CN114971016A

CN114971016A - Intelligent monitoring method for transformer faults

Info

Publication number: CN114971016A
Application number: CN202210580598.0A
Authority: CN
Inventors: 洪桂权; 罗爱华; 王翠云; 王鑫; 黄嘉艺
Original assignee: Jiangxi Yawei Electric Co ltd
Current assignee: Jiangxi Yawei Electric Co ltd
Priority date: 2022-05-25
Filing date: 2022-05-25
Publication date: 2022-08-30

Abstract

The invention relates to the technical field of intelligent monitoring, and discloses an intelligent monitoring method for transformer faults; the method has the advantages that the error of a single monitoring result is eliminated by simultaneously calculating various data items in different aspects of the transformer during operation and evaluating the real-time operation state of the transformer, and meanwhile, the abnormal state in the evaluation result is audited, the accuracy and timeliness of monitoring the abnormal operation state can be improved by differentially auditing the abnormal operation states in different degrees, and the specific position of temperature abnormality is further analyzed and determined; the overall operation state of the transformer is evaluated from the aspects of abnormity and maintenance of the operation of the transformer, and the operation effect of the transformer is improved by adaptively adjusting the maintenance duration; the method and the device are used for solving the problems that the authenticity of different abnormal running states cannot be verified in the existing scheme, and the overall running state of the transformer is evaluated to adaptively shorten the overhaul time.

Description

Intelligent monitoring method for transformer faults

Technical Field

The invention relates to the technical field of intelligent monitoring, in particular to an intelligent monitoring method for transformer faults, electronic equipment and a computer readable storage medium.

Background

The state monitoring technology is generated along with the development of the modern maintenance technology of the transformer, the limitation of collecting transformer information in the past is broken through, at present, a power system can continuously record various related data influencing the service life of the transformer in real time by adopting the online monitoring of the transformer, the potential fault hazard can be found as early as possible by automatically processing the data, and the basic state maintenance of the transformer is realized.

When the existing transformer fault monitoring scheme is implemented, various data in different aspects cannot be combined to carry out overall evaluation on the real-time running state of the transformer, only single data matching and analysis can be carried out, and the authenticity of the monitored abnormal state cannot be checked and verified, so that the accuracy of monitoring and analysis is poor; meanwhile, the overall operation state of the transformer cannot be evaluated from different aspects, and the transformer can only be periodically overhauled, so that the overall monitoring effect of the transformer in different operation states is poor.

Disclosure of Invention

The invention provides an intelligent monitoring method for transformer faults, electronic equipment and a computer readable storage medium, and mainly aims to solve the technical problems that the authenticity of different abnormal running states cannot be verified, the whole running state of a transformer cannot be evaluated, and the overhaul time is shortened adaptively in the existing scheme.

In order to achieve the above object, the present invention provides an intelligent monitoring method for transformer faults, including:

monitoring and data acquisition are carried out on different areas of the transformer during operation, so as to obtain a monitoring data set; the monitoring dataset comprises temperature data, loudness data, and hydrogen data;

performing feature extraction and definition on each item of data in the monitoring data set to obtain a monitoring definition set containing temperature definition data, loudness definition data and hydrogen definition data;

acquiring the area position of the transformer and the corresponding area weight thereof, and simultaneously acquiring the corresponding operation evaluation value YP by combining the area weight of the transformer with the monitoring definition set;

if the minimum value YPmin of the operation evaluation range is less than or equal to the maximum value YPmax of the operation evaluation range, judging that the operation state of the corresponding transformer is slightly abnormal, generating a first operation abnormity signal, and setting the corresponding transformer as a first target transformer according to the first operation abnormity signal;

if the operation evaluation value YP is larger than the maximum operation evaluation range value YPmax, judging that the operation state of the corresponding transformer is moderately abnormal, generating a second operation abnormity signal, and setting the corresponding transformer as a second target transformer according to the second operation abnormity signal;

verifying the authenticity of the first target transformer and the second target transformer in abnormal states respectively, and giving an early warning prompt on the abnormal states passing the verification;

and integrating the abnormal data and the overhaul data which pass the verification in a preset evaluation period, and generating a life prediction prompt in a self-adaptive manner according to an integrated result.

