Power grid line fault inspection method, terminal and storage medium

Abstract

The present invention relates to the technical field of power grid, and specifically provides a grid line fault inspection method, terminal and storage medium, including: establishing a communication connection with a sensor deployed on the grid line through the Internet of Things, and establishing a communication connection with a relay deployed on the grid line through the Internet of Things; obtaining monitoring data collected by the sensor through the Internet of Things, the monitoring data including voltage, current and temperature, identifying abnormal monitoring data based on the monitoring data and a preset data threshold; locating the fault location based on the target sensor to which the abnormal monitoring data belongs, and sending a disconnection instruction to the relay upstream of the fault location through the Internet of Things; identifying the fault type based on the abnormal monitoring data and a preset fault identification rule; generating a drone inspection route based on the fault type and fault location, and sending the drone inspection route to the corresponding drone. The present invention improves the efficiency of grid line fault inspection.

Description

Technical Field

[0001] The present invention belongs to the technical field of power supply network, and in particular relates to a network line fault inspection method, a terminal and a storage medium. Background Art

[0002] The mileage of power grid lines is getting longer and longer as they develop day by day, crossing various complex terrains. With fewer and fewer front-line operation and maintenance personnel, the operation and maintenance pressure on front-line operation and maintenance personnel is increasing.

[0003] Most of the existing power grid fault monitoring methods use machine learning for fault identification. For example, the monitoring data of multiple target devices in the converter substation are obtained at the same time; the monitoring data of multiple target devices are detected for anomalies to obtain target abnormal monitoring data; the target abnormal monitoring data is input into the trained prediction model to obtain the prediction result output by the prediction model, and the prediction result represents the probability that the target device corresponding to the target abnormal monitoring data has a fault.

[0004] Although this fault detection method can discover hidden dangers in power grid operation, machine learning requires a large number of data samples, is time-consuming and labor-intensive, and may result in false alarms.

[0005] In addition, the existing power grid fault monitoring method only realizes the evaluation of the power grid operation status, and cannot combine power grid monitoring with power grid maintenance. When a power grid fault occurs, the response is not timely. Summary of the invention

[0006] In view of the above-mentioned deficiencies in the prior art, the present invention provides a power grid line fault inspection method, a terminal and a storage medium to solve the above-mentioned technical problems.

[0007] In a first aspect, the present invention provides a method for inspecting a power grid line fault, comprising:

[0008] Establishing communication connection with sensors deployed on power grid lines through the Internet of Things, and establishing communication connection with relays deployed on power grid lines through the Internet of Things;

[0009] Acquire monitoring data collected by sensors through the Internet of Things, the monitoring data including voltage, current and temperature, and identify abnormal monitoring data based on the monitoring data and a pre-set data threshold;

[0010] The fault location is located based on the target sensor to which the abnormal monitoring data belongs, and a disconnection instruction is sent to the relay upstream of the fault location through the Internet of Things;

[0011] Identify the fault type based on the abnormal monitoring data and the preset fault identification rules;

[0012] A drone inspection route is generated based on the fault type and fault location, and the drone inspection route is sent to the corresponding drone.

[0013] In an optional embodiment, establishing a communication connection with a sensor deployed on a power grid line through the Internet of Things, and establishing a communication connection with a relay deployed on the power grid line through the Internet of Things, includes:

[0014] Preliminarily constructing a power grid topology based on the geographical locations of nodes of the power grid and the connection relationships between the nodes, wherein the nodes include substation equipment, distribution equipment and relays;

[0015] Based on the actual position of the sensor in the power grid line, adding a sensor identifier at a corresponding position in the power grid topology, the sensor including a voltage sensor, a current sensor and a temperature sensor;

[0016] Establish communication connections with sensors and relays through the Internet of Things, and assign unique IDs to each sensor and each relay.

[0017] In an optional embodiment, monitoring data collected by a sensor is acquired through the Internet of Things, the monitoring data including voltage, current and temperature, and abnormal monitoring data is identified based on the monitoring data and a preset data threshold, including:

[0018] Pre-set data thresholds corresponding to different monitoring data items;

[0019] Count the monitoring data items of the sensors, and divide the sensors with the same corresponding data thresholds into the same monitoring group;

[0020] Creating a message queue for the monitoring group, caching the monitoring data of the sensors in the monitoring group into the message queue, wherein the monitoring data carries the ID of the sensor;

[0021] A data processing thread is created for the message queue, and the data processing thread extracts the cached monitoring data from the message queue and marks the monitoring data exceeding the corresponding data threshold as abnormal monitoring data.

