Fault detection system for power distribution equipment

Abstract

The invention discloses a fault detection system for power distribution equipment, and relates to the technical field of fault detection, and the system comprises a power distribution equipment prior data acquisition module, a power distribution equipment fault item evaluation module, an unmanned aerial vehicle building module, a fault detection execution terminal and a database. And the pre-estimated fault item set of the transformer in the region is determined, the fault of the high-altitude transformer is predicted, and data support is provided for follow-up targeted inspection of the transformer in the region. Based on regional transformer group tags, each detection route in the region is generated, blindness of transformer inspection is avoided, the fault detection efficiency of subsequent transformers is improved, the frequency of power grid faults is reduced, convenience of high-altitude transformer inspection by the unmanned aerial vehicle is improved, redundancy of sensors carried by the unmanned aerial vehicle is also avoided, and the reliability of transformer inspection is improved. Operation load of the unmanned aerial vehicle is reduced, and use efficiency and service life of the unmanned aerial vehicle are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of fault detection, and in particular to a fault detection system for power distribution equipment. Background Art

[0002] As an indispensable energy source for promoting industrial development and daily life, the stable supply of electricity is of vital importance. Transformers are an important link between power stations and users, and undertake the important tasks of voltage conversion and power transmission. Their stability is directly related to the safety of the entire power grid. If the transformer failure is not discovered and handled in time, it may cause a chain reaction and trigger a larger-scale power grid failure. Therefore, fault detection of transformers can effectively prevent potential safety hazards and ensure the stable power supply of the power grid. Especially for transformers located in the sky, fault detection is particularly important due to their special installation location and environmental factors.

[0003] Prior art, such as the invention application patent with announcement number: CN110048507B, discloses an automatic inspection method and system for a distribution automation system, including an automatic detection terminal, a communication link and an automatic inspection main control unit. The automatic detection terminal is connected to the distribution automation terminal to realize data collection of the primary equipment of the distribution network and the collection of control execution action signals. The automatic inspection main control unit is respectively connected to the automatic detection terminal and the distribution automation master station through a communication link. By setting an automatic detection terminal on each distribution automation terminal device and transmitting it to the automatic inspection main control unit, the data is comprehensively analyzed with the data collected from the distribution automation master station to generate key operating indicator data that affects the safe operation of the distribution automation system, and a fault alarm signal is generated based on the indicator data to achieve accurate positioning and early repair of faults in the distribution automation system, thereby improving the safety and stability of the operation of the distribution automation system.

[0004] Regarding the above technical solutions, it is found that most of them currently directly perform fault detection on the equipment to be tested, and there is rarely any prior data such as environmental data for high-altitude transformers to determine the fault items of high-altitude transformers. Due to the particularity of high-altitude transformers, they are easily affected by environmental factors. The prior art ignores this aspect, making it difficult to predict the faults of high-altitude transformers, and thus difficult to conduct targeted inspections of transformers, which increases the blindness of transformer inspections, reduces the efficiency of subsequent transformer fault detection, and increases the frequency of power grid failures. At the same time, there is little attention paid to the inspection routes of automatic inspection devices and the selection of sensors carried, which reduces the convenience of drone inspections of high-altitude transformers. It also leads to redundancy of sensors carried by drones, increases the operating load of drones, and reduces the use efficiency and service life of drones. Summary of the invention

[0005] The purpose of the present invention is to provide a fault detection system for power distribution equipment, which solves the problems existing in the background technology.

[0006] To solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a fault detection system for power distribution equipment, including: a power distribution equipment prior data acquisition module, used to obtain prior data of each transformer in the area, wherein the prior data includes environmental data and power data.

[0007] The distribution equipment fault item evaluation module is used to determine the estimated fault item set of each transformer in the region based on the prior data of each transformer in the region and generate regional transformer group labels.

[0008] The drone building module is used to generate various detection routes in the area according to the regional transformer group labels, and allocate the sensors carried by the drone to each detection route in the area.

[0009] The fault detection execution terminal is used to perform fault detection on several transformers on each detection route in the area through drones on each detection route in the area.

