Fault location method, device and equipment of transformer area power grid and storage medium

Abstract

Embodiments of the application disclose a fault positioning method, device and equipment of a transformer area power grid and a storage medium, and relate to the technical field of power management. The method comprises: acquiring first electrical data monitored by a first monitoring device, second electrical data monitored by a second monitoring device and third electrical data monitored by a third monitoring device; determining a branch monitoring result of a corresponding branch according to the second electrical data and the third electrical data, and determining a trunk monitoring result according to the first electrical data and the second electrical data when the branch monitoring results of all branches are normal; performing trunk fault positioning according to the trunk monitoring result to obtain a trunk fuzzy fault position; and performing accurate positioning based on the trunk fuzzy fault position by using a drone to obtain a trunk fault position. The technical scheme provided in the embodiments of the application can quickly and accurately position the fault position of the transformer area power grid, improve the speed of power recovery, and meet the demand for rapid power recovery.

Description

Technical Field

[0001] This application relates to the field of power management technology, and in particular to a fault location method, apparatus, equipment and storage medium for a distribution network. Background Technology

[0002] A transformer substation power grid refers to the power supply range or area of ​​a transformer. When a fault occurs in a transformer substation power grid, manual inspection is usually used, where maintenance personnel check the line section by section. However, manual fault location not only consumes a lot of manpower and time, but also makes it difficult to locate the fault point in a timely and accurate manner in complex geographical environments (such as mountains and forests) and severe weather conditions (such as heavy rain and blizzards). This results in a slow power restoration speed, which cannot meet the demand for rapid power restoration. Summary of the Invention

[0003] This application provides a method, apparatus, device, and storage medium for fault location in a distribution network, which can quickly and accurately locate the fault location in the distribution network, thus solving the problem that it is difficult to locate the fault point in a timely and accurate manner in the prior art.

[0004] In a first aspect, embodiments of this application provide a fault location method for a distribution network, the distribution network including a main line and at least one branch line, a first monitoring device installed at the beginning of the main line, a second monitoring device installed at the beginning of each branch line, and a third monitoring device installed at each end of each branch line, the method comprising:

[0005] Acquire the first electrical data monitored by the first monitoring device, the second electrical data monitored by the second monitoring device, and the third electrical data monitored by the third monitoring device;

[0006] The branch monitoring results of the corresponding branch are determined based on the second and third electrical data, and when the branch monitoring results of all branches are normal, the trunk monitoring results are determined based on the first and second electrical data.

[0007] Based on the trunk road monitoring results, the trunk road fault location is determined, resulting in an fuzzy fault location.

[0008] By using drones to accurately locate faults on main roads based on fuzzy fault locations, the fault location on the main road can be obtained.

[0009] Secondly, embodiments of this application provide a fault location device for a transformer substation power grid. The transformer substation power grid includes a main line and at least one branch line. A first monitoring device is installed at the beginning of the main line, a second monitoring device is installed at the beginning of each branch line, and a third monitoring device is installed at each end of each branch line. The device includes:

[0010] The acquisition module is used to acquire the first electrical data monitored by the first monitoring device, the second electrical data monitored by the second monitoring device, and the third electrical data monitored by the third monitoring device;

[0011] The determination module is used to determine the branch monitoring result of the corresponding branch based on the second electrical data and the third electrical data, and when the branch monitoring result of all branches is normal, to determine the trunk monitoring result based on the first electrical data and the second electrical data.

[0012] The fuzzy positioning module is used to locate faults on the trunk road based on the trunk road monitoring results, and obtain the fuzzy fault location on the trunk road.

[0013] The precise positioning module is used to accurately locate faults on main roads using drones based on fuzzy fault locations.

[0014] Thirdly, embodiments of this application provide an electronic device, which includes:

[0015] At least one processor; and a memory communicatively connected to the at least one processor;

[0016] The memory stores a computer program that can be executed by at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the fault location method of the power grid of any embodiment of this application.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a fault location method for a distribution network as described in any embodiment of this application.

[0018] In this embodiment, the first electrical data monitored by the first monitoring device, the second electrical data monitored by the second monitoring device, and the third electrical data monitored by the third monitoring device can be acquired. Then, the branch monitoring results of the corresponding branch are determined based on the second electrical data and the third electrical data. When the branch monitoring results of all branches are normal, the trunk monitoring results are determined based on the first electrical data and the second electrical data. Then, the trunk fault is located based on the trunk monitoring results to obtain the fuzzy fault location of the trunk. The UAV is then used to perform precise location based on the fuzzy fault location of the trunk to obtain the fault location of the trunk, thus realizing the fault location function of the power grid in the distribution area. Compared to manual inspection, the above-mentioned technical solution, which relies on monitoring electrical data and combining it with drone inspection, can quickly determine the location of a vague fault in the main circuit based on the analysis of electrical data, regardless of the external environment. Then, drones are used for precise inspection at the location of the vague fault, significantly shortening the time from fault occurrence to fault location and reducing the workload of maintenance personnel. This makes the fault location process more efficient, thereby improving the efficiency of determining the fault location in the main circuit and facilitating faster maintenance. Simultaneously, by analyzing the electrical data collected by the first, second, and third monitoring devices, the topology of the entire power grid in the distribution area can be considered, taking into account electrical data from different key locations. This improves the accuracy of determining the location of vague faults in the main circuit and greatly narrows the scope of drone inspection. Precise drone positioning further improves the accuracy of fault location determination and reduces the occurrence of misjudgments. This effectively solves the problem of difficulty in timely and accurate fault location in existing technologies, thereby increasing the speed of power restoration and meeting the demand for rapid power restoration. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating a fault location method for a distribution network provided in an embodiment of this application.

