An intelligent monitoring method for transformer faults

Abstract

The invention relates to the field of intelligent monitoring technology, and discloses an intelligent monitoring method for transformer faults; by jointly calculating various data items of different aspects of transformer operation and evaluating the real-time operating state of the transformer, and by reviewing the abnormal state in the evaluation result, the error existing in a single monitoring result is eliminated; by performing differentiated verification and review on abnormal operating states of different degrees, the accuracy and timeliness of abnormal operating state monitoring can be improved, and the specific location of the temperature anomaly can be further analyzed and determined; by evaluating the overall operating state of the transformer from the abnormal aspects and maintenance aspects of the transformer operation, and adaptively adjusting the maintenance time, the operating effect of the transformer is improved; the invention is used to solve the problem that the authenticity of different abnormal operating states cannot be verified in the existing solution, and the overall operating state of the transformer cannot be evaluated to adaptively shorten the maintenance time.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring technology, and in particular to an intelligent monitoring method for transformer faults, electronic equipment and a computer-readable storage medium. Background Art

[0002] With the development of modern transformer maintenance technology, condition monitoring technology has emerged, breaking the limitations of previous transformer information collection. At present, the power system can continuously record various relevant data that affect the life of the transformer in real time through online monitoring of the transformer. Automated processing of this data can detect potential faults early and achieve basic transformer condition maintenance.

[0003] When implementing the existing transformer fault monitoring scheme, it is not possible to combine the data from different aspects to make an overall assessment of the real-time operating status of the transformer. It can only perform single data matching and analysis, and cannot review and verify the authenticity of the monitored abnormal status, resulting in poor accuracy of monitoring and analysis. At the same time, it is also impossible to evaluate the overall operating status of the transformer from different aspects. The transformer can only be regularly inspected and maintained, resulting in poor overall monitoring of transformers in different operating states. Summary of the invention

[0004] The present invention provides an intelligent monitoring method for transformer faults, an electronic device and a computer-readable storage medium, the main purpose of which is to solve the technical problem that the existing solutions cannot verify the authenticity of different abnormal operating states and evaluate the overall operating state of the transformer to adaptively shorten the maintenance time.

[0005] To achieve the above object, the present invention provides an intelligent monitoring method for transformer faults, the method comprising:

[0006] Monitor and collect data from different areas of the transformer during operation to obtain a monitoring data set; the monitoring data set includes temperature data, loudness data, and hydrogen data;

[0007] Extracting and defining features of each data in the monitoring data set to obtain a monitoring definition set including temperature definition data, loudness definition data and hydrogen definition data;

[0008] Obtain the regional location of the transformer and its corresponding regional weight, and combine the regional weight of the transformer with the monitoring definition set to obtain the corresponding operation evaluation value YP;

[0009] If the minimum value of the operation evaluation range YPmin≤the operation evaluation value YP≤the maximum value of the operation evaluation range YPmax, it is determined that the operation state of the corresponding transformer is slightly abnormal and a first abnormal operation signal is generated, and the corresponding transformer is set as a first target transformer according to the first abnormal operation signal;

[0010] If the operation evaluation value YP> the maximum value YPmax of the operation evaluation range, the operation state of the corresponding transformer is determined to be moderately abnormal and a second abnormal operation signal is generated, and the corresponding transformer is set as a second target transformer according to the second abnormal operation signal;

[0011] Verify the authenticity of the first target transformer and the second target transformer in abnormal state respectively, and issue an early warning for the abnormal state that has passed the verification;

[0012] The verified abnormal data and maintenance data are integrated within the preset evaluation period, and the life prediction prompts are adaptively generated based on the integration results.

[0013] Preferably, extracting and defining features of each data in the monitoring data set includes:

[0014] Acquire temperature data, loudness data and hydrogen data in the monitoring data set;

[0015] Respectively extracting the values ​​of the real-time temperature, real-time loudness and real-time hydrogen concentration from the temperature data, loudness data and hydrogen data and defining labels;

[0016] Numbering and marking the areas corresponding to the collected real-time temperature, real-time loudness and real-time hydrogen concentration respectively;

[0017] The real-time temperature, real-time loudness and real-time hydrogen concentration of the definition mark and their corresponding areas are arranged and combined in chronological order to obtain a monitoring definition set including temperature definition data, loudness definition data and hydrogen definition data.