[0010] The beneficial effects of the present invention are: (1) The present invention obtains the prior data of transformers in the area in the power distribution equipment prior data acquisition module, thereby laying a foundation for the subsequent power distribution equipment fault project evaluation.

[0011] (2) In the distribution equipment fault item evaluation module, the present invention determines the estimated fault item set of the transformers in the area based on the prior data of the transformers in the area, predicts the fault of the high-altitude transformer, and provides data support for the subsequent targeted inspection of the transformers in the area.

[0012] (3) In the drone building module of the present invention, based on the regional transformer group label, various detection routes in the region are generated, and the transformers are inspected in a targeted manner to avoid the blindness of transformer inspections, improve the efficiency of subsequent transformer fault detection, and reduce the frequency of power grid failures. The sensors carried by the drones are allocated to each detection route in the region, thereby improving the convenience of drone inspections of high-altitude transformers. At the same time, the redundancy of drone-mounted sensors is avoided, the operating load of the drone is reduced, and the use efficiency and service life of the drone are guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 It is a schematic diagram of the system structure connection of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] Reference Figure 1 As shown, the present invention provides a fault detection system for power distribution equipment, including: a power distribution equipment prior data acquisition module, a power distribution equipment fault project evaluation module, a drone building module, and a fault detection execution terminal.

[0017] It should be noted that the present invention also includes a database for storing heat dissipation difficulty values ​​corresponding to each altitude interval, safe air pressure values, allowable air pressure fluctuation values, safe wind speeds, wind direction and wind speed change thresholds, safe day and night temperature differences, safe minimum temperatures, allowable number of biological types, allowable biological activity and transformer volume, safe electrical data, transformer fault data, the altitude of each transformer, and each onboard sensor corresponding to each fault item.

[0018] It should also be noted that the distribution equipment prior data acquisition module is connected to the distribution equipment fault project assessment module, the distribution equipment fault project assessment module is connected to the drone building module, the drone building module is connected to the fault detection execution terminal, and the database is respectively connected to the distribution equipment fault project assessment module and the drone building module.

[0019] The power distribution equipment priori data acquisition module is used to acquire priori data of each transformer in the area, wherein the priori data includes environmental data and power data.

[0020] In a specific embodiment of the present invention, the environmental data includes altitude, air pressure data, temperature data, wind load data, and biological activity data.

[0021] It should be noted that the fault detection system of the power distribution equipment is connected to the meteorological center, and the meteorological center includes but is not limited to monitoring the altitude, air pressure data, temperature data, wind load data, and biological activity data.

[0022] The power data includes electrical data and mechanical parameters.

[0023] It should be noted that the power data is obtained from the power management center.

[0024] The present invention obtains prior data of transformers in a region in a power distribution equipment prior data acquisition module, thereby laying a foundation for subsequent power distribution equipment fault project evaluation.

[0025] The distribution equipment fault item evaluation module is used to determine the estimated fault item set of each transformer in the region based on the prior data of each transformer in the region, and generate a regional transformer group label.

[0026] In a specific embodiment of the present invention, the set of estimated fault items of each transformer in the region is determined based on the prior data of each transformer in the region, and the specific determination method is: extracting environmental data from the prior data of each transformer in the region, and determining each first estimated fault item of each transformer in the region.

[0027] Power data is extracted from prior data of each transformer in the area, and each second estimated fault item of each transformer in the area is determined.

[0028] According to each first predicted fault item and each second predicted fault item of each transformer in the region, a set of predicted fault items of each transformer in the region is obtained by summarizing.

[0029] In a specific embodiment of the present invention, the first estimated fault items of each transformer in the determined area are specifically determined by: extracting the altitude from the environmental data of each transformer in the area, and combining the heat dissipation difficulty values ​​corresponding to each altitude interval stored in the database, screening out the heat dissipation difficulty values ​​of each transformer in the area, and comparing them with the preset heat dissipation difficulty threshold value; if the heat dissipation difficulty value of a transformer is greater than or equal to the heat dissipation difficulty threshold value, the internal heat dissipation fault item is used as the first estimated fault item of the transformer.