[0021] Figure 2 This is another flowchart illustrating the fault location method for the power grid in the distribution area provided in this application embodiment;

[0022] Figure 3 This is a schematic diagram of a fault location device for a distribution network provided in an embodiment of this application;

[0023] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0025] It should be noted that the terms "first," "second," "target," and "original," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in sequences other than those illustrated or described herein. Furthermore, the terms "comprising," "having," and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] Figure 1 This is a flowchart illustrating a fault location method for a distribution network provided in this application embodiment. This embodiment can be applied to scenarios requiring rapid and accurate fault location of the distribution network under complex geographical environments and severe weather conditions. The fault location method for a distribution network provided in this embodiment can be executed by a fault location device for the distribution network provided in this application embodiment. This device can be implemented through software and / or hardware. In a specific embodiment, the fault location device for the distribution network can be integrated into an electronic device, such as a computer. The executing entity of this method can be an electronic device.

[0027] In one specific embodiment, the distribution network may include a main line and at least one branch line. A first monitoring device is installed at the beginning of the main line, a second monitoring device is installed at the beginning of each branch line, and a third monitoring device is installed at each end of each branch line. The electronic device can communicate with the first, second, and third monitoring devices. Each branch line may include multiple ends, which can be connected to a user terminal to deliver electrical energy to the user terminal. The first monitoring device is installed at the beginning of the main line and is used to measure and collect electrical parameters of the main line in real time. The second monitoring device is installed at the beginning of the branch line and has a similar function to the first monitoring device, used to monitor the electrical operation of the branch line from its source and provide data support for judging the operating status of the branch line. The third monitoring device is installed at the end of the branch line and is used to acquire electrical parameters at the end of the branch line and provide data support for judging the operating status of the branch line.

[0028] See Figure 1 The fault location method for the power grid in this embodiment includes, but is not limited to, the following steps:

[0029] S110: Acquire the first electrical data monitored by the first monitoring device, the second electrical data monitored by the second monitoring device, and the third electrical data monitored by the third monitoring device.

[0030] The first electrical data refers to the electrical data at the beginning of the trunk line monitored by the first monitoring device, which reflects the power quality and operating status of the trunk line at the initial position and provides data support for subsequent analysis of whether the trunk line is faulty; the second electrical data refers to the electrical data at the beginning of the branch line monitored by the second monitoring device, which is used to analyze the operating status of the branch line at the beginning position; the third electrical data refers to the electrical data at the end of the branch line monitored by the third monitoring device; for example, the electrical data may include parameters such as voltage, current, power, frequency, phase and quantity.

[0031] Specifically, when the power grid in the distribution area is in operation, the first monitoring device, the second monitoring device, and the third monitoring device can monitor the electrical data of the power grid in the distribution area in real time, and collect the monitored electrical data according to the preset sampling frequency and sampling rules. Thus, when locating faults in the power grid in the distribution area, the electrical data monitored and collected in real time by the first monitoring device can be obtained to obtain the first electrical data, the electrical data monitored and collected in real time by the second monitoring device can be obtained to obtain the second electrical data, and the electrical data monitored and collected in real time by the third monitoring device can be obtained to obtain the third electrical data.

[0032] S120. Determine the branch monitoring results of the corresponding branch based on the second electrical data and the third electrical data, and when the branch monitoring results of all branches are normal, determine the trunk monitoring results based on the first electrical data and the second electrical data.

[0033] Among them, the branch monitoring result is the conclusion of whether the branch is operating normally; the branch monitoring result can include whether the branch is normal or faulty.

[0034] The trunk road monitoring results are the conclusions on whether the trunk road is operating normally; the trunk road monitoring results can include trunk road normal or trunk road fault.

[0035] Specifically, after obtaining the first electrical data, the second electrical data, and the third electrical data, the branch monitoring result of the corresponding branch can be determined based on the second electrical data and the third electrical data. For example, for the current branch in at least one branch, an equivalent circuit model of the current branch can be established, and the parameters of the equivalent circuit model can be initially determined based on the second electrical data to obtain the first parameter. Then, the parameters of the equivalent circuit model can be determined based on the third electrical data to obtain the second parameter. When the change between the first parameter and the second parameter is within a first preset change range, the branch monitoring result of the current branch is determined to be normal. The first preset change range is a pre-set range of values ​​used to determine whether the change between the first parameter and the second parameter is within the normal change range, thereby determining the branch monitoring result. Users can adjust and set this first preset change range according to actual usage needs; this application embodiment does not specifically limit this.

[0036] Specifically, an equivalent circuit model of the current branch can be established based on its circuit structure and actual load characteristics. For example, if the current branch is a purely resistive load (such as an incandescent lamp or electric heater), a simple resistance model can be used; if the current branch contains inductive loads such as motors, a model with resistors and inductors in series can be used. Then, the starting voltage and starting current are obtained from the second electrical data and substituted into the equivalent circuit model to obtain the first parameter. For example, for a simple resistance model, the first parameter, i.e., resistance, can be determined using Ohm's law based on the starting voltage and starting current in the second electrical data; or, for a resistance and inductance model... In the series model, the first parameters, namely resistance and inductance, are determined based on the starting terminal voltage and starting terminal current in the second electrical data using Ohm's law and the voltage-current relationship of the inductor. Then, the terminal voltage and terminal current are obtained from the third electrical data of each terminal, and the sum of these terminal voltages is calculated to obtain the total terminal voltage, and the sum of these terminal currents is calculated to obtain the total terminal current. The total terminal voltage and total terminal current are then substituted into the equivalent circuit model to obtain the second parameters. Finally, if the change between the first parameter and the second parameter is within a first preset range, the branch monitoring result of the current branch is determined to be normal; otherwise, the branch monitoring result of the current branch is determined to be faulty.

[0037] When the branch monitoring results for all branches are normal, it indicates that all branches of the power grid in the distribution area are normal. At this point, it is necessary to further determine whether the main line is faulty. This can be done by determining the main line monitoring results based on the first and second electrical data. For example, an overall equivalent circuit model of the main line and branches can be established, and the theoretical electrical data of the branches can be calculated based on the overall equivalent circuit model. Then, the change between the theoretical electrical data and the second electrical data of the corresponding branch can be calculated to obtain the change corresponding to the branch. When the change corresponding to all branches is within the second preset change range, the main line monitoring result is determined to be normal. The second preset change range is a pre-set range of values ​​used to determine whether the change between the theoretical electrical data and the second electrical data is within the normal change range, thereby determining the main line monitoring result. Users can adjust and set this second preset change range according to actual usage needs. This application embodiment does not specifically limit this.