[0018] Preferably, the step of combining the regional weight of the transformer with a monitoring definition set to obtain a corresponding operation evaluation value YP includes:

[0019] Match the obtained regional location of the transformer with the pre-built regional location table to obtain the corresponding regional weight and define it as QQi;

[0020] The regional weight of the definition mark is combined with each data of the definition mark in the monitoring definition set, and the operation evaluation value YP corresponding to the transformer is obtained by calculation through the formula; the formula is:

[0021] YP=QQi×[a1×(SWi-SWi0)+a2×(SXi-SXi0)+a3×(SQi-SQi0)]

[0022] Wherein, a1, a2, a3 are different proportional coefficients all greater than zero, SWi0 is a preset standard temperature, SXi0 is a preset standard loudness, and SQi0 is a preset standard hydrogen concentration.

[0023] Preferably, the verifying the authenticity of the first target transformer and the second target transformer in abnormal state respectively includes:

[0024] Verifying the operation of the first target transformer and the second target transformer respectively according to the first abnormal operation signal and the second abnormal operation signal;

[0025] Counting the durations of the first motion abnormality signal and the second motion abnormality signal in the preset first verification period and the second verification period, and marking the total durations of the first motion abnormality signal and the second motion abnormality signal as the first motion abnormality duration and the second motion abnormality duration, respectively;

[0026] The values ​​of the first abnormal operation duration and the second abnormal operation duration are extracted respectively and marked as YSk, k=1, 2; the verification value HY of the first abnormal operation signal and the second abnormal operation signal is obtained by the formula HY=YSk / HSk; where HSk is the total duration corresponding to the first verification period and the second verification period;

[0027] The verification value corresponding to the first abnormal operation signal and the verification value corresponding to the second abnormal operation signal are respectively set as the first verification value and the second verification value and evaluated;

[0028] If the first verification value is greater than the first verification threshold and the second verification threshold is greater than the second verification threshold, it is determined that the verification of the first operation abnormality signal and the second operation abnormality signal is passed, and an early warning is issued for the operation of the first transformer corresponding to the first operation abnormality signal and the second transformer corresponding to the second operation abnormality signal.

[0029] Preferably, the step of providing an early warning prompt for the abnormal state that has passed the verification includes:

[0030] Obtain the integer part of the operation evaluation value corresponding to the first transformer and the second transformer that have passed the verification and set it as the warning value, match the warning value with the pre-built warning table to obtain the corresponding warning range and its corresponding warning content, and prompt specific abnormalities according to the corresponding warning content;

[0031] And, the distribution position of the temperature anomaly in the warning content is analyzed and located.

[0032] Preferably, analyzing and locating the distribution position of the temperature anomaly in the warning content includes:

[0033] Generate a positioning instruction according to the temperature anomaly in the warning content, and control the infrared thermal imager to work and obtain an infrared thermal imaging image inside the transformer according to the positioning instruction;

[0034] Modular processing is performed on the infrared thermal imaging image to obtain an image partition set including a plurality of monitoring areas;

[0035] Obtaining pixel values ​​of all pixel points on a plurality of monitoring areas in the image partition set, and summing the pixel values ​​of all pixel points to obtain a pixel sum;

[0036] Arrange a number of the pixels in descending order, set the first p pixels and the corresponding monitoring area as the selected area, and generate a temperature abnormality prompt according to the position of the selected area; p is a positive integer.

[0037] Preferably, the modular processing of the infrared thermal imaging image comprises:

[0038] Obtaining the midpoint of the infrared thermal imaging image and setting it as the origin, and establishing a two-dimensional coordinate system according to preset coordinate axis directions and coordinate distances;

[0039] Acquire the areas of the four coordinate regions on the two-dimensional coordinate system, reduce the areas of the coordinate regions according to a preset reduction ratio, and set the reduced coordinate regions as monitoring area image division sets;

[0040] A plurality of monitoring areas and their corresponding area coordinates constitute an image partition set.

