Power distribution fault diagnosis method and system based on Beidou positioning, and medium

Abstract

The invention discloses a power distribution fault diagnosis method and system based on Beidou positioning, and a medium, mainly relates to the technical field of power distribution fault diagnosis, and is used for solving the problems that an existing power distribution fault diagnosis method is time-consuming and labor-consuming, cannot deal with sudden fault conditions, and is long in algorithm model processing program. Comprising the following steps: determining a corresponding detection sub-line of each unmanned aerial vehicle in a power distribution fault detection line; when the unmanned aerial vehicle receives the acquisition signal, temperature values at the current detection points are acquired through temperature sensors on the unmanned aerial vehicle in sequence based on the trend of the detection sub-lines, and when the temperature values exceed a preset temperature threshold value, a fault signal is uploaded to a central processing unit; when the fault does not exceed the preset temperature threshold value, collecting current data at the current detection point through a current transformer on the unmanned aerial vehicle; and when the relationship between the current data of the current detection point and the current data of the previous current detection point meets a preset fault relationship, a fault signal is uploaded to the central processing unit.

Description

Technical Field

[0001] The present application relates to the technical field of power distribution fault diagnosis, and in particular to a power distribution fault diagnosis method, system and medium based on Beidou positioning. Background Art

[0002] Distribution fault diagnosis refers to the process of analyzing and judging the faults in the distribution system, finding out the causes of the faults and taking corresponding repair measures. The purpose of distribution fault diagnosis is to ensure the safe operation of the power system, reduce power outage time and improve power supply reliability.

[0003] In the field of power distribution fault diagnosis, commonly used methods include the following: (1) Traditional manual inspection method: Through manual observation of the power distribution system fault site, such as whether the equipment is smoking or making abnormal sounds, the fault type and location can be preliminarily determined. (2) Model-based fault diagnosis method: Obtain a number of sample images, upload the images to the model, train the model, and then analyze and study the images collected on site in a laboratory environment through the trained model to find out the cause of the fault and the solution.

[0004] However, the above (1) traditional manual inspection method requires manual inspection one by one, which is time-consuming, labor-intensive, error-prone, and cannot be monitored in real time. (2) Model-based fault diagnosis method: The model diagnosis process involves data collection, data cleaning, data input, algorithm operation, data output and other procedures, involving multiple modules and equipment, and the processing procedure is long and time-consuming. In addition, using model algorithms to train a large amount of historical data to achieve automatic diagnosis of faults requires a large amount of labeled data and computing resources, and the model needs to be retrained for new fault types. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present application provides a power distribution fault diagnosis method, system and medium based on Beidou positioning to solve the problems that the existing power distribution fault diagnosis method is time-consuming and labor-intensive, cannot cope with sudden fault conditions, and has a long algorithm model processing procedure.

[0006] In the first aspect, the present application provides a distribution fault diagnosis method based on Beidou positioning, the method comprising: obtaining a distribution fault detection circuit and the number of drones to determine the detection sub-circuit corresponding to each drone in the distribution fault detection circuit; wherein, a Beidou positioning module, a current transformer, a temperature sensor, and a computing chip are provided in the drone, and the detection sub-circuit is preset with a current detection point; when the drone receives the acquisition signal, based on the direction of the detection sub-circuit, the temperature value at the current detection point is collected in sequence through the temperature sensor on the drone, and when the temperature value exceeds the preset temperature threshold, the fault signal is uploaded to the central processing unit; when the fault does not exceed the preset temperature threshold, the current data at the current detection point is collected through the current transformer on the drone; when the relationship between the current data at the current current detection point and the previous current detection point meets the preset fault relationship, the fault signal is uploaded to the central processing unit; wherein, the current current detection point does not include the initial current detection point, and the fault signal includes Beidou positioning information.

[0007] Furthermore, before determining the detection sub-circuit corresponding to each UAV in the power distribution fault detection circuit, the method further includes: obtaining position data of the current detection point on the detection sub-circuit through a preset interface.