[0038] Specifically, the main circuit can be viewed as an equivalent power source with electromotive force and internal resistance, and an equivalent line impedance. The equivalent circuit models of all branches are connected in parallel to the equivalent circuit of the main circuit to form an overall equivalent circuit model. Then, based on the first electrical data and the second electrical data of each branch, the parameters of the overall equivalent circuit model are determined, namely, electromotive force, internal resistance, and equivalent line impedance. Then, using the current distribution principle of parallel circuits, based on the parameters of the overall equivalent circuit model and the parameters of the equivalent circuit model when the branches are operating normally, the theoretical electrical data distributed from the main circuit to each branch is calculated. For example, taking the theoretical current as an example, the change between the theoretical current and the starting current in the second electrical data of the corresponding branch is calculated to obtain the change corresponding to that branch. If the changes corresponding to all branches are within the second preset change range, the monitoring result of the main circuit is determined to be normal; otherwise, the monitoring result of the main circuit is determined to be a fault.

[0039] S130. Based on the trunk road monitoring results, locate the trunk road fault to obtain the fuzzy fault location.

[0040] Among them, the fuzzy fault location of the trunk road is the preliminary fault location obtained when locating faults on the trunk road based on the trunk road monitoring results. At this time, the fuzzy fault location of the trunk road is not the precise fault location, but a range of locations where a fault may exist.

[0041] Specifically, after obtaining the trunk line monitoring results, if the trunk line monitoring results indicate that the trunk line is normal, it means that the trunk line has not experienced a fault. At this time, S110 to S120 can be repeated to continue to determine whether the trunk line has experienced a fault. If the trunk line monitoring results indicate that the trunk line is faulty, it means that the trunk line has experienced a fault. At this time, the trunk line fault can be located based on the trunk line monitoring results to obtain the fuzzy fault location of the trunk line. For example, the electrical quantity change analysis of the first electrical data and the second electrical data can be performed, and the trunk line topology can be combined to determine one or more possible fault location intervals. For example, if the voltage and current change abnormally at the same time on a certain section of the trunk line, and there is no branch line connected in the middle of this section of the line, then this section of the line is determined as the fault location interval. For complex trunk line structures, multiple fault location intervals may be determined. These fault location intervals constitute the fuzzy fault location of the trunk line.

[0042] S140. Using drones to accurately locate faults on main roads based on fuzzy fault locations, the fault location on the main road is obtained.

[0043] Among them, the fault location on the trunk road is the specific fault location determined by further inspection and precise detection of the vague fault location on the trunk road using drones, providing accurate fault location information for operation and maintenance personnel.

[0044] Specifically, after obtaining the fuzzy fault location of the trunk road, the flight path of the UAV can be planned based on the fuzzy fault location of the trunk road to ensure that the flight path of the UAV can completely cover the possible fault area, and the planned flight path can avoid obstacles (such as buildings and trees) and other dangerous areas. For example, the flight path can be a broken line or a spiral line to ensure sufficient inspection of the fuzzy fault location of the trunk road.

[0045] Then, the drone can be controlled to perform inspections along the flight path and acquire images collected during the inspection process. For example, the visible light camera on the drone can be used to take pictures of the blurred fault location on the main road to obtain the visible light image corresponding to the blurred fault location on the main road; or, the infrared image acquisition device on the drone can be used to acquire infrared images of the blurred fault location on the main road.

[0046] Afterwards, the acquired images can be analyzed to determine the location of the main line fault. For example, the location of abnormal temperature rise can be determined from infrared images to obtain the temperature anomaly point, which is also the location of the main line fault. Alternatively, visible light images can be analyzed manually or automatically. When analyzed manually, professionals can examine the visible light images to check for obvious damage to the line, hanging foreign objects, and damaged insulators, thereby determining the location of the main line fault. When analyzed automatically, image recognition technology can be used to identify line fault characteristics through a trained neural network model, thereby obtaining the location of the main line fault.

[0047] Optionally, after obtaining the location of the main line fault, the location of the main line fault can be displayed. For example, the location of the main line fault can be displayed on a screen, by issuing a prompt sound, by sending a text message or email, so as to remind maintenance personnel to go to the location of the main line fault for repair in a timely manner, thereby improving the speed of power restoration.

[0048] The technical solution of this application embodiment can acquire first electrical data monitored by a first monitoring device, second electrical data monitored by a second monitoring device, and third electrical data monitored by a third monitoring device. Then, based on the second electrical data and the third electrical data, the branch monitoring result of the corresponding branch is determined. When the branch monitoring result of all branches is normal, the trunk monitoring result is determined based on the first electrical data and the second electrical data. Then, the trunk fault is located based on the trunk monitoring result to obtain the fuzzy fault location of the trunk. The trunk fault location is then accurately located using a drone based on the fuzzy fault location of the trunk, thus realizing the fault location function of the power grid in the distribution area. Compared to manual inspection, the above-mentioned technical solution, which relies on monitoring electrical data and combining it with drone inspection, can quickly determine the location of a vague fault in the main circuit based on the analysis of electrical data, regardless of the external environment. Then, drones are used for precise inspection at the location of the vague fault, significantly shortening the time from fault occurrence to fault location and reducing the workload of maintenance personnel. This makes the fault location process more efficient, thereby improving the efficiency of determining the fault location in the main circuit and facilitating faster maintenance. Simultaneously, by analyzing the electrical data collected by the first, second, and third monitoring devices, the topology of the entire power grid in the distribution area can be considered, taking into account electrical data from different key locations. This improves the accuracy of determining the location of vague faults in the main circuit and greatly narrows the scope of drone inspection. Precise drone positioning further improves the accuracy of fault location determination and reduces the occurrence of misjudgments. This effectively solves the problem of difficulty in timely and accurate fault location in existing technologies, thereby increasing the speed of power restoration and meeting the demand for rapid power restoration.

[0049] The following further describes a fault location method for a distribution network provided by an embodiment of this application. Figure 2 This is another flowchart illustrating the fault location method for a distribution network provided in this application. This application's embodiment is an optimization based on the above embodiments.

