 to 12:15 and 13:05 to 13:15, then:
if the current time is 10:02, the electric quantity parameters of the first voltage device and the fourth voltage device need to be displayed, namely, the first display window displays the electric quantity parameters of the first voltage device, and the second display window displays the electric quantity parameters of the fifth voltage device; if the current time is 10:11, the electric quantity parameters of the second voltage device and the fifth voltage device need to be displayed, namely, the first display window displays the electric quantity parameters of the second voltage device, and the second display window displays the electric quantity parameters of the fifth voltage device, so that updating is realized.
The number of display windows may be set according to the actual situation,
optionally, in the above technical solution, the method further includes:
s10, expanding a blank area of the intercepted screenshot of the user on the display interface, identifying the number of each transformer in the screenshot, calling the product information and the installation position of each transformer according to the number of each transformer, and adding the product information and the installation position to the blank area to obtain the processed screenshot.
The process of expanding the blank area for the intercepted screenshot of the user on the display interface comprises the following steps: and adding pixel points at the edge of the screenshot to expand a blank area.
The code of each transformer can be set in advance, the code is printed on the label, and the label is attached to the transformer, so that the number of each transformer in the screenshot can be obtained in an image recognition mode.
The codes, the product information and the installation position of each transformer are stored in a database in advance, so that the product information and the installation position of the transformer can be called through the codes and added into a blank area to obtain a screenshot after processing.
In the prior art, when a user propagates a screenshot, the user also needs to edit a text to explain product information and installation positions, so that the screenshot and the text are separated, the screenshot is complex and can be easily missed by others.
Optionally, in the above technical solution, the method further includes:
s11, collecting vibration signals of the transformer in any time period, continuously collecting vibration signals in a plurality of time periods when the amplitude larger than the preset amplitude exists in the sound signals in the time period, and sending out abnormal reminding when the amplitude larger than the preset amplitude exists in all the time periods.
In the prior art, when one or two amplitudes larger than a preset amplitude appear in one time period, an alarm is sent out, but due to the fact that the number of accidental factors is large, the alarm is too sensitive, the alarm accuracy is reduced.
In the above embodiments, although steps S1, S2, etc. are numbered, only specific embodiments are given herein, and those skilled in the art may adjust the execution sequence of S1, S2, etc. according to the actual situation, which is also within the scope of the present invention, and it is understood that some embodiments may include some or all of the above embodiments.
As shown in fig. 2, the transformer early warning system adopting big data model analysis in the embodiment of the invention comprises a construction module, a position determining module, an acquisition module and an early warning module;
the construction module is used for: constructing a simulation model of the transformer;
the position determining module is used for: determining a plurality of first positions at which the temperature change rate exceeds a first preset temperature change rate threshold value and a plurality of second positions at which the magnetic field strength change rate exceeds the first preset magnetic field strength change rate threshold value by applying different initial conditions to a simulation model of the transformer;
the acquisition module is used for: acquiring an actual temperature change rate of each first position and an actual magnetic field strength change rate of each second position;
the early warning module is used for: when the actual temperature change rate of any first position exceeds a second preset temperature change rate threshold, first early warning information is sent out, and when the actual temperature change rate of any second position exceeds a second preset magnetic field intensity change rate threshold, second early warning information is sent out.
The temperature change and the magnetic field intensity change are main factors influencing the running state of the transformer, and the first position sensitive to the temperature and the second position sensitive to the magnetic field intensity can be accurately determined through simulation calculation, so that data can be acquired more pertinently, the data volume is effectively reduced, and the cost is reduced.
Optionally, in the above technical solution, the method includes a time period determining module and a frequency correcting module;
the time period determining module is used for:
determining the time period of the maximum temperature change rate of each first position and the time period of the maximum magnetic field intensity change rate of each second position according to the historical data of a plurality of continuous days;
the frequency correction module is used for:
increasing the acquisition frequency of the temperature within the period of time to which the maximum temperature change rate of each first location belongs, and decreasing the acquisition frequency of the temperature outside the period of time to which the maximum temperature change rate of each first location belongs;
increasing the acquisition frequency of the magnetic field strength within the time period of the maximum magnetic field strength change rate for each second location and decreasing the acquisition frequency of the magnetic field strength outside the time period of the maximum magnetic field strength change rate for each second location.