[0008] Furthermore, the UAV includes a mechanical arm and a preset control program corresponding to the mechanical arm, and the current transformer includes a plug-type mutual inductance coil that can be automatically plugged in and out; the current data at the current detection point is collected through the current transformer on the UAV, specifically including: at the current detection point, through the preset control program, controlling the mechanical arm to insert the plug-type mutual inductance coil around the distribution line into the current transformer socket to collect the current data at the current detection point; when it is determined that the fluctuation value of the collected current data within the preset collection time period is less than the preset fluctuation threshold, one end of the plug-type mutual inductance coil is automatically popped out from the current transformer socket.

[0009] Furthermore, the preset fault relationship includes at least: between the current data at the current detection point and the previous current detection point, only one is 0; or the difference between the current data at the current detection point and the previous current detection point is greater than a preset difference threshold.

[0010] Furthermore, after uploading the fault signal to the central processor, the method also includes: obtaining Beidou positioning information in the fault signal and establishing a fault maintenance task; wherein the fault maintenance task includes Beidou positioning information; obtaining the positioning information of each maintenance terminal to send the fault maintenance task to the maintenance terminal closest to the Beidou positioning information.

[0011] In the second aspect, the present application provides a power distribution fault diagnosis system based on Beidou positioning, and the system includes: a route determination module, which is used to obtain the power distribution fault detection line and the number of drones to determine the detection sub-line corresponding to each drone in the power distribution fault detection line; wherein, the drone is provided with a Beidou positioning module, a current transformer, a temperature sensor, and a computing chip, and the detection sub-line is preset with a current detection point; a fault detection module, which is used to collect the temperature value at the current detection point through the temperature sensor on the drone in sequence based on the direction of the detection sub-line when the drone receives the collection signal, and upload the fault signal to the central processing unit when the temperature value exceeds the preset temperature threshold; when the fault does not exceed the preset temperature threshold, the current data at the current detection point is collected through the current transformer on the drone; when the relationship between the current data at the current detection point and the previous current detection point meets the preset fault relationship, the fault signal is uploaded to the central processing unit; wherein, the current current detection point does not include the initial current detection point, and the fault signal includes Beidou positioning information.

[0012] Furthermore, the route determination module includes a current detection point determination unit, which is used to obtain position data of current detection points on the detection sub-circuit through a preset interface.

[0013] Furthermore, the UAV includes a mechanical arm and a preset control program corresponding to the mechanical arm, and the current transformer includes a plug-type mutual inductance coil that can be automatically plugged in and out; the fault detection module includes a current transformer control unit, which is used to control the mechanical arm to insert the plug-type mutual inductance coil around the distribution line and into the current transformer socket through a preset control program at the current detection point to collect current data at the current detection point; when it is determined that the fluctuation value of the collected current data within a preset collection time period is less than a preset fluctuation threshold, one end of the plug-type mutual inductance coil is automatically popped out from the current transformer socket.

[0014] Furthermore, the system also includes a maintenance module for obtaining Beidou positioning information in the fault signal and establishing a fault maintenance task; wherein the fault maintenance task includes Beidou positioning information; obtaining the positioning information of each maintenance terminal to send the fault maintenance task to the maintenance terminal closest to the Beidou positioning information.

[0015] In a third aspect, the present application provides a non-volatile computer storage medium on which computer instructions are stored. When the computer instructions are executed, they implement a power distribution fault diagnosis method based on Beidou positioning as any of the above items.

[0016] Those skilled in the art can understand that the present application has at least the following beneficial effects: The present application discloses a power distribution fault diagnosis method, system and medium based on Beidou positioning. By setting temperature sensors and current transformers on drones, it is possible to effectively collect temperature data and current data of overhead circuits or power distribution lines in remote areas. In addition, by utilizing the principle that residual current will appear in the circuit when a line fails, the specific interval (location) where the fault occurs can be effectively and quickly determined only by the computing chip set on the drone. This solves the problems of existing power distribution fault diagnosis methods being time-consuming and labor-intensive, unable to cope with sudden fault conditions, and having a long algorithm model processing procedure. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Some embodiments of the present disclosure are described below with reference to the accompanying drawings, in which: Figure 1 This is a flow chart of a power distribution fault diagnosis method based on Beidou positioning provided in an embodiment of the present application.