[0041] Preferably, the integration of the verified abnormal data and maintenance data within a preset evaluation period includes:

[0042] Obtain the abnormal type of abnormal data within a preset evaluation period and the number of maintenance times and maintenance duration in the maintenance data; match the acquired abnormal type with the pre-built abnormal type weight table to obtain the corresponding abnormal weight and mark it; respectively extract the values ​​of the number of maintenance times and maintenance duration and mark them;

[0043] The abnormal weight of the value mark is combined with the number of maintenance times and the maintenance duration to obtain the maintenance evaluation value of the transformer; the maintenance evaluation value is analyzed and evaluated to obtain the overall operating status of the transformer, and its operating life is adaptively predicted and prompted, as well as the maintenance duration of the transformer is adaptively shortened.

[0044] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0045] a memory storing at least one instruction; and

[0046] The processor executes the instructions stored in the memory to implement the above-mentioned intelligent monitoring method for transformer faults.

[0047] In order to solve the above problem, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned intelligent monitoring method for transformer faults.

[0048] Compared with the background technology: the present invention performs joint calculations on various data items of different aspects of transformer operation, and evaluates the real-time operating status of the transformer according to the calculation results, and at the same time, reviews the abnormal status in the evaluation results to eliminate the errors existing in a single monitoring result. By performing differentiated verification and review on abnormal operating states of different degrees, the accuracy and timeliness of abnormal operating state monitoring can be improved, and the specific location of the temperature anomaly is further analyzed and determined, which can effectively improve the accuracy and completeness of fault monitoring prompts; by evaluating the overall operating status of the transformer from the abnormal aspects and maintenance aspects of its operation, and predicting its operating life according to the evaluation results, and adaptively adjusting the maintenance time to improve the operation effect of the transformer; compared with the existing scheme of regularly inspecting the overall status of the transformer, the present invention can achieve more comprehensive and intelligent monitoring and maintenance effects, so the intelligent monitoring method, electronic device and computer-readable storage medium of transformer faults proposed in the present invention can solve the problem that the authenticity of different abnormal operating states cannot be verified, and the overall operating status of the transformer can be evaluated to adaptively shorten the maintenance time. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic flow chart of an intelligent monitoring method for transformer faults provided by one embodiment of the present invention;

[0050] Figure 2 A schematic diagram of a flow chart of a method implemented by an embodiment of the present invention;

[0051] Figure 3 A flowchart of another method implemented by an embodiment of the present invention;

[0052] Figure 4 A schematic diagram of a flow chart of another method implemented by an embodiment of the present invention;

[0053] Figure 5 A schematic diagram of the structure of an electronic device for implementing the intelligent monitoring method for transformer faults provided in one embodiment of the present invention.

[0054] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0055] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0056] The embodiment of the present 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 the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the intelligent monitoring method for transformer faults can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0057] Embodiment 1:

[0058] Reference Figure 1 FIG. 1 is a flow chart of an intelligent monitoring method for transformer faults provided by an embodiment of the present invention. In this embodiment, the intelligent monitoring method for transformer faults includes:

[0059] S1: Monitor and collect data from different areas of the transformer during operation to obtain a monitoring data set; the monitoring data set includes temperature data, loudness data, and hydrogen data;

[0060] In the embodiment of the present invention, the operation of the transformer is monitored from the aspects of temperature, loudness and hydrogen, so as to timely and efficiently discover the fault of the transformer operation. When a fault occurs inside the transformer, whether it is an overheating fault or a discharge fault, the molecular structure of the oil will be destroyed, thereby cracking a large amount of hydrogen. Therefore, by monitoring and analyzing the hydrogen inside the transformer, accurate monitoring of the transformer fault can be achieved;

[0061] In addition, data from various aspects can be collected through temperature sensors, loudness sensors and hydrogen sensors respectively; different from the existing scheme in which data is collected and analyzed and warned through various sensors, the embodiments of the present invention, based on the existing monitoring scheme, perform data verification on abnormal situations that appear in the analysis and evaluation, and use infrared thermal imagers to verify and locate abnormal temperatures, which can effectively improve the accuracy of data analysis and evaluation, thereby realizing intelligent monitoring and early warning of transformer faults.