The data volume of gathering can be further reduced, storage space is further reduced, reduce cost.
Optionally, in the above technical solution, the display device further includes a display module;
the display module is used for:
arranging a plurality of display windows on a display interface;
and updating the electric quantity parameters of the transformers corresponding to each display window in real time according to the time period of the maximum temperature change rate of each first position of each transformer and the time period of the maximum magnetic field intensity change rate of each second position of each transformer.
At different moments, the display is more targeted so as to process the abnormal situation.
Optionally, in the above technical solution, the system further includes an expansion adding module, where the expansion adding module is configured to:
expanding a blank area of the intercepted screenshot of the user on the display interface, identifying the number of each transformer in the screenshot, calling the product information and the installation position of each transformer according to the number of each transformer, and adding the product information and the installation position to the blank area to obtain the processed screenshot.
In the prior art, when a user propagates a screenshot, the user also needs to edit a text to explain product information and installation positions, so that the screenshot and the text are separated, the screenshot is complex and can be easily missed by others.
Optionally, in the above technical solution, the early warning module is further configured to:
and collecting vibration signals of the transformer in any time period, continuously collecting vibration signals in a plurality of time periods when the amplitude larger than the preset amplitude exists in the sound signals in the time period, and sending out abnormal reminding when the amplitude larger than the preset amplitude exists in all the time periods.
In the prior art, when one or two amplitudes larger than a preset amplitude appear in one time period, an alarm is sent out, but due to the fact that the number of accidental factors is large, the alarm is too sensitive, the alarm accuracy is reduced.
The steps for implementing the corresponding functions of each parameter and each unit module in the transformer early warning system adopting the big data model analysis according to the present invention can refer to each parameter and each step in the embodiment of the transformer early warning method adopting the big data model analysis according to the present invention, and will not be described herein.
Those skilled in the art will appreciate that the present invention may be implemented as a system, method, or computer program product.
Accordingly, the present disclosure may be embodied in the following forms, namely: either entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or entirely software, or a combination of hardware and software, referred to herein generally as a "circuit," module "or" system. Furthermore, in some embodiments, the invention may also be embodied in the form of a computer program product in one or more computer-readable media, which contain computer-readable program code.
Any combination of one or more computer readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the computer-readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (10)
1. The transformer early warning method adopting big data model analysis is characterized by comprising the following steps:
constructing a simulation model of the transformer;
determining a plurality of first positions at which the temperature change rate exceeds a first preset temperature change rate threshold value and a plurality of second positions at which the magnetic field strength change rate exceeds the first preset magnetic field strength change rate threshold value by applying different initial conditions to a simulation model of the transformer;
acquiring an actual temperature change rate of each first position and an actual magnetic field strength change rate of each second position;
when the actual temperature change rate of any first position exceeds a second preset temperature change rate threshold value, first early warning information is sent out, and when the actual temperature change rate of any second position exceeds a second preset magnetic field intensity change rate threshold value, second early warning information is sent out;
checking humidity jump state time in the associated equipment and state jump of a circuit controller in the associated equipment at the same time when the first early warning information and/or the second early warning information appear and the cooling equipment working condition is normal but the cooling equipment stops working;
and outputting the state-hopped circuit controller to a user.
2. The method for pre-warning a transformer using big data model analysis according to claim 1, further comprising:
determining the time period of the maximum temperature change rate of each first position and the time period of the maximum magnetic field intensity change rate of each second position according to the historical data of a plurality of continuous days;
increasing the acquisition frequency of the temperature within the period of time to which the maximum temperature change rate of each first location belongs, and decreasing the acquisition frequency of the temperature outside the period of time to which the maximum temperature change rate of each first location belongs;
increasing the acquisition frequency of the magnetic field strength within the time period of the maximum magnetic field strength change rate for each second location and decreasing the acquisition frequency of the magnetic field strength outside the time period of the maximum magnetic field strength change rate for each second location.