Preferably, the extracting and defining the features of the items of data in the monitoring data set includes:

acquiring temperature data, loudness data and hydrogen data in the monitoring dataset;

respectively extracting values of real-time temperature, real-time loudness and real-time hydrogen concentration in the temperature data, the loudness data and the hydrogen data, and defining marks;

respectively numbering and marking the regions corresponding to the real-time temperature, the real-time loudness and the real-time hydrogen concentration;

and sequentially arranging and combining the real-time temperature, the real-time loudness, the real-time hydrogen concentration and the corresponding regions of the real-time temperature, the real-time loudness and the real-time hydrogen concentration of the definition marks according to a time sequence to obtain a monitoring definition set containing temperature definition data, loudness definition data and hydrogen definition data.

Preferably, the obtaining of the corresponding operation evaluation value YP by combining the area weight of the transformer with the monitoring definition set includes:

matching the obtained region position of the transformer with a pre-constructed region position table to obtain a corresponding region weight, and defining and marking the region weight as QQi;

combining the area weight of the definition mark with each data of the definition mark in the monitoring definition set, and calculating and acquiring an operation evaluation value YP corresponding to the transformer through a formula; the formula is:

YP＝QQi×[a1×(SWi-SWi0)+a2×(SXi-SXi0)+a3×(SQi-SQi0)]

in the formula, a1, a2 and a3 are different proportionality coefficients which are all larger than zero, SWi0 is a preset standard temperature, SXi0 is a preset standard loudness, and SQi0 is a preset standard hydrogen concentration.

Preferably, the verifying the authenticity of the first target transformer and the second target transformer in abnormal states respectively includes:

verifying the operation of the first target transformer and the second target transformer respectively according to the first running difference signal and the second running difference signal;

counting the occurrence time lengths of a first running exception signal and a second running exception signal in a preset first verification time period and a preset second verification time period respectively, and calibrating the total occurrence time lengths of the first running exception signal and the second running exception signal as a first running exception time length and a second running exception time length respectively;

extracting the values of the first running time length and the second running time length respectively and marking the values as YSk, wherein k is 1 and 2; obtaining the check value HY of the first running exception signal and the second running exception signal through the formula HY YSk/HSk; wherein HSk is the total duration corresponding to the first and second verification periods;

setting the check value corresponding to the first running exception signal and the check value corresponding to the second running exception signal as a first check value and a second check value respectively and evaluating;

and if the first verification value is greater than the first verification threshold and the second verification threshold is greater than the second verification threshold, determining that the first running exception signal and the second running exception signal pass verification, and early warning the running of a first transformer corresponding to the first running exception signal and a second transformer corresponding to the second running exception signal.

Preferably, the warning prompt of the abnormal state passing the verification includes:

acquiring integral parts of operation evaluation values corresponding to a first transformer and a second transformer which pass the verification, setting the integral parts as early warning values, matching the early warning values with a pre-constructed early warning table to acquire corresponding early warning ranges and corresponding early warning contents, and prompting specific abnormity according to the corresponding early warning contents;

and analyzing and positioning the distribution position of the temperature abnormality in the early warning content.

Preferably, the analyzing and locating the distribution position of the temperature anomaly in the early warning content includes:

generating a positioning instruction according to the temperature abnormity in the early warning content, controlling the thermal infrared imager to work according to the positioning instruction, and acquiring an infrared thermal imaging image in the transformer;

performing modular processing on the infrared thermal imaging image to obtain an image partition set comprising a plurality of monitoring areas;

acquiring pixel values of all pixel points in a plurality of monitoring areas in the image division set, and summing the pixel values of all the pixel points to obtain a pixel sum;

arranging a plurality of pixels in a descending order, setting the pixels at the front p bits and the corresponding monitoring areas as selected areas, and generating a prompt of abnormal temperature according to the positions of the selected areas; p is a positive integer.