[0030] It should be understood that as the altitude increases, the air density decreases, the heat dissipation conditions become worse, and the heat generated inside the transformer is difficult to dissipate. Therefore, it is necessary to analyze the altitude of the transformer.

[0031] It should also be noted that the specific analysis method of the heat dissipation difficulty value corresponding to each altitude interval is as follows: extracting transformer fault data from a database, wherein the transformer fault data includes each fault item corresponding to each transformer, screening each transformer whose fault item is internal heat dissipation, and recording it as each internal heat dissipation fault transformer, and obtaining the altitude of each internal heat dissipation fault transformer from the database, mapping to obtain each internal heat dissipation fault transformer corresponding to each altitude, and then dividing each altitude according to a preset interval to obtain each altitude interval, and obtaining the number of internal heat dissipation fault transformers in each altitude interval, counting the total number of internal heat dissipation fault transformers, and dividing the number of internal heat dissipation fault transformers in each altitude interval by the total number of internal heat dissipation fault transformers to obtain the heat dissipation difficulty value corresponding to each altitude interval.

[0032] Extract air pressure data from the environmental data of each transformer in the area, where the air pressure data includes the air pressure value at each detection time point, and import it into the air pressure compliance value assessment model , the gas pressure of each transformer in the output area meets the value , where the air pressure compliance value includes 1 and -1. When the air pressure compliance value is 1, it indicates that the air pressure of the transformer meets the requirements and there is no need to perform oil quality testing on the transformer. When the air pressure compliance value is -1, it indicates that the air pressure of the transformer does not meet the requirements and it is necessary to perform oil quality testing on the transformer. The oil quality fault item is taken as the first estimated fault item of the transformer.

[0033] In the formula , They are represented as existential quantifiers and universal quantifiers respectively. , Represent the logical symbols and, or, is the number of detection time points, , are the air pressure value at the i-th detection time point and the air pressure value at the i+1-th detection time point of the j-th transformer, respectively. , The safe air pressure value and the allowable air pressure fluctuation value stored in the database. is the number of each transformer, , is any integer greater than 2, is the number of each detection time point, , is any integer greater than 2.

[0034] It needs to be explained that , which means that the air pressure value at any detection time point of the j-th transformer is greater than the safe air pressure value. , which means that the air pressure value of the j-th transformer at a certain detection time point is less than or equal to the safe air pressure value.

[0035] It should be noted that when the air pressure around the transformer does not meet the requirements, such as low air pressure or poor air pressure stability, the dissolved gas in the insulating gas, such as transformer oil, is more likely to form bubbles, thereby reducing the insulation strength. Therefore, it is necessary to analyze the air pressure around the transformer.

[0036] Temperature data is extracted from the environmental data of each transformer in the area, where the temperature data includes the temperature difference between day and night and the lowest temperature. It is used to determine whether hardware component testing and oil quality testing are required for each transformer in the area, and to determine the temperature estimated fault items of each transformer in the area.

[0037] Extract wind load data from the environmental data of each transformer in the area, where the wind load data includes the wind speed of each wind direction at each detection time point, and import it into the wind direction and wind speed anomaly evaluation model , output the abnormal wind direction and wind speed values ​​of each transformer in the area , where the wind direction and wind speed abnormal values ​​include 1 and -1. When the wind direction and wind speed abnormal value is -1, it indicates that the wind direction and wind speed of the transformer meet the requirements and there is no need to perform displacement detection on the transformer. When the wind direction and wind speed abnormal value is 1, it indicates that the wind direction and wind speed of the transformer do not meet the requirements and there is a need to perform displacement detection on the transformer. The displacement fault item is used as the first estimated fault item of the transformer.

[0038] In the formula , are the wind speed of the mth wind direction of the jth transformer at the i-th detection time point, and the wind speed of the m+1th wind direction at the i-th detection time point, respectively. is the number of wind directions, , The safe wind speed, wind direction and wind speed change thresholds stored in the database. is the number of each wind direction, , is any integer greater than 2.