[0062] S2: Extract and define the features of each data in the monitoring data set to obtain a monitoring definition set including temperature definition data, loudness definition data and hydrogen definition data; including:

[0063] Obtain temperature data, loudness data, and hydrogen data from the monitoring data set;

[0064] The values ​​of real-time temperature, real-time loudness and real-time hydrogen concentration in the temperature data, loudness data and hydrogen data are extracted respectively and defined as SWi, SXi and SQi; i = {1, 2, 3, ..., n}, where n is a positive integer;

[0065] The regions corresponding to the collected real-time temperature, real-time loudness and real-time hydrogen concentration are numbered and marked as SWij, SXij and SQij respectively; j = {1, 2, 3, ..., m}, where m is a positive integer;

[0066] The real-time temperature, real-time loudness and real-time hydrogen concentration of the definition mark and their corresponding areas are sequentially arranged and combined in chronological order to obtain temperature definition data, loudness definition data and hydrogen definition data;

[0067] The temperature definition data, the loudness definition data and the hydrogen definition data constitute the monitoring definition set.

[0068] In the embodiment of the present invention, the purpose of taking values, numbering and marking each collected data is to standardize and normalize each data item so as to improve the accuracy of data calculation and eliminate errors in calculation and analysis between different types of data.

[0069] S3: Obtain the regional location of the transformer and its corresponding regional weight, and combine the regional weight of the transformer with the monitoring definition set to obtain the corresponding operation evaluation value YP; including:

[0070] The regional location of the obtained transformer is matched with the pre-built regional location table to obtain the corresponding regional weight and define it as QQi; the regional weight can realize the different importance of transformers in different regional locations to achieve differentiated representation;

[0071] Among them, the regional location table is composed of several different regional locations and their corresponding regional weights. Different regional locations are pre-set with a regional weight. The specific value of the regional weight can be set based on the existing transformer operation big data;

[0072] Combine the regional weight of the defined mark with the data of the defined mark in the monitoring definition set, and obtain the operation evaluation value YP corresponding to the transformer through calculation; the formula is:

[0073] YP=QQi×[a1×(SWi-SWi0)+a2×(SXi-SXi0)+a3×(SQi-SQi0)]

[0074] In the formula, a1, a2, and a3 are different proportional coefficients all greater than zero, SWi0 is a preset standard temperature, SXi0 is a preset standard loudness, and SQi0 is a preset standard hydrogen concentration; the preset proportional coefficients and each preset standard value in the formula are set by technicians in this field according to actual conditions or obtained through a large amount of data simulation.

[0075] In the embodiment of the present invention, the operation evaluation value is a numerical value used to combine data from various aspects of the transformer during operation to comprehensively evaluate its real-time operating status; differentiated monitoring and analysis of transformer faults are achieved by combining the regional weights corresponding to each transformer with various data items of operation. Different from the existing solution in which data items of different aspects are matched and analyzed in turn, the embodiment of the present invention can achieve a more comprehensive and efficient analysis effect.

[0076] S4: if the minimum value of the operation evaluation range YPmin≤the operation evaluation value YP≤the maximum value of the operation evaluation range YPmax, it is determined that the operation state of the corresponding transformer is slightly abnormal and a first abnormal operation signal is generated, and the corresponding transformer is set as a first target transformer according to the first abnormal operation signal;

[0077] If the operation evaluation value YP> the maximum value YPmax of the operation evaluation range, the operation state of the corresponding transformer is determined to be moderately abnormal and a second operation abnormality signal is generated, and the corresponding transformer is set as a second target transformer according to the second operation abnormality signal;

[0078] In the embodiment of the present invention, the real-time operation status of the transformer is evaluated as a whole based on the operation evaluation value, so as to quickly find the abnormality of the transformer operation; in addition, since there is a certain error in the single data analysis, it is necessary to further verify the abnormal operation status that occurs, which can effectively improve the accuracy of transformer fault monitoring and analysis;

[0079] In addition, when the minimum value of the operation evaluation range YPmin> the operation evaluation value YP, it indicates that the operation status of the transformer is normal.

[0080] S5: Verify the authenticity of the first target transformer and the second target transformer in the abnormal state respectively, and issue an early warning prompt for the abnormal state that has passed the verification;

[0081] like Figure 2 As shown, the specific steps include:

[0082] S51: verifying the operation of the first target transformer and the second target transformer respectively according to the first abnormal operation signal and the second abnormal operation signal;

[0083] S52: Counting the durations of the first motion abnormality signal and the second motion abnormality signal in the preset first verification period and the second verification period, and marking the total durations of the first motion abnormality signal and the second motion abnormality signal as the first motion abnormality duration and the second motion abnormality duration, respectively; both can be in seconds;