Preferably, the modularizing the infrared thermographic image comprises:

acquiring a midpoint of the infrared thermal imaging image, setting the midpoint as an origin, and establishing a two-dimensional coordinate system according to a preset coordinate axis direction and a preset coordinate distance;

acquiring the areas of four coordinate regions on the two-dimensional coordinate system, reducing the areas of the coordinate regions according to a preset reduction proportion, and setting the reduced coordinate regions as a monitoring region image partition set;

and the monitoring areas and the corresponding area coordinates thereof form an image partition set.

Preferably, the integrating the abnormal data and the overhaul data passing the verification in the preset evaluation period includes:

acquiring the abnormal type of abnormal data in a preset evaluation period and the overhaul times and the overhaul duration in the overhaul data; matching the acquired abnormal type with a pre-constructed abnormal type weight table to acquire and mark a corresponding abnormal weight; respectively extracting and marking the numerical values of the overhaul times and the overhaul duration;

the abnormal weight of the value mark is combined with the maintenance times and the maintenance duration to obtain the maintenance evaluation value of the transformer; and analyzing and evaluating the overhaul evaluation value to obtain the integral operation state of the transformer, carrying out prediction prompt on the operation life of the transformer in a self-adaptive manner, and shortening the overhaul time of the transformer in a self-adaptive manner.

In order to solve the above problem, the present invention also provides an electronic device, including:

a memory storing at least one instruction; and

and the processor executes the instructions stored in the memory to realize the intelligent monitoring method for the transformer fault.

In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement the above method for intelligently monitoring transformer faults.

Compared with the background art: according to the invention, the data items in different aspects of the transformer in operation are simultaneously calculated, the real-time operation state of the transformer is evaluated according to the calculation result, meanwhile, the abnormal state in the evaluation result is checked to eliminate the error of a single monitoring result, and the abnormal operation states in different degrees are subjected to differential checking and checking, so that the accuracy and timeliness of monitoring the abnormal operation state can be improved, the specific position of temperature abnormality is further analyzed and determined, and the accuracy and integrity of fault monitoring prompt can be effectively improved; the overall operation state of the transformer is evaluated from the aspects of abnormity and maintenance of the operation of the transformer, the operation life of the transformer is predicted according to the evaluation result, and the maintenance time length is adaptively adjusted to improve the operation effect of the transformer; compared with the prior art that the whole state of the transformer is overhauled regularly, the intelligent monitoring method, the electronic equipment and the computer readable storage medium for the transformer fault can achieve the effects of more comprehensive and intelligent monitoring and overhauling, and therefore the problem that the authenticity of different abnormal running states cannot be verified, the whole running state of the transformer can be evaluated, and overhauling time is shortened adaptively can be solved.

Drawings

Fig. 1 is a schematic flowchart of an intelligent transformer fault monitoring method according to an embodiment of the present invention;

FIG. 2 is a flow chart illustrating a method implemented by an embodiment of the invention;

FIG. 3 is a schematic flow chart diagram illustrating another method implemented by an embodiment of the invention;

FIG. 4 is a flow chart illustrating another method implemented by an embodiment of the invention;

fig. 5 is a schematic structural diagram of an electronic device for implementing the intelligent transformer fault monitoring method according to an embodiment of the present invention.

The implementation, functional features and advantages of the present invention will be further described with reference to the accompanying drawings.

Detailed Description

It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

The embodiment of the application provides an intelligent monitoring method for transformer faults. The execution subject of the intelligent monitoring method for transformer faults includes, but is not limited to, at least one of electronic devices such as a server, a terminal and the like which can be configured to execute the method provided by the embodiments of the present application. In other words, the intelligent monitoring method for transformer faults can be executed by software or hardware installed in a terminal device or a server device, and the software can be a block chain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.

Example 1:

fig. 1 is a schematic flow chart of an intelligent transformer fault monitoring method according to an embodiment of the present invention. In this embodiment, the method for intelligently monitoring a transformer fault includes:

s1: monitoring and data acquisition are carried out on different areas of the transformer during operation, so as to obtain a monitoring data set; the monitoring data set comprises temperature data, loudness data and hydrogen data;

in the embodiment of the invention, the operation of the transformer is monitored from the aspects of temperature, loudness and hydrogen, so that the operation fault of the transformer can be timely and efficiently found, wherein when the inside of the transformer has a fault, no matter the fault is an overheating fault or a discharging fault, the molecular structure of oil can be damaged, and a large amount of hydrogen is cracked, so that the fault of the transformer can be accurately monitored by monitoring and analyzing the hydrogen inside the transformer;