[0018] Figure 2 It is a schematic diagram of the internal structure of a power distribution fault diagnosis system based on Beidou positioning provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] It should be understood by those skilled in the art that the embodiments described below are only preferred embodiments of the present disclosure, and do not mean that the present disclosure can only be implemented through the preferred embodiments. The preferred embodiments are only used to explain the technical principles of the present disclosure, and are not used to limit the protection scope of the present disclosure. Based on the preferred embodiments provided by the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work should still fall within the protection scope of the present disclosure.

[0020] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0021] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0022] The present application embodiment provides a power distribution fault diagnosis method based on Beidou positioning, such as Figure 1 As shown, the method provided in the embodiment of the present application mainly includes the following steps: Step 110: Obtain the power distribution fault detection circuit and the number of drones to determine the detection sub-circuit corresponding to each drone in the power distribution fault detection circuit.

[0023] It should be noted that the execution subject involved in this application is any server, and the Beidou positioning module, current transformer, temperature sensor, and computing chip are set in the drone through the server, and the detection sub-circuit is preset with a current detection point. Among them, the Beidou positioning module can be any feasible module capable of Beidou positioning, the current transformer can be any feasible current transformer device including a plug-type mutual inductance coil, the temperature sensor can be any feasible device or apparatus capable of temperature detection, and the computing chip can be any feasible chip capable of subtraction operation.

[0024] In addition, the method for presetting the current detection point can be specifically as follows: Before determining the detection sub-circuit corresponding to each UAV in the power distribution fault detection circuit, the position data of the current detection point on the detection sub-circuit is obtained by executing the preset interface set in the main body.

[0025] Step 120, when the drone receives the collection signal, based on the direction of the detection sub-line, the temperature value at the current detection point is collected in sequence through the temperature sensor on the drone, and when the temperature value exceeds the preset temperature threshold, the fault signal is uploaded to the central processor; when the fault does not exceed the preset temperature threshold, the current data at the current detection point is collected through the current transformer on the drone; when the relationship between the current data at the current current detection point and the previous current detection point meets the preset fault relationship, the fault signal is uploaded to the central processor.

[0026] It should be noted that the current current detection point does not include the initial current detection point, and the fault signal includes the bucket positioning information. The direction of the detection sub-circuit is the direction of the current or any feasible preset direction standard. The preset fault relationship includes at least: between the current data at the current current detection point and the previous current detection point, there is only one that is 0; or the difference between the current data at the current current detection point and the previous current detection point is greater than the preset difference threshold. The preset fault relationship can be that the difference between the current data at the current current detection point and the previous current detection point is greater than the preset threshold.

[0027] In addition, in order to assist the plug-in type mutual inductance coil to surround the power distribution circuit and then measure the data on the power distribution circuit, the drone in this application includes a mechanical arm and a preset control program corresponding to the mechanical arm, which can control the plug-in type mutual inductance coil to surround the power distribution circuit and then be inserted into the current transformer socket. It should be supplemented that the mechanical arm and the preset control program corresponding to the mechanical arm can be set and installed by a person skilled in the art according to the existing technical means.

[0028] Among them, the current data at the current detection point is collected by the current transformer on the drone, which can be specifically: At the current detection point, through the preset control program, the robotic arm is controlled to insert the plug-type mutual inductance coil around the distribution line into the current transformer socket to collect current data at the current detection point; when it is determined that the fluctuation value of the collected current data is less than the preset fluctuation threshold within the preset collection time period, one end of the plug-type mutual inductance coil is automatically popped out from the current transformer socket.

[0029] In addition, in order to realize an effective dimensional power distribution circuit, after uploading the fault signal to the central processor, the method may further include: Obtain Beidou positioning information from the fault signal and establish a fault maintenance task; wherein the fault maintenance task includes Beidou positioning information; obtain the positioning information of each maintenance terminal to send the fault maintenance task to the maintenance terminal closest to the Beidou positioning information.