[0039] Biological activity data is extracted from the environmental data of each transformer in the area, where the biological activity data includes the number of biological types, the amount of biological activity and the volume of biological activity, and then the intrusion risk value of each transformer in the area is evaluated. When the intrusion risk value of a transformer in the area is greater than or equal to the preset intrusion risk value, the insulator detection item is used as the first estimated fault item of the transformer.

[0040] It should be noted that the biological types include birds, mice and snakes, etc., the biological activity amount specifically refers to the duration of biological activities, and the biological activity volume specifically refers to the volume of the overlapping area between the biological activity area and the transformer.

[0041] The first estimated fault items of each transformer in the area are summarized and obtained.

[0042] In a specific embodiment of the present invention, it is determined whether each transformer in the area needs to be tested for hardware components and oil quality, and the temperature estimated fault items of each transformer in the area are determined. The specific determination method is: the day and night temperature difference of each transformer in the area , minimum temperature Import into the temperature anomaly assessment model In the output, the temperature abnormality value of each transformer in the area is output, where the temperature abnormality value includes the values ​​of 1, 01, 00, and -1. When the temperature abnormality value is -1, it indicates that the temperature of the transformer meets the requirements, and there is no need to perform hardware component detection and insulation strength detection on the transformer. When the temperature abnormality value is 01, it indicates that the day and night temperature difference of the transformer does not meet the requirements, and the transformer needs to perform hardware component detection, and the hardware component failure item is used as the first estimated failure item of the transformer. When the temperature abnormality value is 00, it indicates that the minimum temperature of the transformer does not meet the requirements, and the transformer needs to perform oil quality detection, and the oil quality failure item is used as the first estimated failure item of the transformer. When the temperature abnormality value is 1, it indicates that both the day and night temperature difference and the minimum temperature of the transformer do not meet the requirements, and the transformer needs to perform hardware component detection and oil quality detection, and both the hardware component failure item and the oil quality failure item are used as the first estimated failure item of the transformer.

[0043] In the formula , They represent the safe day and night temperature difference and safe minimum temperature stored in the database respectively.

[0044] It should be noted that the temperature difference between day and night is usually greater in the sky than on the ground. Such drastic temperature changes will cause the transformer casing, windings and other parts to expand and contract. Repeated expansion and contraction may cause problems such as loose connections. Low temperatures will increase the viscosity of transformer oil and reduce its fluidity, affecting the heat dissipation effect. At the same time, low temperatures may also make the insulation material brittle and reduce its mechanical strength, so it is necessary to analyze the temperature of the transformer.

[0045] In a specific embodiment of the present invention, the intrusion risk value of each transformer in the assessment area is calculated by the following specific formula: ,in , , are the number of biological types, biological activity and biological activity volume of the jth transformer in the region, , , They are the number of allowed biological types, allowed biological activities and transformer volume stored in the database respectively.

[0046] In a specific embodiment of the present invention, the second estimated fault items of each transformer in the region are determined by extracting electrical data and mechanical parameters from the power data of each transformer in the region, evaluating the current, voltage and transport quality values ​​of each transformer in the region, , mechanical quality value, where is the electrical data of the jth transformer, Safety electrical data stored in the database.

[0047] It should be noted that the electrical data includes the current mean, voltage mean, current difference, and voltage difference; the safety electrical data includes the safety current mean, safety voltage mean, safety current difference, and safety voltage difference.

[0048] It should also be noted that the mechanical parameters include vibration amplitude and vibration frequency, and the analysis method of the mechanical quality value of each transformer in the region is consistent with the analysis method of the current and voltage mechanical quality values.

[0049] When the current and voltage transport quality value of a transformer in the area is 1, it indicates that the transformer line does not need to be detected. When the current and voltage transport quality value of a transformer in the area is -1, it indicates that the transformer line needs to be detected. In this case, the line fault detection item is recorded as the second estimated fault item of the transformer.

[0050] Similarly, whether each transformer in the area needs to be displaced is determined based on the mechanical quality value of each transformer in the area. If a transformer needs to be displaced, the displacement fault item is used as the second estimated fault item of the transformer.