in addition, various aspects of data can be respectively passed through a temperature sensor, a loudness sensor and a hydrogen sensor; the method is different from the method for carrying out data acquisition, analysis and early warning through each sensor in the existing scheme, and in the embodiment of the invention, on the basis of the existing monitoring scheme, data verification is carried out on abnormal conditions occurring in analysis and evaluation, and the abnormal temperature is verified and positioned through the thermal infrared imager, so that the accuracy of data analysis and evaluation can be effectively improved, and the intelligent monitoring and early warning of transformer faults are realized.

S2: performing feature extraction and definition on each item of data in the monitoring data set to obtain a monitoring definition set containing temperature definition data, loudness definition data and hydrogen definition data; the method comprises the following steps:

acquiring temperature data, loudness data and hydrogen data in a monitoring data set;

respectively extracting values of real-time temperature, real-time loudness and real-time hydrogen concentration in the temperature data, the loudness data and the hydrogen data, and defining and marking the values as SWi, Sxi and SQi; i ═ 1, 2, 3,. and n, n is a positive integer;

respectively numbering the regions corresponding to the collected real-time temperature, real-time loudness and real-time hydrogen concentration, and marking the regions as SWij, Sxij and SQij; j ═ 1, 2, 3,.. m }, m being a positive integer;

sequentially arranging and combining the real-time temperature, real-time loudness, real-time hydrogen concentration and corresponding regions of the real-time temperature, real-time loudness and real-time hydrogen concentration of the definition mark according to the time sequence to obtain temperature definition data, loudness definition data and hydrogen definition data;

the temperature definition data, loudness definition data, and hydrogen definition data constitute a monitoring definition set.

In the embodiment of the invention, the purpose of taking values, numbering and marking the collected data is to standardize and standardize the data items so as to improve the accuracy of data calculation and eliminate errors of calculation and analysis among different types of data.

S3: acquiring the area position of the transformer and the corresponding area weight thereof, and simultaneously acquiring the corresponding operation evaluation value YP by combining the area weight of the transformer with the monitoring definition set; the method comprises the following steps:

matching the obtained region position of the transformer with a pre-constructed region position table to obtain a corresponding region weight, and defining and marking the region weight as QQi; the region weight can realize different importance corresponding to the transformers in different region positions, so as to realize differential representation;

the transformer comprises a transformer body, a region position table, a power supply unit and a control unit, wherein the region position table is composed of a plurality of different region positions and corresponding region weights, the different region positions are preset with one region weight, and the specific numerical value of the region weight can be set based on the big data of the existing transformer operation;

YP＝QQi×[a1×(SWi-SWi0)+a2×(SXi-SXi0)+a3×(SQi-SQi0)]

in the formula, a1, a2 and a3 are different proportionality coefficients which are all larger than zero, SWi0 is preset standard temperature, SXi0 is preset standard loudness, and SQi0 is preset standard hydrogen concentration; the preset proportionality coefficient and each preset standard value in the formula are set by those skilled in the art according to actual conditions or obtained through simulation of a large amount of data.

In the embodiment of the invention, the operation evaluation value is a numerical value used for integrally evaluating the real-time operation state of the transformer by combining data of all aspects during operation of the transformer; the differential monitoring and analysis of transformer faults are realized by combining the area weight corresponding to each transformer with each running data item, and the method is different from the prior scheme that the data items in different aspects are sequentially matched and analyzed.

S4: if the minimum value YPmin of the operation evaluation range is less than or equal to the maximum value YPmax of the operation evaluation range, judging that the operation state of the corresponding transformer is slightly abnormal, generating a first operation abnormity signal, and setting the corresponding transformer as a first target transformer according to the first operation abnormity signal;

if the operation evaluation value YP is larger than the maximum operation evaluation range value YPmax, judging that the operation state of the corresponding transformer is moderately abnormal, generating a second operation abnormal signal, and setting the corresponding transformer as a second target transformer according to the second operation abnormal signal;

in the embodiment of the invention, the real-time state of the operation of the transformer is integrally evaluated based on the operation evaluation value so as to quickly find the abnormality of the operation of the transformer; in addition, due to certain errors in single data analysis, the abnormal operation state needs to be checked further, so that the accuracy of transformer fault monitoring and analysis can be effectively improved;

in addition, when the operation evaluation range minimum value YPmin > the operation evaluation value YP, it indicates that the operation state of the transformer is normal.