[0030] besides, Figure 2 A power distribution fault diagnosis system based on Beidou positioning is provided in the embodiment of the present application. Figure 2 As shown, the system provided in the embodiment of the present application mainly includes: The route determination module 210 is used to obtain the power distribution fault detection line and the number of drones to determine the detection sub-line corresponding to each drone in the power distribution fault detection line.

[0031] It should be noted that the route determination module 210 can be any feasible device or apparatus capable of determining the detection sub-circuit corresponding to each drone in the power distribution fault detection circuit. The drone is provided with a Beidou positioning module, a current transformer, a temperature sensor, and a computing chip, and the detection sub-circuit is preset with a current detection point.

[0032] In addition, the scheme of presetting the current detection point may be specifically as follows: the current detection point determination unit in the route determination module 210 obtains the position data of the current detection point on the detection sub-circuit through a preset interface.

[0033] The fault detection module 220 is used to collect the temperature value at the current detection point through the temperature sensor on the drone in sequence based on the direction of the detection sub-line when the drone receives the collection signal, and upload the fault signal to the central processor when the temperature value exceeds the preset temperature threshold; when the fault does not exceed the preset temperature threshold, collect the current data at the current detection point through the current transformer on the drone; when the relationship between the current data at the current current detection point and the previous current detection point meets the preset fault relationship, upload the fault signal to the central processor; wherein the current current detection point does not include the initial current detection point, and the fault signal includes Beidou positioning information.

[0034] In addition, the drone includes a robotic arm and a preset control program corresponding to the robotic arm, and the current transformer includes a plug-type mutual inductance coil that can be automatically plugged in and out.

[0035] Among them, the current data at the current detection point is collected by the current transformer on the drone, which can be specifically: the current transformer control unit in the fault detection module 220 controls the mechanical arm to insert the plug-type mutual inductance coil around the distribution line into the current transformer socket through a preset control program at the current detection point to collect the current data at the current detection point; when it is determined that the fluctuation value of the collected current data within the preset collection time period is less than the preset fluctuation threshold, one end of the plug-type mutual inductance coil is automatically popped out from the current transformer socket.

[0036] In addition, the system also includes a maintenance module for obtaining Beidou positioning information in the fault signal and establishing a fault maintenance task; wherein the fault maintenance task includes Beidou positioning information; obtaining the positioning information of each maintenance terminal to send the fault maintenance task to the maintenance terminal closest to the Beidou positioning information.

[0037] In addition, an embodiment of the present application further provides a non-volatile computer storage medium on which executable instructions are stored. When the executable instructions are executed, a power distribution fault diagnosis method based on Beidou positioning as described above is implemented.

[0038] So far, the technical solutions of the present disclosure have been described in combination with the above multiple embodiments, but it is easy for those skilled in the art to understand that the protection scope of the present disclosure is not limited to these specific embodiments. Without departing from the technical principles of the present disclosure, those skilled in the art can split and combine the technical solutions in the above-mentioned various embodiments, and can also make equivalent changes or replacements to the relevant technical features. Any changes, equivalent replacements, improvements, etc. made within the technical concept and / or technical principle of the present disclosure will fall within the protection scope of the present disclosure.

Claims

1. A power distribution fault diagnosis method based on Beidou positioning, characterized in that: The method comprises: Obtain the power distribution fault detection circuit and the number of drones to determine the detection sub-circuit corresponding to each drone in the power distribution fault detection circuit; wherein the drone is provided with a Beidou positioning module, a current transformer, a temperature sensor, and a computing chip, and the detection sub-circuit is preset with a current detection point; When the drone receives the collection signal, based on the direction of the detection sub-line, the temperature value at the current detection point is collected in sequence through the temperature sensor on the drone. When the temperature value exceeds the preset temperature threshold, the fault signal is uploaded to the central processor; when the fault does not exceed the preset temperature threshold, the current data at the current detection point is collected through the current transformer on the drone; when the relationship between the current data at the current current detection point and the previous current detection point meets the preset fault relationship, the fault signal is uploaded to the central processor; wherein, the current current detection point does not include the initial current detection point, and the fault signal includes bucket positioning information.