[0022] In an optional implementation, creating a message queue for the monitoring group and caching the monitoring data of the sensors in the monitoring group into the message queue includes:

[0023] Create a RabbitMQ client at the data sending end of the sensor;

[0024] Create a switch in RabbitMQ, configure the bound data sending address as a message producer in the switch, and configure the thread as a message consumer;

[0025] A queue is created, where the queue is used to cache monitoring data.

[0026] In an optional implementation, the fault location is located based on the target sensor to which the abnormal monitoring data belongs, and a disconnection instruction is sent to a relay upstream of the fault location through the Internet of Things, including:

[0027] Get the ID of the target sensor;

[0028] Locating a target sensor identifier in the power grid topology based on the ID of the target sensor;

[0029] An upstream relay closest to the target sensor identifier is selected from the power grid topology.

[0030] In an optional implementation, the fault location is located based on the target sensor to which the abnormal monitoring data belongs, and a disconnection instruction is sent to a relay upstream of the fault location through the Internet of Things, including:

[0031] If there are multiple target sensors, obtaining multiple target sensor identifiers corresponding to the multiple target sensors in the power grid topology;

[0032] The target sensors in the same branch are classified into the same sensor group;

[0033] Filter out the most upstream target sensor identifier in the sensor group, use the adjacent upstream relay of the most upstream target sensor identifier as the target relay corresponding to the sensor group, and send a disconnection instruction to the target relay;

[0034] Iterate over all sensor groups.

[0035] In an optional implementation, identifying the fault type based on the abnormal monitoring data and a preset fault identification rule includes:

[0036] If the abnormal monitoring data is current, the previous current value of the target sensor is obtained. If the difference between the current value and the previous current value exceeds the set fluctuation threshold, the fault type is determined to be a short circuit. If the difference between the current value and the previous current value does not exceed the set fluctuation threshold, the fault type is determined to be an overcurrent.

[0037] If the abnormal monitoring data is zero-sequence current, the fault type is determined to be a ground fault;

[0038] If the abnormal monitoring data is voltage, the fault type is determined to be voltage instability.

[0039] In an optional embodiment, generating a drone inspection route based on the fault type and the fault location, and sending the drone inspection route to a corresponding drone, includes:

[0040] Pre-set the inspection range corresponding to the fault type. The inspection range of ground fault is the fault location point. The inspection range of short circuit and voltage instability includes the branch where the fault location is located. The inspection range of overcurrent includes the downstream power equipment of the fault location.

[0041] Identify the target drone nest closest to the fault location, and randomly select a drone from the target drone nest as the target drone;

[0042] Based on the fault location, the inspection range corresponding to the fault type, and the location of the drone nest, the shortest path algorithm is used to generate an inspection route for the target drone;

[0043] The inspection route is sent to the target UAV.

[0044] In a second aspect, a terminal is provided, including:

[0045] processor, memory, wherein:

[0046] The memory is used to store computer programs.

[0047] The processor is used to call and run the computer program from the memory, so that the terminal executes the above-mentioned terminal method.

[0048] According to a third aspect, a computer storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed on a computer, the computer executes the methods described in the above aspects.

[0049] The beneficial effect of the present invention lies in that the power grid line fault inspection method, terminal and storage medium provided by the present invention build a communication foundation for power grid monitoring and maintenance through the Internet of Things, and further combine with drone control to realize automatic inspection of fault lines, thereby improving the efficiency of power grid line fault inspection.

[0050] In addition, the invention has a reliable design principle, a simple structure and a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0052] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention.

[0053] Figure 2 A schematic diagram of the structure of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0056] Figure 1 The following is a schematic flow chart of a method according to an embodiment of the present invention. According to different requirements, the order of the steps in the flow chart can be changed, and some steps can be omitted.