S5: verifying the authenticity of the first target transformer and the second target transformer in abnormal states respectively, and giving an early warning prompt on the abnormal states passing the verification;

as shown in fig. 2, the specific steps include:

s51: verifying the operation of the first target transformer and the second target transformer respectively according to the first running difference signal and the second running difference signal;

s52: counting the occurrence time lengths of the first and second running exception signals in a preset first verification time period and a preset second verification time period respectively, and calibrating the total occurrence time lengths of the first and second running exception signals as a first running exception time length and a second running exception time length respectively; the units can all be seconds;

it should be noted that, because the abnormal degree corresponding to the second transaction exception signal is greater than the abnormal degree corresponding to the first transaction exception signal, the duration of the second verification period is less than the duration of the first verification period, so as to implement differentiated verification and verification of different abnormal states;

s53: respectively extracting values of the first running away time length and the second running away time length and marking the values as YSk, wherein k is 1 and 2; obtaining the check value HY of the first running exception signal and the second running exception signal through the formula HY YSk/HSk; wherein HSk is the total duration corresponding to the first and second verification periods;

s54: setting the check value corresponding to the first running exception signal and the check value corresponding to the second running exception signal as a first check value and a second check value respectively and evaluating;

s55: if the first verification value is larger than the first verification threshold and the second verification threshold is larger than the second verification threshold, judging that the first running exception signal and the second running exception signal pass verification, and early warning operation of a first transformer corresponding to the first running exception signal and a second transformer corresponding to the second running exception signal;

otherwise, the verification of the first running exception signal and the second running exception signal is judged not to pass;

in addition, the abnormal state that will pass the verification carries out early warning suggestion, includes:

acquiring integral parts of operation evaluation values corresponding to the first transformer and the second transformer which pass the verification, setting the integral parts as early warning values, matching the early warning values with a pre-constructed early warning table to acquire corresponding early warning ranges and corresponding early warning contents, and prompting specific abnormity according to the corresponding early warning contents;

the larger the early warning value is, the more corresponding abnormal types are represented, the different abnormal types correspond to a preset early warning range, the early warning range is associated with early warning content, and the early warning content comprises but is not limited to temperature abnormality, loudness abnormality and hydrogen abnormality;

and analyzing and positioning the distribution position of the temperature abnormality in the early warning content, comprising the following steps:

as shown in fig. 3, the infrared thermographic image is subjected to a modular process, which includes:

s561: acquiring a midpoint of an infrared thermal imaging image, setting the midpoint as an origin, and establishing a two-dimensional coordinate system according to a preset coordinate axis direction and a coordinate distance;

s562: acquiring the areas of four coordinate areas on a two-dimensional coordinate system, reducing the areas of the coordinate areas according to a preset reduction proportion, and setting the reduced coordinate areas as a monitoring area image partition set;

s563: the monitoring areas and the corresponding area coordinates form an image partition set;

s564: acquiring pixel values of all pixel points in a plurality of monitoring areas in an image division set, and summing the pixel values of all the pixel points to obtain a pixel sum;

s565: arranging a plurality of pixels in a descending order, setting the front p-bit pixels and the corresponding monitoring areas as selected areas, and generating a prompt of abnormal temperature according to the positions of the selected areas; p is a positive integer.

The embodiment of the invention can improve the accuracy and timeliness of monitoring the abnormal operation state by carrying out differential verification and audit on the abnormal operation states with different degrees, and further analyze and determine the specific position of the temperature abnormality, can effectively improve the accuracy and integrity of fault monitoring and prompting, and is different from the prior scheme that only one type of abnormality can be early warned and prompted.