[0050] Optionally, the first monitoring device may include a first fault recorder and a first smart meter; the second monitoring device may include a second fault recorder and a second smart meter; and the third monitoring device may include a third smart meter. The fault recorder is a device capable of automatically and accurately recording the changes in electrical data (such as voltage and current) of the power grid in the distribution area before, during, and after a fault. The first fault recorder is installed at the beginning of the main line, and the second fault recorder is installed at the beginning of the branch line. The smart meter is an intelligent energy metering device that, in addition to the energy metering function of a traditional meter, can monitor and record various electrical data such as voltage, current, power factor, active power, and reactive power in real time, and can transmit this data to other devices through a communication interface. The first smart meter is installed at the beginning of the main line, the second smart meter is installed at the beginning of the branch line, and the third smart meter is installed at the end of the branch line. It should be noted that the first and second fault recorders are low-precision fault recorders, which are low in cost and thus reduce the cost of fault location.

[0051] See Figure 2 The method in this embodiment includes, but is not limited to, the following steps:

[0052] S201. Acquire the first electrical data monitored by the first monitoring device, the second electrical data monitored by the second monitoring device, and the third electrical data monitored by the third monitoring device.

[0053] Specifically, it is possible to acquire the first electrical data monitored by the first smart meter, the second electrical data monitored by the second smart meter, and the third electrical data monitored by the third smart meter.

[0054] Optionally, before acquiring the first electrical data monitored by the first monitoring device, the second electrical data monitored by the second monitoring device, and the third electrical data monitored by the third monitoring device, a point cloud dataset obtained by the UAV collecting data on the power grid in the distribution area can be acquired. Then, a distribution area line laying model of the power grid in the distribution area can be established based on the point cloud dataset, and the trunk lines and branch lines in the distribution area line laying model can be distinguished and displayed.

[0055] The point cloud dataset is a collection of a large number of three-dimensional points obtained by scanning the power grid of the transformer substation using a lidar or other three-dimensional scanning equipment mounted on a drone. It also records the spatial location information of each point. These points together constitute the spatial morphological representation of the power grid of the transformer substation and its surrounding environment.

[0056] The transformer substation line laying model is a digital model built based on point cloud datasets. It is used to intuitively display the laying of lines in the transformer substation power grid. It is also a virtual representation of the line topology, equipment location and connection relationship of the transformer substation power grid.

[0057] Specifically, the flight path of the drone can be planned based on factors such as the scope, terrain, and surrounding environment of the power grid area to ensure comprehensive coverage. Secondly, the drone is controlled to fly along the planned path, and during flight, it uses onboard LiDAR and other equipment to scan the power grid area, obtaining a point cloud dataset. Next, the point cloud dataset is preprocessed, using methods such as filtering and noise reduction. Key features of the power grid area are extracted from the preprocessed point cloud data, primarily the geometric features of poles, lines, and transformers. For example, for poles, based on their shape, relative height, and perpendicularity to the ground, geometric shape analysis algorithms can be used for automatic identification, obtaining parameters such as position, height, and diameter. For instance, Hough transform can be used to identify cylindrical utility poles. For lines, based on their long, thin shape and aerial extension, spatial distribution patterns and curvature analysis can be used to identify the line's direction and connections, distinguishing different line segments with different directions and connections.

[0058] Then, based on the identified tower locations and line connections, the point cloud data corresponding to adjacent towers are sequentially connected in three-dimensional space to construct the basic framework of the transformer substation line laying model. The identified transformers, switches, and other power grid auxiliary equipment are added to the transformer substation line laying model according to their position and attitude information in the point cloud dataset, so that they match the basic framework and improve the transformer substation line laying model, thereby obtaining the transformer substation power grid line laying model.

[0059] Next, the trunk and branch lines in the transformer substation line laying model can be classified, and different attribute labels can be set for trunk and branch lines in the transformer substation line laying model. For example, based on the design drawings of the transformer substation power grid, the power supply hierarchy relationship learned from the on-site survey, and the analysis of line current flow, load distribution, etc., the lines in the transformer substation line laying model can be classified into trunk and branch lines. The trunk and branch lines can be distinguished by naming rules or by adding a classification label field in the data structure. Finally, the transformer substation line laying model can be rendered and displayed on the display screen of the electronic device, and the trunk and branch lines can be distinguished by line thickness and color. For example, the trunk lines can be represented by thicker red lines, and the branch lines can be represented by thinner green lines.

[0060] In this embodiment of the application, by constructing a model of the distribution area's power line layout and distinguishing between trunk lines and branch lines, users such as power operation and maintenance personnel and engineering technicians can quickly understand the structural layout and electrical connection relationships of the distribution area's power grid, providing a basis for the refined management of the distribution area's power grid.

[0061] S202. Determine the branch monitoring results of the corresponding branch based on the second electrical data and the third electrical data.

[0062] Specifically, for the current branch in at least one branch, the sum of the third electrical data of each end of the current branch can be calculated to obtain the end electrical data. Then, the difference between the second electrical data of the current branch and the end electrical data can be calculated to obtain the first difference value. The absolute value of the first difference value is determined as the first actual loss value of the current branch. Then, when the first actual loss value is greater than the preset branch loss value, the branch monitoring result of the current branch is determined to be a branch fault; when the first actual loss value is not greater than the preset branch loss value, the branch monitoring result of the current branch is determined to be a normal branch. Among them, the terminal electrical data is the sum of the third electrical data of all the terminals of the current branch, reflecting the overall electrical quantity of the current branch at the terminal; the first difference is the difference between the second electrical data and the terminal electrical data of the current branch, which can reflect the change of electrical quantity from the start to the end of the current branch; the first actual loss value is used to intuitively represent the degree of electrical quantity loss that actually occurs from the start to the end of the current branch; the preset branch loss value is a pre-set threshold, representing the maximum electrical quantity loss that is allowed from the start to the end of the branch under normal conditions, and is used to determine whether the branch loss is normal, thereby determining whether the branch is operating normally. That is, if the first actual loss value is greater than the preset branch loss value, it indicates that the current branch has a fault, such as aging of the line, poor contact, or leakage, which leads to excessive electrical quantity loss; otherwise, it indicates that the current branch is normal.