[0084] It should be noted that, because the abnormality level corresponding to the second abnormal signal is greater than the abnormality level corresponding to the first abnormal signal, the duration of the second verification period is shorter than the duration of the first verification period, so as to achieve differentiated audit verification of different abnormal states;

[0085] S53: extract the values ​​of the first abnormal operation duration and the second abnormal operation duration respectively and mark them as YSk, k=1, 2; obtain the verification value HY of the first abnormal operation signal and the second abnormal operation signal by the formula HY=YSk / HSk; where HSk is the total duration corresponding to the first verification period and the second verification period;

[0086] S54: setting the verification value corresponding to the first abnormal operation signal and the verification value corresponding to the second abnormal operation signal as the first verification value and the second verification value respectively and evaluating them;

[0087] S55: If the first verification value is greater than the first verification threshold and the second verification threshold is greater than the second verification threshold, it is determined that the verification of the first abnormal operation signal and the second abnormal operation signal is passed, and an early warning is issued for the operation of the first transformer corresponding to the first abnormal operation signal and the second transformer corresponding to the second abnormal operation signal;

[0088] Otherwise, it is determined that the verification of the first abnormal operation signal and the second abnormal operation signal fails;

[0089] In addition, the abnormal status that has been verified will be warned, including:

[0090] Obtain the integer part of the operation evaluation value corresponding to the first transformer and the second transformer that have passed the verification and set it as the warning value, match the warning value with the pre-built warning table to obtain the corresponding warning range and its corresponding warning content, and prompt specific abnormalities according to the corresponding warning content;

[0091] The larger the warning value, the more corresponding abnormality types there are. Different abnormality types correspond to a preset warning range, and the warning range is associated with a warning content, which includes but is not limited to temperature abnormality, loudness abnormality and hydrogen abnormality.

[0092] And, analyze and locate the distribution of temperature anomalies in the warning content, including:

[0093] Generate a positioning instruction according to the temperature anomaly in the warning content, control the infrared thermal imager to work and obtain the infrared thermal imaging image inside the transformer according to the positioning instruction;

[0094] like Figure 3 As shown, the infrared thermal imaging image is modularly processed, including:

[0095] S561: Obtain the midpoint of the infrared thermal imaging image and set it as the origin, and establish a two-dimensional coordinate system according to the preset coordinate axis direction and coordinate distance;

[0096] S562: Acquire the areas of four coordinate regions on the two-dimensional coordinate system, reduce the areas of the coordinate regions according to a preset reduction ratio, and set the reduced coordinate regions as monitoring area image division sets;

[0097] S563: A plurality of monitoring areas and their corresponding area coordinates constitute an image partition set;

[0098] S564: Obtain pixel values ​​of all pixel points in a plurality of monitoring areas in the image division set, and sum the pixel values ​​of all pixel points to obtain a pixel sum;

[0099] S565: Arrange a number of pixels in descending order, set the first p pixels and the corresponding monitoring area as the selected area, and generate a temperature abnormality prompt according to the position of the selected area; p is a positive integer.

[0100] The embodiment of the present invention can improve the accuracy and timeliness of abnormal operating status monitoring by performing differentiated verification and audit on abnormal operating status of different degrees, and further analyze and determine the specific location of the temperature anomaly, which can effectively improve the accuracy and completeness of fault monitoring prompts. Different from the existing solutions that can only warn and prompt a certain type of abnormality, the embodiment of the present invention can achieve better early warning and prompt effects.

[0101] S6: Integrate the abnormal data and maintenance data that have passed the verification within the preset evaluation period, and adaptively generate life prediction prompts based on the integration results;

[0102] like Figure 4 As shown, the specific steps include:

[0103] S61: Obtain the abnormal type of abnormal data within a preset evaluation period and the number of maintenance and maintenance duration in the maintenance data;

[0104] S62: Match the acquired abnormality type with the pre-built abnormality type weight table to obtain the corresponding abnormality weight and mark it as YQi;

[0105] Among them, the abnormal type weight table is composed of several different abnormal types and their corresponding abnormal weights. Different abnormal types are pre-set with a corresponding abnormal weight, and the specific value of the abnormal weight is set based on the existing transformer abnormal big data;

[0106] S63: extract the values ​​of the number of maintenance times and maintenance duration respectively and mark them as JCi and JSi; the unit of maintenance duration can be hours;

[0107] S64: The abnormal weight of the value mark is combined with the number of maintenance times and the maintenance duration, and the maintenance evaluation value JP of the transformer is obtained by calculation through a formula; the formula is:

[0108]

[0109] In the formula, b1 and b2 are different proportional coefficients and both are greater than zero;

[0110] S65: Analyze and evaluate the maintenance evaluation value to obtain the overall operating status of the transformer, and adaptively predict its operating life; the formulas in the embodiments of the present invention are all dimensionless and numerically calculated, and a formula closest to the actual situation is obtained by collecting a large amount of data for software simulation.