S6: integrating the abnormal data and the overhaul data which pass the verification in a preset evaluation period, and generating a service life prediction prompt in a self-adaptive manner according to an integrated result;

as shown in fig. 4, the specific steps include:

s61: acquiring the abnormal type of abnormal data in a preset evaluation period and the overhaul times and the overhaul duration in the overhaul data;

s62: matching the obtained exception type with a pre-constructed exception type weight table to obtain a corresponding exception weight, and marking the exception weight as YQi;

the abnormal type weight table is composed of a plurality of different abnormal types and corresponding abnormal weights thereof, the different abnormal types are preset with one corresponding abnormal weight, and the specific numerical value of the abnormal weight is set based on the existing abnormal big data of the transformer;

s63: respectively extracting numerical values of the overhaul times and the overhaul duration and marking the numerical values as JCi and JSi; the unit of the overhaul time length can be hours;

s64: combining the abnormal weight of the value mark with the overhaul times and the overhaul duration, and calculating to obtain an overhaul evaluation value JP of the transformer through a formula; the formula is:

wherein b1 and b2 are different proportionality coefficients and are both larger than zero;

s65: analyzing and evaluating the overhaul evaluation value to obtain the integral operation state of the transformer, and predicting and prompting the operation life of the transformer in a self-adaptive manner; the formulas in the embodiment of the invention are all a formula which is obtained by removing dimensions, taking the numerical value of the dimension to calculate and acquiring a large amount of data to perform software simulation to obtain the closest real condition.

Wherein, carry out analysis and evaluation to the maintenance evaluation value and obtain the whole running state of transformer, include:

if the maintenance evaluation value is smaller than a preset maintenance evaluation threshold value, judging that the overall operation state of the corresponding transformer is normal and generating a normal detection and evaluation signal;

if the overhaul evaluation value is not less than the preset overhaul evaluation threshold value and not more than v% of the overhaul evaluation threshold value, and v is a real number greater than one hundred, judging that the overall operation state of the corresponding transformer is slightly abnormal and generating a first overhaul signal; the first detection and estimation signal indicates that the operation life of the corresponding transformer is lower than the normal operation life; setting the corresponding transformer as a first target transformer according to the first detection and estimation signal, and adaptively shortening the overhaul time of the first target transformer;

if the overhaul evaluation value is larger than v% of a preset overhaul evaluation threshold value, judging that the overall operation of the corresponding transformer is moderate and abnormal and generating a second overhaul signal; the second detection and estimation signal indicates that the operation life of the corresponding transformer is far shorter than the normal operation life; setting the corresponding transformer as a second target transformer according to a second detection and estimation signal, and adaptively shortening the overhaul time length of the second target transformer, wherein the overhaul time length of the shortened second target transformer is less than that of the first target transformer;

the plurality of normal detection and estimation signals, the first detection and estimation signal and the second detection and estimation signal form the result of the abnormity and maintenance integration of the transformer.

In the embodiment of the invention, the overall operation state of the transformer is evaluated from the aspects of abnormity and maintenance of the operation of the transformer, the operation life of the transformer is predicted according to the evaluation result, and the operation effect of the transformer is improved by adaptively adjusting the maintenance time; compared with the conventional scheme that the overall state of the transformer is overhauled regularly, the transformer state monitoring method and device can achieve the effects of comprehensive and intelligent monitoring and overhauling.

Compared with the background art: according to the invention, the data items in different aspects of the transformer in operation are simultaneously calculated, the real-time operation state of the transformer is evaluated according to the calculation result, meanwhile, the abnormal state in the evaluation result is checked to eliminate the error of a single monitoring result, and the abnormal operation states in different degrees are subjected to differential checking and checking, so that the accuracy and timeliness of monitoring the abnormal operation state can be improved, the specific position of temperature abnormality is further analyzed and determined, and the accuracy and integrity of fault monitoring prompt can be effectively improved; the overall operation state of the transformer is evaluated from the aspects of abnormity and maintenance of the operation of the transformer, the operation life of the transformer is predicted according to the evaluation result, and the maintenance time length is adaptively adjusted to improve the operation effect of the transformer; compared with the prior art that the overall state of the transformer is periodically overhauled, the transformer fault intelligent monitoring method, the electronic device and the computer readable storage medium can achieve the effects of more comprehensive and intelligent monitoring and overhauling, and therefore the problem that the authenticity of different abnormal running states cannot be verified, the overall running state of the transformer cannot be evaluated, and overhauling time is shortened adaptively can be solved.