[0063] By using the absolute value of the difference between the second electrical data of the current branch and the third electrical data of all ends of the current branch, the electrical loss situation inside the branch can be reflected more intuitively, thereby improving the accuracy and efficiency of determining the first actual loss value. Then, by further comparing the first actual loss value with the preset branch loss value, it is possible to accurately determine whether the branch has abnormal loss, thereby improving the accuracy and efficiency of determining the branch monitoring results and providing an accurate data basis for subsequently determining the branch fault location.

[0064] S203. Determine whether there is a branch monitoring result for a branch fault.

[0065] Specifically, after obtaining the branch monitoring results for each branch, it can be determined whether the branch monitoring result indicates a branch fault. If there is a branch with a branch monitoring result indicating a branch fault among all branches, then S204 can be executed; if there is no branch with a branch monitoring result indicating a branch fault among all branches, then S207 can be executed.

[0066] S204. When the branch monitoring result indicates a branch fault, the branch corresponding to the branch monitoring result shall be identified as the faulty branch.

[0067] Among them, a faulty branch is a branch that is determined to be faulty by calculating and comparing electrical data during the branch monitoring process, and there may be one or more faulty branches.

[0068] Optionally, after identifying a faulty branch, the branch can be marked as faulty, and the number of fault markings for that branch can be updated. When the number of fault markings for a branch exceeds a preset threshold, the users under that branch can be marked for key monitoring, and a list of these users can be displayed to remind maintenance personnel to closely monitor whether these users are engaging in dangerous electrical practices, such as haphazardly extending electrical wires. The preset threshold is a pre-set value used to determine whether to mark the users under that branch for key monitoring.

[0069] S205. Use the second fault recorder corresponding to the faulty branch to locate the faulty branch and obtain the fuzzy fault location of the branch.

[0070] Among them, the branch fuzzy fault location is the preliminary fault location obtained when locating branch faults based on branch monitoring results. At this time, the branch fuzzy fault location is not the precise fault location, but a range of possible fault locations.

[0071] Specifically, a fault recorder can record the dynamic changes in electrical data before and after a fault occurs, such as waveform, amplitude, and phase information. Therefore, after identifying the faulty branch, the change characteristics of the electrical data during the fault occurrence period can be extracted from a second fault recorder installed at the beginning of the faulty branch. These characteristics include voltage waveform, amplitude, and phase changes, or current waveform, amplitude, and phase changes. Then, based on these change characteristics, the time of fault occurrence can be determined, such as the starting point of a significant change in electrical quantity, like a sudden voltage drop or current surge. Finally, based on the change characteristics and the time of fault occurrence, the fuzzy fault location of the branch can be determined. For example, parameters such as the line length, wire diameter, and material of the faulty branch can be obtained, and these parameters can be used to determine the location of the fault. The information calculation measures the impedance per unit length of the line. Then, based on the impedance per unit length and the voltage and current recorded by the second fault recorder at the moment of the fault, the distance from the fault point to the second fault recorder is determined, denoted as the first distance. This first distance is a preliminary estimate; in reality, errors may occur due to inaccurate line parameters and load effects. Therefore, an error threshold is set, denoted as the preset error threshold. Next, based on the first distance and the preset error threshold, a possible fault distance range is determined, i.e., [first distance - preset error threshold, first distance + preset error threshold]. The branch fuzzy fault location is then determined based on the distance range; that is, the location corresponding to each distance within the distance range is determined, and these locations are combined into a fault location range, i.e., the branch fuzzy fault location.

[0072] S206. Use drones to accurately locate branch faults based on fuzzy branch fault locations to obtain the branch fault location.

[0073] Among them, the branch fault location is the specific fault location determined by further inspection and precise detection of the ambiguous fault location of the branch using drones, providing accurate fault location information for operation and maintenance personnel.

[0074] Specifically, after obtaining the fuzzy fault location of the branch, a drone can be used for precise positioning. The specific implementation logic at this time is the same as that in S140, and will not be repeated here. Thus, the accurate fault location, i.e. the branch fault location, can be obtained.

[0075] It should be noted that S205 and S206 are explained using the example of determining the fault location of a single faulty branch. When there are multiple faulty branches, the process for determining the fault location of each faulty branch is the same, namely S205 and S206.

[0076] Optionally, the fault type can be determined based on images captured by the drone. For example, if the image of the branch fault location shows obvious signs of wire burning, insulation damage, and short circuit, the fault type can be identified as a short circuit fault; if the image of the branch fault location shows signs of breakage, the fault type can be identified as an open circuit fault. By determining the fault type, maintenance personnel can prepare the corresponding repair tools and materials in advance, making the repair work more targeted, thereby improving repair efficiency and success rate.

[0077] Optionally, after using drones to precisely locate branch faults based on their fuzzy locations, a branch fault warning can be generated and displayed in the distribution area line layout model. This warning alerts maintenance personnel to the fault location for repair. Specifically, the corresponding faulty branch can be located in the distribution area line layout model based on its fault location, highlighted, and flashed at its fault location. Simultaneously, the warning information can be displayed via pop-up windows on electronic devices, sound alerts, SMS messages, or emails to promptly remind maintenance personnel to proceed with repairs. The warning information can include the fault location, fault type, and repair suggestions. Displaying the warning information in the distribution area line layout model allows maintenance personnel to quickly and accurately locate faults, avoiding blind searching across large areas of the power grid. It also provides initial repair ideas, saving repair time, improving repair efficiency and quality, and effectively reducing fault recurrence due to improper repairs.

[0078] Optionally, after maintenance personnel repair the fault location of the faulty branch, they can issue a continue monitoring command to the electronic equipment to notify it to continue monitoring the power grid in the distribution area and locate the fault location. That is, when the electronic equipment receives the continue monitoring command issued by the maintenance personnel, it can repeat steps S201 to S203 and execute corresponding steps according to the branch monitoring results. Specifically, when the branch monitoring result of a branch is a branch fault, steps S204 to S206 are executed; when the branch monitoring result of all branches is normal, steps S207 to S210 are executed.