[0051] The second estimated fault items of each transformer in the area are summarized and obtained.

[0052] In a specific embodiment of the present invention, the regional transformer group label is generated by a specific generation method: based on the estimated fault item set of each transformer in the region, each estimated fault item of each transformer in the region is extracted, and they are compared with each other. If the estimated fault items of a transformer are the same as the estimated fault items of another transformer, the transformer and the other transformer are grouped together, and then the groups of transformers and their corresponding estimated fault item sets are statistically obtained.

[0053] The number of elements in the estimated fault item set of each group of transformers is obtained, and regional transformer group labels are generated accordingly.

[0054] In a specific embodiment, if the number of elements in the predicted fault item set of a group of transformers is 1, then the group of transformers is recorded as a transformer belonging group within a type I area; if the number of elements in the predicted fault item set of a group of transformers is 2, then the group of transformers is recorded as a transformer belonging group within a type II area.

[0055] In the distribution equipment fault item evaluation module, the present invention determines the estimated fault item set of transformers in the area based on the prior data of transformers in the area, predicts the fault of high-altitude transformers, and provides data support for subsequent targeted inspections of transformers in the area.

[0056] The drone building module is used to generate various detection routes in the area according to the regional transformer group label, and allocate various sensors carried by the drone to each detection route in the area.

[0057] In a specific embodiment of the present invention, the detection routes in the region are generated according to the regional transformer group label, and the specific method is: extract each group of transformers in the transformer group in each type of region from the regional transformer group label, obtain each group of transformers in the transformer group in the specified type of region, and compare the estimated fault item set of each group of transformers in the transformer group in the specified type of region with the estimated fault item set of each group of transformers in the transformer groups in the remaining types of regions; if the estimated fault item set of a certain group of transformers in the transformer group in the remaining type of region is included in the estimated fault item set of a certain group of transformers in the transformer group in the specified type of region, the two groups of transformers are used as designated links, and the estimated fault item set of the designated link is obtained.

[0058] It should be noted that the transformer group within the designated area is specifically the transformer group within the highest level area.

[0059] It should also be noted that the level of the transformer group in the first category area is lower than that in the second category area, the level of the transformer group in the second category area is lower than that in the third category area, and so on, to obtain the transformer group in the highest level area.

[0060] The estimated fault item set of each group of transformers in the transformer group in each type of area is compared with the estimated fault item set of the designated link. If the estimated fault item set of a group of transformers in the transformer group in a certain type of area is included in the estimated fault item set of the designated link, then the group of transformers is included in the designated link.

[0061] In this way, each designated link is obtained and used as each detection route in the area.

[0062] In a specific embodiment of the present invention, the onboard sensors of the drones are allocated to the detection routes in the region, and the specific method is: obtain the regional transformer group labels of the transformer groups in the detection routes in the region, obtain a group of transformers corresponding to the highest-level regional transformer group label in a detection route in the region, obtain each estimated fault item in the estimated fault item set of the group of transformers, and combine the onboard sensors corresponding to the fault items stored in the database to screen out the onboard sensors of the group of transformers, use them as the onboard sensors of the drones of the detection route, and obtain the onboard sensors of the drones of the detection routes in the region.

[0063] The present invention generates various detection routes in the area based on the regional transformer group label in the drone building module, conducts targeted inspections on the transformers, avoids the blindness of transformer inspections, improves the efficiency of subsequent transformer fault detection, reduces the frequency of power grid faults, and allocates sensors carried by drones to various detection routes in the area, thereby improving the convenience of drone inspections of high-altitude transformers. At the same time, it also avoids the redundancy of sensors carried by drones, reduces the operating load of drones, and ensures the use efficiency and service life of drones.

[0064] The fault detection execution terminal is used to perform fault detection on a number of transformers on each detection route in the area through drones on each detection route in the area.