[0111] Among them, the maintenance evaluation value is analyzed and evaluated to obtain the overall operating status of the transformer, including:

[0112] If the maintenance evaluation value is less than the preset maintenance evaluation threshold, the overall operation state of the corresponding transformer is determined to be normal and a normal evaluation signal is generated;

[0113] If the maintenance evaluation value is not less than the preset maintenance evaluation threshold and not greater than v% of the maintenance evaluation threshold, where v is a real number greater than one hundred, it is determined that the overall operating state of the corresponding transformer is slightly abnormal and a first evaluation signal is generated; the first evaluation signal indicates that the operating life of the corresponding transformer is lower than the normal operating life; and according to the first evaluation signal, the corresponding transformer is set as the first target transformer, and the maintenance time of the first target transformer is adaptively shortened;

[0114] If the maintenance evaluation value is greater than the preset maintenance evaluation threshold value v%, the overall operation of the corresponding transformer is judged to be moderately abnormal and a second evaluation signal is generated; the second evaluation signal indicates that the operating life of the corresponding transformer is much lower than the normal operating life; and the corresponding transformer is set as the second target transformer according to the second evaluation signal, and the maintenance time of the second target transformer is adaptively shortened, and the shortened maintenance time of the second target transformer is less than the maintenance time of the first target transformer;

[0115] The plurality of normal evaluation signals, the first evaluation signal and the second evaluation signal constitute the result of transformer abnormality and maintenance integration.

[0116] In the embodiment of the present invention, the overall operating state of the transformer is evaluated from the abnormal aspects and maintenance aspects of its operation, and its operating life is predicted based on the evaluation results, and the maintenance duration is adaptively adjusted to improve the operating effect of the transformer; compared with the existing solution of regularly inspecting the overall state of the transformer, the embodiment of the present invention can achieve more comprehensive and intelligent monitoring and maintenance effects.

[0117] Compared with the background technology: the present invention performs joint calculations on various data items of different aspects of transformer operation, and evaluates the real-time operating status of the transformer according to the calculation results, and at the same time, reviews the abnormal status in the evaluation results to eliminate the errors existing in a single monitoring result. By performing differentiated verification and review on abnormal operating states of different degrees, the accuracy and timeliness of abnormal operating state monitoring can be improved, and the specific location of the temperature anomaly is further analyzed and determined, which can effectively improve the accuracy and completeness of fault monitoring prompts; by evaluating the overall operating status of the transformer from the abnormal aspects and maintenance aspects of its operation, and predicting its operating life according to the evaluation results, and adaptively adjusting the maintenance time to improve the operation effect of the transformer; compared with the regular maintenance of the overall status of the transformer in the existing scheme, the embodiment of the present invention can achieve more comprehensive and intelligent monitoring and maintenance effects, so the intelligent monitoring method, electronic device and computer-readable storage medium of transformer faults proposed by the present invention can solve the problem that the authenticity of different abnormal operating states cannot be verified, and the overall operating status of the transformer can be evaluated to adaptively shorten the maintenance time.

[0118] Embodiment 2:

[0119] like Figure 5 FIG. 1 is a schematic diagram of the structure of an electronic device for implementing an intelligent monitoring method for transformer faults provided by an embodiment of the present invention.

[0120] The electronic device 1 may include a processor 10 , a memory 11 and a bus, and may also include a computer program stored in the memory 11 and executable on the processor 10 , such as a transformer fault intelligent monitoring method program 12 .

[0121] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, such as a mobile 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 memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 may not only be used to store application software and various types of data installed in the electronic device 1, such as the code of a transformer fault intelligent monitoring program 12, etc., but also be used to temporarily store data that has been output or is to be output.