[0079] S207. When the branch monitoring results of all branches are normal, the main branch monitoring results shall be determined based on the first electrical data and the second electrical data.

[0080] Specifically, when the branch monitoring results of all branches are normal, the sum of the second electrical data of at least one branch can be calculated to obtain the branch electrical data. Then, the difference between the branch electrical data and the first electrical data is calculated to obtain the second difference value. The absolute value of the second difference value is determined as the second actual loss value. Then, when the second actual loss value is greater than the preset trunk loss value, the trunk monitoring result is determined to be a trunk fault; when the second actual loss value is not greater than the preset trunk loss value, the trunk monitoring result is determined to be a trunk normal. Among them, the branch electrical data is the sum of the second electrical data at the starting point of all branches, reflecting the overall electrical quantity of all branches at the starting point; the second difference is the difference between the branch electrical data and the first electrical data, which can reflect the change between the main line and the total electrical quantity of all branches; the second actual loss value is used to intuitively represent the degree of electrical quantity loss that actually occurs in the main line; the preset main line loss value is a pre-set threshold, representing the maximum electrical quantity loss that the main line is allowed to occur under normal conditions, thereby determining whether the main line is operating normally. That is, if the second actual loss value is greater than the preset main line loss value, it indicates that the main line has a fault, such as aging of the main line, poor contact at the connection point, or leakage, resulting in excessive electrical quantity loss; otherwise, it indicates that the main line is normal.

[0081] By using the absolute value of the difference between the first electrical data and the second electrical data of all branches, the electrical loss situation inside the trunk can be reflected more intuitively, thereby improving the accuracy and efficiency of determining the second actual loss value. Then, by further comparing the second actual loss value with the preset trunk loss value, it is possible to accurately determine whether there is abnormal loss in the trunk, thereby improving the accuracy and efficiency of determining the trunk monitoring results and providing an accurate data basis for subsequently determining the location of trunk faults.

[0082] S208. Determine whether the trunk road monitoring results indicate a trunk road fault.

[0083] Specifically, if the trunk line monitoring result indicates a trunk line fault, S209 can be executed; if the trunk line monitoring result indicates a normal trunk line, S201 can be executed to continue monitoring whether a fault has occurred in the power grid of the distribution area.

[0084] S209. When the trunk line monitoring result indicates a trunk line fault, the first fault recorder is used to locate the fault in the trunk line and obtain the fuzzy fault location in the trunk line.

[0085] Specifically, when the trunk line monitoring result indicates a trunk line fault, the first fault recorder installed at the beginning of the trunk line can be used to locate the fault. The specific implementation logic at this time is the same as the specific implementation logic in S205, and will not be repeated here. Thus, the fault location range can be obtained, that is, the fuzzy fault location of the trunk line.

[0086] S210. Using drones to accurately locate faults on main roads based on fuzzy fault locations, the fault location on the main road is obtained.

[0087] Specifically, the implementation process and technical principle of S210 and S140 are the same. Please refer to the detailed description of S140 above, which will not be repeated here.

[0088] Optionally, after using drones to precisely locate the fault position based on the fuzzy location of the main line, a main line early warning information can be generated based on the fault position. This information is then displayed in the distribution area line layout model to remind maintenance personnel to proceed to the fault location for repair. Specifically, the main line can be highlighted in the model, and the fault location can be flashed. Simultaneously, the warning information can be displayed via pop-up windows on electronic devices, sound alerts, SMS messages, or emails to promptly remind maintenance personnel to proceed to the fault location for repair. The warning information can include the fault location, fault type, and repair suggestions. Displaying the main line early warning information in the distribution area line layout model allows maintenance personnel to quickly and accurately locate the fault point, avoiding blind searching across a large area of ​​the power grid. It also provides maintenance personnel with initial repair ideas, thereby saving repair time, improving fault repair efficiency and quality, and effectively reducing fault recurrence caused by improper repairs.

[0089] Optionally, after maintenance personnel repair the fault location on the main line, they can issue a continue monitoring command to the electronic equipment to notify it to continue monitoring the power grid in the distribution area and locate the fault location. That is, when the electronic equipment receives the continue monitoring command issued by the maintenance personnel, it can repeat steps S201 to S203 and execute corresponding steps according to the branch monitoring results. Specifically, when the branch monitoring result of a branch is a branch fault, steps S204 to S206 are executed; when the branch monitoring result of all branches is normal, steps S207 to S210 are executed.

[0090] The technical solution of this application embodiment can acquire first electrical data monitored by a first monitoring device, second electrical data monitored by a second monitoring device, and third electrical data monitored by a third monitoring device. Based on the second and third electrical data, the branch monitoring result of the corresponding branch is determined. Then, when the branch monitoring result indicates a branch fault, the branch corresponding to the monitoring result is identified as the faulty branch. Next, a second fault recorder corresponding to the faulty branch is used to locate the faulty branch, obtaining a vague fault location. A drone is then used to perform precise location based on the vague fault location, obtaining the branch fault location. By using a low-cost second fault recorder for preliminary location of the faulty branch, the accuracy and efficiency of determining the vague fault location can be improved, thereby narrowing the fault range from the entire branch to a relatively small area. This avoids maintenance personnel blindly searching the entire branch, greatly reducing the time and effort required for fault location. Further precise searching using a drone improves the accuracy and efficiency of determining the branch fault location, thereby improving the accuracy and efficiency of fault location in the power grid area and increasing the speed of power restoration, meeting the need for rapid power restoration.

[0091] When all branch monitoring results indicate normal operation, the trunk monitoring result is determined based on the first and second electrical data. If the trunk monitoring result indicates a fault, a first fault recorder is used to locate the fault in the trunk, obtaining a vague fault location. Using a low-cost first fault recorder for preliminary trunk location reduces implementation complexity and improves computational efficiency, thereby increasing the accuracy and efficiency of determining the vague fault location and providing accurate data support for subsequent trunk fault location determination. Subsequently, a drone is used to perform precise location based on the vague fault location in the trunk, obtaining the trunk fault location. This improves the accuracy and efficiency of trunk fault location determination, thereby increasing the accuracy and efficiency of fault location in the power grid area and accelerating power restoration, meeting the need for rapid power restoration.