[0065] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A fault detection system for power distribution equipment, characterized in that: include: A power distribution equipment prior data acquisition module is used to acquire prior data of each transformer in the area, wherein the prior data includes environmental data and power data; The distribution equipment fault item evaluation module is used to determine the estimated fault item set of each transformer in the region based on the prior data of each transformer in the region and generate the regional transformer group label; The drone building module is used to generate various detection routes in the region according to the regional transformer group labels, and allocate the sensors carried by the drone to each detection route in the region; The fault detection execution terminal is used to perform fault detection on several transformers on each detection route in the area through drones on each detection route in the area.

2. A fault detection system for power distribution equipment according to claim 1, characterized in that: The environmental data include altitude, air pressure data, temperature data, wind load data, and biological activity data; The power data includes electrical data and mechanical parameters.

3. A fault detection system for power distribution equipment according to claim 2, characterized in that: The method for determining the estimated fault item set of each transformer in the region based on the prior data of each transformer in the region is as follows: Extracting environmental data from prior data of each transformer in the area, and determining each first estimated fault item of each transformer in the area; Extracting power data from prior data of each transformer in the area, and determining each second estimated fault item of each transformer in the area; According to each first predicted fault item and each second predicted fault item of each transformer in the region, a set of predicted fault items of each transformer in the region is obtained by summarizing.

4. A fault detection system for power distribution equipment according to claim 3, characterized in that: The specific determination method of each first estimated fault item of each transformer in the determination area is as follows: Extract the altitude from the environmental data of each transformer in the area, and combine the heat dissipation difficulty values ​​corresponding to each altitude interval stored in the database to screen out the heat dissipation difficulty values ​​of each transformer in the area, and compare them with the preset heat dissipation difficulty threshold. If the heat dissipation difficulty value of a transformer is greater than or equal to the heat dissipation difficulty threshold, the internal heat dissipation fault item is used as the first estimated fault item of the transformer; Extract air pressure data from the environmental data of each transformer in the area, where the air pressure data includes the air pressure value at each detection time point, and import it into the air pressure compliance value assessment model , the gas pressure of each transformer in the output area meets the value , where the air pressure compliance value includes 1 and -1. When the air pressure compliance value is 1, it indicates that the air pressure of the transformer meets the requirements and the transformer does not need to be tested for oil quality. When the air pressure compliance value is -1, it indicates that the air pressure of the transformer does not meet the requirements and the transformer needs to be tested for oil quality. The oil quality fault item is used as the first estimated fault item of the transformer. In the formula , They are represented as existential quantifiers and universal quantifiers respectively. , Represent the logical symbols and, or, is the number of detection time points, , are the air pressure value at the i-th detection time point and the air pressure value at the i+1-th detection time point of the j-th transformer, respectively. , The safe air pressure value and the allowable air pressure fluctuation value stored in the database. is the number of each transformer, , is any integer greater than 2, is the number of each detection time point, , is any integer greater than 2; Extract temperature data from the environmental data of each transformer in the area, including the temperature difference between day and night and the lowest temperature, to determine whether each transformer in the area needs to undergo hardware component testing and oil quality testing, and determine the temperature-estimated fault items of each transformer in the area; Extract wind load data from the environmental data of each transformer in the area, where the wind load data includes the wind speed of each wind direction at each detection time point, and import it into the wind direction and wind speed anomaly evaluation model , output the abnormal wind direction and wind speed values ​​of each transformer in the area , so as to determine whether the displacement fault item is used as the first estimated fault item of the transformer; In the formula , are the wind speed of the mth wind direction of the jth transformer at the i-th detection time point, and the wind speed of the m+1th wind direction at the i-th detection time point, respectively. is the number of wind directions, , The safe wind speed, wind direction and wind speed change thresholds stored in the database. is the number of each wind direction, , is any integer greater than 2; Extract biological activity data from the environmental data of each transformer in the area, where the biological activity data includes the number of biological types, the amount of biological activity and the volume of biological activity, and then evaluate the intrusion risk value of each transformer in the area. When the intrusion risk value of a transformer in the area is greater than or equal to the preset intrusion risk value, the insulator detection item is used as the first estimated fault item of the transformer; The first estimated fault items of each transformer in the area are summarized and obtained.