[0122] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits 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, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules (such as an intelligent monitoring program for transformer faults, etc.) stored in the memory 11, and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0123] The bus may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0124] Figure 5 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 5The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0125] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0126] Furthermore, the electronic device 1 may also include a network interface. 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 is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0127] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0128] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0129] The memory 11 in the electronic device 1 stores a transformer fault intelligent monitoring program 12 which is a combination of multiple instructions. When running in the processor 10, the following can be achieved:

[0130] Monitor and collect data from different areas of the transformer during operation to obtain a monitoring data set; the monitoring data set includes temperature data, loudness data, and hydrogen data;

[0131] Extract and define features of each data in the monitoring data set to obtain a monitoring definition set including temperature definition data, loudness definition data and hydrogen definition data;

[0132] Obtain the regional location of the transformer and its corresponding regional weight, and combine the regional weight of the transformer with the monitoring definition set to obtain the corresponding operation evaluation value YP;

[0133] If the minimum value of the operation evaluation range YPmin≤the operation evaluation value YP≤the maximum value of the operation evaluation range YPmax, it is determined that the operation state of the corresponding transformer is slightly abnormal and a first abnormal operation signal is generated, and the corresponding transformer is set as the first target transformer according to the first abnormal operation signal;

[0134] If the operation evaluation value YP> the maximum value YPmax of the operation evaluation range, the operation state of the corresponding transformer is determined to be moderately abnormal and a second operation abnormality signal is generated, and the corresponding transformer is set as a second target transformer according to the second operation abnormality signal;

[0135] Verify the authenticity of the first target transformer and the second target transformer in abnormal state respectively, and issue an early warning for the abnormal state that has passed the verification;

[0136] The verified abnormal data and maintenance data are integrated within the preset evaluation period, and the life prediction prompts are adaptively generated based on the integration results.

[0137] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 5 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0138] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0139] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor of an electronic device.

[0140] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0141] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0142] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0143] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0144] The blockchain referred to in this invention is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. An intelligent monitoring method for transformer faults, characterized in that: The method comprises: Monitor and collect data from different areas of the transformer during operation to obtain a monitoring data set; the monitoring data set includes temperature data, loudness data, and hydrogen data; Extracting and defining features of each data in the monitoring data set to obtain a monitoring definition set including temperature definition data, loudness definition data and hydrogen definition data; Obtain the regional location of the transformer and its corresponding regional weight, and combine the regional weight of the transformer with the monitoring definition set to obtain the corresponding operation evaluation value YP; If the minimum value of the operation evaluation range YPmin≤the operation evaluation value YP≤the maximum value of the operation evaluation range YPmax, it is determined that the operation state of the corresponding transformer is slightly abnormal and a first abnormal operation signal is generated, and the corresponding transformer is set as a first target transformer according to the first abnormal operation signal; If the operation evaluation value YP> the maximum value YPmax of the operation evaluation range, the operation state of the corresponding transformer is determined to be moderately abnormal and a second abnormal operation signal is generated, and the corresponding transformer is set as a second target transformer according to the second abnormal operation signal; Verify the authenticity of the first target transformer and the second target transformer in abnormal state respectively, and issue an early warning for the abnormal state that has passed the verification; Integrate the verified abnormal data and maintenance data within the preset evaluation period, and adaptively generate life prediction prompts based on the integration results. The step of combining the regional weight of the transformer with the monitoring definition set to obtain a corresponding operation evaluation value YP includes: Match the obtained regional location of the transformer with the pre-built regional location table to obtain the corresponding regional weight and define it as QQi; The regional weight of the definition mark is combined with each data of the definition mark in the monitoring definition set, and the operation evaluation value YP corresponding to the transformer is obtained by calculation through the formula; the formula is: YP=QQi×[a1×(SWi-SWi0)+a2×(SXi-SXi0)+a3×(SQi-SQi0)] Wherein, a1, a2, a3 are different proportional coefficients all greater than zero, SWi0 is a preset standard temperature, SXi0 is a preset standard loudness, and SQi0 is a preset standard hydrogen concentration.

2. The intelligent monitoring method for transformer fault according to claim 1, characterized in that: The feature extraction and definition of each data in the monitoring data set includes: Acquire temperature data, loudness data and hydrogen data in the monitoring data set; Respectively extracting the values ​​of the real-time temperature, real-time loudness and real-time hydrogen concentration from the temperature data, loudness data and hydrogen data and defining labels; Numbering and marking the areas corresponding to the collected real-time temperature, real-time loudness and real-time hydrogen concentration respectively; The real-time temperature, real-time loudness and real-time hydrogen concentration of the definition mark and their corresponding areas are arranged and combined in chronological order to obtain a monitoring definition set including temperature definition data, loudness definition data and hydrogen definition data.