3. The method for pre-warning a transformer using big data model analysis according to claim 1, further comprising:
arranging a plurality of display windows on a display interface;
and updating the electric quantity parameters of the transformers corresponding to each display window in real time according to the time period of the maximum temperature change rate of each first position of each transformer and the time period of the maximum magnetic field intensity change rate of each second position of each transformer.
4. The method for pre-warning a transformer using big data model analysis according to claim 3, further comprising:
expanding a blank area of the intercepted screenshot of the user on the display interface, identifying the number of each transformer in the screenshot, calling the product information and the installation position of each transformer according to the number of each transformer, and adding the product information and the installation position to the blank area to obtain the processed screenshot.
5. The method for pre-warning a transformer using big data model analysis according to any one of claims 1 to 4, further comprising:
and collecting vibration signals of the transformer in any time period, continuously collecting vibration signals in a plurality of time periods when the amplitude larger than the preset amplitude exists in the sound signals in the time period, and sending out abnormal reminding when the amplitude larger than the preset amplitude exists in all the time periods.
6. The transformer early warning system adopting the big data model analysis is characterized by comprising a construction module, a position determination module, an acquisition module and an early warning module;
the construction module is used for: constructing a simulation model of the transformer;
the position determining module is used for: determining a plurality of first positions at which the temperature change rate exceeds a first preset temperature change rate threshold value and a plurality of second positions at which the magnetic field strength change rate exceeds the first preset magnetic field strength change rate threshold value by applying different initial conditions to a simulation model of the transformer;
the acquisition module is used for: acquiring an actual temperature change rate of each first position and an actual magnetic field strength change rate of each second position;
the early warning module is used for: when the actual temperature change rate of any first position exceeds a second preset temperature change rate threshold value, first early warning information is sent out, and when the actual temperature change rate of any second position exceeds a second preset magnetic field intensity change rate threshold value, second early warning information is sent out;
checking humidity jump state time in the associated equipment and state jump of a circuit controller in the associated equipment at the same time when the first early warning information and/or the second early warning information appear and the cooling equipment working condition is normal but the cooling equipment stops working;
and outputting the state-hopped circuit controller to a user.
7. The transformer early warning system adopting big data model analysis according to claim 6, comprising a time period determining module and a frequency correcting module;
the time period determining module is used for:
determining the time period of the maximum temperature change rate of each first position and the time period of the maximum magnetic field intensity change rate of each second position according to the historical data of a plurality of continuous days;
the frequency correction module is used for:
increasing the acquisition frequency of the temperature within the period of time to which the maximum temperature change rate of each first location belongs, and decreasing the acquisition frequency of the temperature outside the period of time to which the maximum temperature change rate of each first location belongs;
increasing the acquisition frequency of the magnetic field strength within the time period of the maximum magnetic field strength change rate for each second location and decreasing the acquisition frequency of the magnetic field strength outside the time period of the maximum magnetic field strength change rate for each second location.
8. The transformer early warning system adopting big data model analysis according to claim 6, further comprising a display module;
the display module is used for:
arranging a plurality of display windows on a display interface;
and updating the electric quantity parameters of the transformers corresponding to each display window in real time according to the time period of the maximum temperature change rate of each first position of each transformer and the time period of the maximum magnetic field intensity change rate of each second position of each transformer.
9. The transformer early warning system adopting big data model analysis according to claim 8, further comprising an expansion adding module, wherein the expansion adding module is configured to:
expanding a blank area of the intercepted screenshot of the user on the display interface, identifying the number of each transformer in the screenshot, calling the product information and the installation position of each transformer according to the number of each transformer, and adding the product information and the installation position to the blank area to obtain the processed screenshot.
10. A transformer early warning system according to any one of claims 6 to 9, characterised in that the early warning module is further adapted to:
and collecting vibration signals of the transformer in any time period, continuously collecting vibration signals in a plurality of time periods when the amplitude larger than the preset amplitude exists in the sound signals in the time period, and sending out abnormal reminding when the amplitude larger than the preset amplitude exists in all the time periods.
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