5. A fault detection system for power distribution equipment according to claim 4, characterized in that: The method for determining whether each transformer in the area needs to undergo hardware component testing and oil quality testing and determining each temperature estimated fault item of each transformer in the area is as follows: The day and night temperature difference of each transformer in the area , minimum temperature Import into the temperature anomaly assessment model , outputting the temperature abnormality values ​​of each transformer in the area, wherein the temperature abnormality values ​​include values ​​of 1, 01, 00, and -1, so as to determine whether both the hardware component fault item and the oil quality fault item are taken as the first estimated fault item of the transformer; In the formula , They represent the safe day and night temperature difference and safe minimum temperature stored in the database respectively.

6. A fault detection system for power distribution equipment according to claim 4, characterized in that: The specific calculation formula for the intrusion risk value of each transformer in the assessment area is: ,in , , are the number of biological types, biological activity and biological activity volume of the jth transformer in the region, , , They are the number of allowed biological types, allowed biological activities and transformer volume stored in the database respectively.

7. A fault detection system for power distribution equipment according to claim 2, characterized in that: The specific determination method of each second estimated fault item of each transformer in the determination area is as follows: Extract electrical data and mechanical parameters from the power data of each transformer in the area, and evaluate the current, voltage and transport quality values ​​of each transformer in the area. , mechanical quality value, where is the electrical data of the jth transformer, For safety electrical data stored in the database; When the current and voltage transport quality value of a transformer in the area is 1, it indicates that the transformer line does not need to be detected. When the current and voltage transport quality value of a transformer in the area is -1, it indicates that the transformer line needs to be detected, and the line fault detection item is recorded as the second estimated fault item of the transformer; Similarly, whether each transformer in the area needs to be detected for displacement is determined based on the mechanical quality value of each transformer in the area. If a transformer needs to be detected for displacement, the displacement fault item is used as the second estimated fault item of the transformer. The second estimated fault items of each transformer in the area are summarized and obtained.

8. A fault detection system for power distribution equipment according to claim 3, characterized in that: The specific method for generating the regional transformer group label is as follows: Based on the estimated fault item set of each transformer in the region, the estimated fault items of each transformer in the region are extracted, and compared with each other, and statistics are obtained for each group of transformers and their corresponding estimated fault item sets; The number of elements in the estimated fault item set of each group of transformers is obtained, and regional transformer group labels are generated accordingly.

9. A fault detection system for power distribution equipment according to claim 8, characterized in that: The specific method of generating each detection route in the region according to the regional transformer group label is as follows: Extract each group of transformers in the transformer group in each type of area from the regional transformer group label, obtain each group of transformers in the transformer group in the specified type of area, and compare the estimated fault item set of each group of transformers in the transformer group in the specified type of area with the estimated fault item set of each group of transformers in the transformer group in the remaining types of areas; if the estimated fault item set of a certain group of transformers in the transformer group in the remaining type of area is included in the estimated fault item set of a certain group of transformers in the transformer group in the specified type of area, then take the two groups of transformers as the designated link, and obtain the estimated fault item set of the designated link; Combine the estimated fault item sets of each group of transformers in the transformer group in each type of area with the estimated fault item set of the designated link. If the estimated fault item set of a certain group of transformers in the transformer group in a certain type of area is included in the estimated fault item set of the designated link, then the group of transformers is included in the designated link. In this way, each designated link is obtained and used as each detection route in the area.

10. A fault detection system for power distribution equipment according to claim 9, characterized in that: The specific method for allocating the onboard sensors of the drones to the detection routes in the region is as follows: obtaining the regional transformer group labels of the transformer groups in the detection routes in the region, obtaining a group of transformers corresponding to the highest-level regional transformer group label in a detection route in the region, obtaining each estimated fault item in the estimated fault item set of the group of transformers, and screening the onboard sensors of the group of transformers in combination with the onboard sensors corresponding to the fault items stored in the database, using them as the onboard sensors of the drones of the detection route, and obtaining the onboard sensors of the drones of the detection routes in the region.

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