Example 2:

fig. 5 is a schematic structural diagram of an electronic device for implementing an intelligent monitoring method for transformer faults according to an embodiment of the present invention.

The electronic device 1 may include a processor 10, a memory 11 and a bus, and may further include a computer program, such as a program 12 for intelligent monitoring method of transformer failure, stored in the memory 11 and executable on the processor 10.

The memory 11 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only to store application software installed in the electronic device 1 and various types of data, such as a code of the intelligent monitoring program 12 for a transformer fault, etc., but also to temporarily store data that has been output or will be output.

The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the whole electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device 1 by running or executing a program or a module (for example, an intelligent monitoring program for transformer failure, etc.) stored in the memory 11 and calling data stored in the memory 11.

The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.

Fig. 5 only shows an electronic device with components, and it will be understood by a person skilled in the art that the structure shown in fig. 5 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or a combination of certain components, or a different arrangement of components.

For example, although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so as to implement functions of charge management, discharge management, power consumption management, and the like through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.

Further, the electronic device 1 may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used for establishing a communication connection between the electronic device 1 and other electronic devices.

Optionally, the electronic device 1 may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.

It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.

The intelligent monitoring program 12 for transformer failure stored in the memory 11 of the electronic device 1 is a combination of instructions, which when executed in the processor 10, can realize:

monitoring and data acquisition are carried out on different areas of the transformer during operation, so as to obtain a monitoring data set; the monitoring data set comprises temperature data, loudness data and hydrogen data;

Specifically, the specific implementation method of the processor 10 for the instruction may refer to the description of the relevant steps in the embodiments corresponding to fig. 1 to fig. 5, which is not repeated herein.

Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).

The invention also provides a computer-readable storage medium having a computer program stored thereon, the computer program being executable by a processor of an electronic device.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.

It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.

The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent monitoring method for transformer faults is characterized by comprising the following steps:

2. The method according to claim 1, wherein the extracting and defining the characteristics of each item in the monitored data set comprises:

respectively numbering and marking the regions corresponding to the acquired real-time temperature, real-time loudness and real-time hydrogen concentration;

3. The method for intelligently monitoring transformer faults according to claim 1, wherein the step of obtaining the corresponding operation evaluation value YP by combining the area weight of the transformer and the monitoring definition set comprises the following steps:

YP=QQi×[a1×(SWi-SWi0)+a2×(SXi-SXi0)+a3×(SQi-SQi0)]

4. The method according to claim 1, wherein the verifying the authenticity of the first target transformer and the second target transformer in abnormal states respectively comprises:

respectively verifying the operation of the first target transformer and the second target transformer according to the first running error signal and the second running error signal;

extracting the values of the first and second break-in duration respectively and marking as YSk, k =1, 2; obtaining the verification value HY of the first running exception signal and the second running exception signal through the formula HY = YSk/HSk; wherein HSk is the total duration corresponding to the first and second verification periods;

5. The intelligent monitoring method for transformer faults as claimed in claim 1, wherein the early warning and prompting of the abnormal state passing the verification comprises:

6. The intelligent monitoring method for transformer faults as claimed in claim 5, wherein the analyzing and locating the distribution positions of the temperature anomalies in the early warning content comprises:

acquiring pixel values of all pixel points on a plurality of monitoring areas in the image partition set, and summing the pixel values of all the pixel points to obtain a pixel sum;

7. The intelligent monitoring method for transformer faults as claimed in claim 6, wherein the modular processing of the infrared thermal imaging images comprises:

8. The intelligent monitoring method for transformer faults as claimed in claim 1, wherein the integrating of the abnormal data and the overhaul data passing the verification in the preset evaluation period comprises:

9. An electronic device, the electronic device comprising:

a memory storing at least one instruction; and

10. A computer readable storage medium having at least one instruction stored therein, the at least one instruction being executed by a processor in an electronic device to implement a transformer fault intelligent monitoring method as described above.