[0057] like Figure 1 As shown, the method includes:

[0058] Step 110, establishing a communication connection with a sensor deployed on a power grid line through the Internet of Things, and establishing a communication connection with a relay deployed on the power grid line through the Internet of Things;

[0059] Step 120, acquiring monitoring data collected by sensors through the Internet of Things, the monitoring data including voltage, current and temperature, and identifying abnormal monitoring data based on the monitoring data and a preset data threshold;

[0060] Step 130, locating the fault location based on the target sensor to which the abnormal monitoring data belongs, and sending a disconnection instruction to the relay upstream of the fault location through the Internet of Things;

[0061] Step 140, identifying the fault type based on the abnormal monitoring data and a preset fault identification rule;

[0062] Step 150: Generate a drone inspection route based on the fault type and the fault location, and send the drone inspection route to the corresponding drone.

[0063] To facilitate understanding of the present invention, the power grid line fault inspection method provided by the present invention is further described below based on the principle of the power grid line fault inspection method of the present invention and the process of monitoring and inspecting the power grid line in the embodiment.

[0064] Specifically, the power grid line fault inspection method includes:

[0065] S1. Establish a communication connection with sensors deployed on the power grid line through the Internet of Things, and establish a communication connection with relays deployed on the power grid line through the Internet of Things.

[0066] A power grid topology is constructed in advance based on the geographical locations of the nodes of the power grid and the connection relationships between the nodes, wherein the nodes include substations, distribution equipment and relays; sensor identifiers are added to the corresponding positions in the power grid topology based on the actual locations of the sensors in the power grid lines, wherein the sensors include voltage sensors, current sensors and temperature sensors; a communication connection with the sensors and relays is established through the Internet of Things, and a unique ID is assigned to each sensor and each relay.

[0067] First of all, the topology construction of the power grid is the basis of power system management, which can help intuitively understand the structure and layout of the power grid. Before building the power grid topology, it is necessary to collect detailed geographical location information of each node, including but not limited to substation equipment (such as transformers, substations), distribution equipment (such as switch cabinets, distribution boxes) and control equipment such as relays. The geographical location information of these nodes can be obtained through geographic information systems (GIS) or on-site surveys.

[0068] After obtaining the geographical location of the nodes, it is necessary to construct the topology of the power grid based on the connection relationship between the nodes. This usually includes high-voltage lines, low-voltage lines, and communication lines. By displaying these lines and nodes in a graphical way, the overall layout of the power grid and the connection relationship between each part can be clearly seen.

[0069] Next, you need to add sensor tags at the corresponding locations in the grid topology based on the actual locations of the sensors in the grid lines. These sensors are key devices for monitoring the operation status of the grid. They can collect key parameters such as voltage, current, and temperature in the grid in real time. When adding sensor tags, you need to ensure that each sensor corresponds to its actual location in the grid for subsequent data analysis and processing.

[0070] Specifically, sensors include voltage sensors, current sensors, and temperature sensors. Voltage sensors are used to monitor voltage changes in the power grid, current sensors are used to monitor the magnitude and direction of current in the power grid, and temperature sensors are used to monitor the temperature of power grid equipment. These sensors are usually installed at key nodes and lines of the power grid to ensure comprehensive monitoring of the power grid's operating status.

[0071] After adding the sensor ID, it is necessary to establish a communication connection with the sensor and relay through the Internet of Things technology. The Internet of Things technology allows various devices to be connected to the Internet to achieve remote transmission and processing of data. For the power grid, the Internet of Things technology can ensure real-time acquisition of power grid operation data and remote monitoring and control of it.

[0072] In order to achieve communication with sensors and relays, each sensor and each relay needs to be assigned a unique ID. These IDs are the identity of the device, and they can ensure that each device can be accurately identified when communicating with the device. At the same time, these IDs can also be used for remote management and maintenance of the device, making it easier to manage and control power grid equipment.

[0073] In short, by constructing the grid topology based on the geographical location of the nodes and the connection relationship between the nodes in advance, adding sensor identification to the grid topology, and establishing communication connections with sensors and relays, comprehensive monitoring and remote control of the grid operation status can be achieved. This is of great significance for improving the operation efficiency and safety of the grid.

[0074] S2. Acquire monitoring data collected by sensors through the Internet of Things, where the monitoring data includes voltage, current and temperature, and identify abnormal monitoring data based on the monitoring data and a pre-set data threshold.

[0075] (1) Pre-set data thresholds corresponding to different monitoring data items.