2. The power distribution fault diagnosis method based on Beidou positioning according to claim 1 is characterized in that: Before determining the detection sub-circuits corresponding to the respective UAVs in the power distribution fault detection circuit, the method further includes: Through the preset interface, the position data of the current detection point on the detection sub-circuit is obtained.

3. The power distribution fault diagnosis method based on Beidou positioning according to claim 1 is characterized in that: The drone includes a mechanical arm and a preset control program corresponding to the mechanical arm, and the current transformer includes a plug-type mutual inductance coil that can be automatically plugged in and out; The current data at the current detection point is collected through the current transformer on the drone, including: At the current detection point, the preset control program is used to control the robotic arm to insert the plug-type mutual inductance coil around the distribution line into the current transformer socket to collect current data at the current detection point; When it is determined that the fluctuation value of the collected current data within the preset collection time period is less than the preset fluctuation threshold, one end of the plug-type mutual inductance coil is automatically ejected from the current transformer socket.

4. The power distribution fault diagnosis method based on Beidou positioning according to claim 1 is characterized in that: The preset fault relationship includes at least: There is only one current data between the current detection point and the previous current detection point that is 0; Or the difference between the current data at the current detection point and the current data at the previous current detection point is greater than a preset difference threshold.

5. The power distribution fault diagnosis method based on Beidou positioning according to claim 1 is characterized in that: After uploading the fault signal to the central processor, the method further includes: Obtain Beidou positioning information in the fault signal and establish a fault maintenance task; wherein the fault maintenance task includes Beidou positioning information; Obtain the positioning information of each maintenance terminal to send the fault maintenance task to the maintenance terminal closest to the Beidou positioning information.

6. A power distribution fault diagnosis system based on Beidou positioning, characterized in that: The system comprises: The route determination module is used to obtain the power distribution fault detection line and the number of drones to determine the detection sub-line corresponding to each drone in the power distribution fault detection line; wherein the drone is provided with a Beidou positioning module, a current transformer, a temperature sensor, and a computing chip, and the detection sub-line is preset with a current detection point; The fault detection module is used to collect the temperature value at the current detection point through the temperature sensor on the drone in sequence based on the direction of the detection sub-circuit when the drone receives the collection signal, and upload the fault signal to the central processor when the temperature value exceeds the preset temperature threshold; when the fault does not exceed the preset temperature threshold, the current data at the current detection point is collected through the current transformer on the drone; when the relationship between the current data at the current current detection point and the previous current detection point meets the preset fault relationship, the fault signal is uploaded to the central processor; wherein the current current detection point does not include the initial current detection point, and the fault signal includes Beidou positioning information.

7. The power distribution fault diagnosis system based on Beidou positioning according to claim 6 is characterized in that: The route determination module includes a current detection point determination unit, Used to obtain the position data of the current detection point on the detection sub-circuit through the preset interface.

8. The power distribution fault diagnosis system based on Beidou positioning according to claim 6 is characterized in that: The drone includes a mechanical arm and a preset control program corresponding to the mechanical arm, and the current transformer includes a plug-type mutual inductance coil that can be automatically plugged in and out; The fault detection module includes a current transformer control unit, Used to control the mechanical arm to surround the distribution line with a plug-type mutual inductance coil and insert it into the current transformer socket at the current detection point through a preset control program to collect current data at the current detection point; When it is determined that the fluctuation value of the collected current data within the preset collection time period is less than the preset fluctuation threshold, one end of the plug-type mutual inductance coil is automatically ejected from the current transformer socket.

9. The power distribution fault diagnosis system based on Beidou positioning according to claim 6 is characterized in that: The system also includes a maintenance module, Used to obtain Beidou positioning information in the fault signal and establish a fault maintenance task; wherein the fault maintenance task includes Beidou positioning information; Obtain the positioning information of each maintenance terminal to send the fault maintenance task to the maintenance terminal closest to the Beidou positioning information.

10. A non-volatile computer storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed, the method for diagnosing power distribution faults based on Beidou positioning as described in any one of claims 1 to 5 is implemented.

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