[0092] Figure 3 This is a schematic diagram of a fault location device for a distribution network provided in an embodiment of this application, with reference to... Figure 3 The fault location device for this power grid area may include:

[0093] The acquisition module 310 is used to acquire the first electrical data monitored by the first monitoring device, the second electrical data monitored by the second monitoring device, and the third electrical data monitored by the third monitoring device;

[0094] The determination module 320 is used to determine the branch monitoring result of the corresponding branch based on the second electrical data and the third electrical data, and when the branch monitoring result of all branches is normal, determine the trunk monitoring result based on the first electrical data and the second electrical data.

[0095] The fuzzy positioning module 330 is used to locate faults on the trunk road based on the trunk road monitoring results, and obtain the fuzzy fault location on the trunk road.

[0096] The precise positioning module 340 is used to accurately locate the fault on the main road by using a drone based on the fuzzy fault location of the main road.

[0097] In one embodiment, the first monitoring device includes a first fault recorder and a first smart meter, the second monitoring device includes a second fault recorder and a second smart meter, and the third monitoring device includes a third smart meter.

[0098] In one embodiment, the fuzzy positioning module 330 is specifically used to: when the trunk monitoring result is a trunk fault, use the first fault recorder to locate the fault in the trunk and obtain the fuzzy fault location of the trunk.

[0099] In one embodiment, the fault location device of the power grid in the distribution area further includes a branch fault location module. The branch fault location module is specifically used for: after determining the branch monitoring result of the corresponding branch based on the second electrical data and the third electrical data, when the branch monitoring result is a branch fault, determining the branch corresponding to the branch monitoring result as the faulty branch; using the second fault recorder corresponding to the faulty branch to locate the faulty branch and obtain the fuzzy fault location of the branch; and using a UAV to perform precise location based on the fuzzy fault location of the branch to obtain the branch fault location.

[0100] In one embodiment, the determining module 320 determines the branch monitoring result of the corresponding branch based on the second electrical data and the third electrical data, including: for the current branch in at least one branch, calculating the sum of the third electrical data of each end of the current branch to obtain the end electrical data; calculating the difference between the second electrical data and the end electrical data of the current branch to obtain a first difference value, and determining the absolute value of the first difference value as the first actual loss value of the current branch; when the first actual loss value is greater than the preset branch loss value, determining the branch monitoring result of the current branch as a branch fault; when the first actual loss value is not greater than the preset branch loss value, determining the branch monitoring result of the current branch as a normal branch.

[0101] In one embodiment, the determining module 320 determines the trunk line monitoring result based on the first electrical data and the second electrical data, including: calculating the sum of the second electrical data of at least one branch line to obtain the branch line electrical data; calculating the difference between the branch line electrical data and the first electrical data to obtain a second difference value, and determining the absolute value of the second difference value as a second actual loss value; when the second actual loss value is greater than a preset trunk line loss value, determining the trunk line monitoring result as a trunk line fault; when the second actual loss value is not greater than the preset trunk line loss value, determining the trunk line monitoring result as a trunk line normal.

[0102] In one embodiment, the fault location device for the power grid in the distribution area further includes a model building module. The model building module is specifically used to: acquire a point cloud dataset obtained by the UAV from the power grid in the distribution area before acquiring the first electrical data monitored by the first monitoring device, the second electrical data monitored by the second monitoring device, and the third electrical data monitored by the third monitoring device; build a distribution area line laying model of the power grid in the distribution area based on the point cloud dataset, and distinguish and display the trunk lines and branch lines in the distribution area line laying model.

[0103] The fault location device for the power grid in this area also includes an early warning display module. The early warning display module is specifically used to: after using a drone to accurately locate the fault based on the fuzzy location of the trunk line, generate trunk line early warning information based on the trunk line fault location; and display the trunk line early warning information in the line laying model of the area to remind maintenance personnel to go to the trunk line fault location for repair.

[0104] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is merely an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the functional modules described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0105] The fault location device for the power grid provided in this embodiment can be applied to the fault location method for the power grid provided in any of the above embodiments, and has the corresponding functions and beneficial effects.

[0106] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 A block diagram is shown of an exemplary electronic device 11 suitable for implementing embodiments of the present application. Figure 4 The electronic device 11 shown is merely an example and should not impose any limitations on the functionality and scope of use of this embodiment.

[0107] like Figure 4As shown, the electronic device 11 is represented in the form of a general-purpose computing electronic device. The components of the electronic device 11 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0108] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0109] Electronic device 11 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 11, including volatile and non-volatile media, removable and non-removable media.

[0110] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Electronic device 11 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (… Figure 4 Not shown; usually referred to as a "hard drive"). Although Figure 4 As not shown, disk drives for reading and writing to removable non-volatile disks (e.g., "floppy disks") and optical disc drives for reading and writing to removable non-volatile optical discs (e.g., CD-ROMs, DVD-ROMs, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0111] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of this application.

[0112] Electronic device 11 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with electronic device 11, and / or with any device that enables electronic device 11 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, electronic device 11 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20.

[0113] like Figure 4 As shown, network adapter 20 communicates with other modules of electronic device 11 via bus 18. It should be understood that, although... Figure 4 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 11, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0114] The processing unit 16 executes various functional applications and page displays by running programs stored in the system memory 28, such as implementing a fault location method for a power grid in a distribution area provided in any embodiment of this application.

[0115] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements, for example, a fault location method for a distribution network provided in any embodiment of this application.

[0116] The computer storage medium of this embodiment can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0117] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0118] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0119] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0120] Those skilled in the art will understand that the modules or steps described above in this application can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device, or fabricating them separately as individual integrated circuit modules, or fabricating multiple modules or steps as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0121] Furthermore, the acquisition, storage, use, and processing of data in this application's technical solution all comply with relevant national laws and regulations.

[0122] Note that the above are merely preferred embodiments and the technical principles employed in this application. Those skilled in the art will understand that this application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments. Many other equivalent embodiments may be included without departing from the inventive concept of this application, and the scope of this application is determined by the scope of the appended claims.