3. The intelligent monitoring method for transformer fault according to claim 1, characterized in that: The verifying the authenticity of the first target transformer and the second target transformer in abnormal state respectively includes: Verifying the operation of the first target transformer and the second target transformer respectively according to the first abnormal operation signal and the second abnormal operation signal; Counting the durations of the first motion abnormality signal and the second motion abnormality signal in the preset first verification period and the second verification period, and marking the total durations of the first motion abnormality signal and the second motion abnormality signal as the first motion abnormality duration and the second motion abnormality duration, respectively; The values ​​of the first abnormal operation duration and the second abnormal operation duration are extracted respectively and marked as YSk, k=1, 2; the verification value HY of the first abnormal operation signal and the second abnormal operation signal is obtained by the formula HY=YSk / HSk; where HSk is the total duration corresponding to the first verification period and the second verification period; The verification value corresponding to the first abnormal operation signal and the verification value corresponding to the second abnormal operation signal are respectively set as the first verification value and the second verification value and evaluated; If the first verification value is greater than the first verification threshold and the second verification threshold is greater than the second verification threshold, it is determined that the verification of the first operation abnormality signal and the second operation abnormality signal is passed, and an early warning is issued for the operation of the first transformer corresponding to the first operation abnormality signal and the second transformer corresponding to the second operation abnormality signal.

4. The intelligent monitoring method for transformer fault according to claim 1, characterized in that: The aforementioned warning prompts for abnormal states that have passed verification include: Obtain the integer part of the operation evaluation value corresponding to the first transformer and the second transformer that have passed the verification and set it as the warning value, match the warning value with the pre-built warning table to obtain the corresponding warning range and its corresponding warning content, and prompt specific abnormalities according to the corresponding warning content; And, the distribution position of the temperature anomaly in the warning content is analyzed and located.

5. The intelligent monitoring method for transformer fault according to claim 4, characterized in that: The analyzing and locating the distribution position of the temperature anomaly in the warning content includes: Generate a positioning instruction according to the temperature anomaly in the warning content, and control the infrared thermal imager to work and obtain an infrared thermal imaging image inside the transformer according to the positioning instruction; Modular processing is performed on the infrared thermal imaging image to obtain an image partition set including a plurality of monitoring areas; Obtaining pixel values ​​of all pixel points on a plurality of monitoring areas in the image partition set, and summing the pixel values ​​of all pixel points to obtain a pixel sum; Arrange a number of the pixels in descending order, set the first p pixels and the corresponding monitoring area as the selected area, and generate a temperature abnormality prompt according to the position of the selected area; p is a positive integer.

6. The intelligent monitoring method for transformer fault according to claim 5, characterized in that: The modular processing of the infrared thermal imaging image comprises: Obtaining the midpoint of the infrared thermal imaging image and setting it as the origin, and establishing a two-dimensional coordinate system according to preset coordinate axis directions and coordinate distances; Acquire the areas of the four coordinate regions on the two-dimensional coordinate system, reduce the areas of the coordinate regions according to a preset reduction ratio, and set the reduced coordinate regions as monitoring area image division sets; A plurality of monitoring areas and their corresponding area coordinates constitute an image partition set.

7. The intelligent monitoring method for transformer fault according to claim 1, characterized in that: The integration of the verified abnormal data and maintenance data within the preset evaluation period includes: Obtain the abnormal type of abnormal data within a preset evaluation period and the number of maintenance times and maintenance duration in the maintenance data; match the acquired abnormal type with the pre-built abnormal type weight table to obtain the corresponding abnormal weight and mark it; respectively extract the values ​​of the number of maintenance times and maintenance duration and mark them; The abnormal weight of the value mark is combined with the number of maintenance times and the maintenance duration to obtain the maintenance evaluation value of the transformer; the maintenance evaluation value is analyzed and evaluated to obtain the overall operating status of the transformer, and its operating life is adaptively predicted and prompted, as well as the maintenance duration of the transformer is adaptively shortened.

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