Claims

1. A fault location method for a transformer substation power grid, the transformer substation power grid comprising a main line and at least one branch line, characterized in that, A first monitoring device is installed at the starting end of the trunk road, a second monitoring device is installed at the starting end of each branch road, and a third monitoring device is installed at each end of each branch road. The method includes: Acquire the first electrical data monitored by the first monitoring device, the second electrical data monitored by the second monitoring device, and the third electrical data monitored by the third monitoring device; The branch monitoring results of the corresponding branch are determined based on the second electrical data and the third electrical data, and when the branch monitoring results of all branches are normal, the trunk monitoring results are determined based on the first electrical data and the second electrical data. Based on the trunk road monitoring results, the trunk road fault location is determined to obtain the fuzzy fault location. The location of the fault on the main road is obtained by using a drone to accurately locate the fault based on the fuzzy fault location on the main road. The second monitoring device includes a second fault recorder. When the branch monitoring result indicates a branch fault, the branch corresponding to the monitoring result is identified as the faulty branch. The device extracts the change characteristics of the second electrical data during the fault occurrence period from the second fault recorder installed at the beginning of the faulty branch. The fault occurrence time is determined based on the change characteristics of the second electrical data. The impedance per unit length of the line is calculated based on the line parameter information of the faulty branch, and a first distance between the fault point and the second fault recorder is determined based on the impedance per unit length and the change characteristics recorded by the second fault recorder at the fault occurrence time. A fault distance interval is determined based on the first distance and a set error threshold, and the fuzzy fault location of the branch is determined based on the fault distance interval. A drone is used to perform precise positioning based on the fuzzy fault location of the branch to obtain the branch fault location.

2. The fault location method for a distribution network according to claim 1, characterized in that, The first monitoring device includes a first fault recorder and a first smart meter; the second monitoring device further includes a second smart meter; and the third monitoring device includes a third smart meter.

3. The fault location method for a distribution network according to claim 2, characterized in that, The step of locating trunk road faults based on the trunk road monitoring results to obtain fuzzy fault locations on the trunk road includes: When the trunk line monitoring result indicates a trunk line fault, the first fault recorder is used to locate the fault in the trunk line and obtain the fuzzy fault location of the trunk line.

4. The fault location method for a distribution network according to claim 1, characterized in that, The step of determining the branch monitoring result of the corresponding branch based on the second electrical data and the third electrical data includes: For the current branch in the at least one branch, calculate the sum of the third electrical data of each end of the current branch to obtain the end electrical data; The difference between the second electrical data of the current branch and the electrical data of the end is calculated to obtain a first difference value, and the absolute value of the first difference value is determined as the first actual loss value of the current branch. When the first actual loss value is greater than the preset branch loss value, the branch monitoring result of the current branch is determined to be a branch fault; When the first actual loss value is not greater than the preset branch loss value, the branch monitoring result of the current branch is determined to be normal.

5. The fault location method for a distribution network according to claim 1, characterized in that, The step of determining the trunk line monitoring result based on the first electrical data and the second electrical data includes: The branch electrical data is obtained by summing the second electrical data of at least one branch; The difference between the branch electrical data and the first electrical data is calculated to obtain a second difference value, and the absolute value of the second difference value is determined as the second actual loss value; When the second actual loss value is greater than the preset trunk road loss value, the trunk road monitoring result is determined to be a trunk road fault; When the second actual loss value is not greater than the preset trunk road loss value, the trunk road monitoring result is determined to be normal.

6. The fault location method for a distribution network according to claim 1, characterized in that, Before acquiring the first electrical data monitored by the first monitoring device, the second electrical data monitored by the second monitoring device, and the third electrical data monitored by the third monitoring device, the method further includes: Obtain the point cloud dataset obtained by the UAV from the power grid of the transformer substation; Based on the point cloud dataset, a distribution area power grid line laying model is established, and the trunk lines and branch lines in the distribution area line laying model are distinguished and displayed. After using a drone to accurately locate the fault position on the main road based on the fuzzy fault location, the process also includes: Generate trunk road early warning information based on the location of the trunk road fault; The trunk line laying model is displayed to remind maintenance personnel to go to the fault location of the trunk line for repair.

7. A fault location device for a transformer substation power grid, the transformer substation power grid comprising a main line and at least one branch line, characterized in that, A first monitoring device is installed at the starting end of the main road, a second monitoring device is installed at the starting end of each branch road, and a third monitoring device is installed at each end of each branch road. The device includes: The acquisition module is used to acquire the first electrical data monitored by the first monitoring device, the second electrical data monitored by the second monitoring device, and the third electrical data monitored by the third monitoring device; The determination module is used to determine the branch monitoring result of the corresponding branch based on the second electrical data and the third electrical data, and when the branch monitoring result of all branches is normal, determine the trunk monitoring result based on the first electrical data and the second electrical data; The fuzzy positioning module is used to locate trunk road faults based on the trunk road monitoring results, and obtain the fuzzy fault location of the trunk road. The precise positioning module is used to use a drone to perform precise positioning based on the ambiguous fault location of the main road, thereby obtaining the fault location of the main road. The second monitoring device includes a second fault recorder. The device also includes a branch fault location module, used to identify the branch corresponding to the branch monitoring result as the faulty branch when the branch monitoring result indicates a branch fault; extract the change characteristic information of the second electrical data during the fault occurrence period from the second fault recorder installed at the starting end of the faulty branch; determine the fault occurrence time based on the change characteristic information of the second electrical data; calculate the impedance per unit length of the line according to the line parameter information of the faulty branch, and determine the first distance between the fault point and the second fault recorder based on the impedance per unit length and the change characteristic information recorded by the second fault recorder at the fault occurrence time; determine the fault distance interval based on the first distance and a set error threshold, and determine the fuzzy fault location of the branch according to the fault distance interval; and use a drone to perform precise positioning based on the fuzzy fault location of the branch to obtain the branch fault location.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the fault location method for the distribution network as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the fault location method for the distribution network as described in any one of claims 1 to 6.

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