[0076] In power systems or any system that requires real-time monitoring, different monitoring data items (such as voltage, current, temperature, etc. of different equipment) have their specific safety or operating ranges. These ranges are usually determined based on equipment specifications, safety standards, or operating experience. In order to ensure the stable operation and safety of the system, data thresholds for these data items need to be set in advance. These thresholds usually include upper and lower limits to determine whether the data is within the normal range.

[0077] (2) Count the monitoring data items of the sensors and divide the sensors with the same corresponding data thresholds into the same monitoring group.

[0078] Multiple sensors monitor different data items or the same data items with different thresholds. To improve the efficiency and accuracy of data processing, sensors with the same data threshold can be grouped into the same monitoring group. This ensures that only sensor data with the same threshold is considered during data analysis and processing, thus simplifying the processing flow.

[0079] (3) Create a RabbitMQ client at the data sending end of the sensor; create a switch in RabbitMQ, configure the bound data sending address as a message producer in the switch, and configure the thread as a message consumer; create a queue, the queue is used to cache monitoring data; cache the monitoring data of the sensors in the monitoring group to the message queue, and the monitoring data carries the sensor ID;

[0080] RabbitMQ is a popular message broker and queue server that allows applications to communicate through a simple protocol. Here, we create a RabbitMQ client on the data sending side of the sensor to send monitoring data to the RabbitMQ server. Then, we create an exchanger to route messages from producers (i.e. sensors) to consumers (i.e. data processing threads). Next, we create one or more queues to cache monitoring data. These queues will be divided according to monitoring groups so that sensor data with the same threshold can be stored in the same queue. When sensors send data, they will be identified with their own ID so that the source of the data can be identified in subsequent processing.

[0081] (4) Creating a data processing thread for the message queue, wherein the data processing thread extracts the cached monitoring data from the message queue and marks the monitoring data exceeding the corresponding data threshold as abnormal monitoring data.

[0082] In order to ensure timely processing and analysis of large amounts of monitoring data, we need to create data processing threads for the message queue. These threads will extract cached monitoring data from the queue and check whether each data item exceeds its corresponding data threshold. If the data exceeds the threshold, it will be marked as abnormal monitoring data and may trigger an alarm or be recorded in the log. In this way, we can detect potential problems in the system in a timely manner and take appropriate measures to deal with them.

[0083] S3. Locate the fault location based on the target sensor to which the abnormal monitoring data belongs, and send a disconnection command to the relay upstream of the fault location through the Internet of Things.

[0084] When there is only one abnormal monitoring data, the ID of the corresponding target sensor is obtained; the target sensor identifier is located in the power grid topology based on the ID of the target sensor; and the upstream relay closest to the target sensor identifier is screened out from the power grid topology.

[0085] If there are multiple target sensors corresponding to multiple abnormal monitoring data, if multiple target sensor identifiers corresponding to the multiple target sensors in the power grid topology are obtained; the target sensor identifiers in the same branch are divided into the same sensor group; the most upstream target sensor identifier in the sensor group is screened out, and the adjacent upstream relay of the most upstream target sensor identifier is used as the target relay corresponding to the sensor group, and a disconnection instruction is sent to the target relay; all sensor groups are traversed.

[0086] Specifically, the following steps are included:

[0087] When there is only one abnormal monitoring data:

[0088] First, the system will obtain the ID of the sensor that generated the abnormal monitoring data. This ID is the unique identifier of the sensor in the system and is used to identify the specific sensor.

[0089] Based on the ID of the target sensor, the system will locate the corresponding target sensor ID in the pre-built power grid topology. The power grid topology is a graphical representation that clearly shows the connection relationship between various nodes (including sensors and relays) in the power grid.

[0090] Next, the system will select the upstream relay closest to the target sensor ID from the grid topology. This is because when a sensor detects abnormal data, it is likely due to a problem in a link upstream, such as unstable power supply or equipment failure. Therefore, controlling the nearest upstream relay to disconnect can quickly cut off the possible source of the fault and prevent the problem from further expanding.

[0091] If there are multiple target sensors corresponding to multiple abnormal monitoring data:

[0092] The system will obtain the IDs of multiple target sensors corresponding to these abnormal data and locate their respective target sensor identifiers in the power grid topology.

[0093] Next, the system analyzes the location relationship of these target sensors in the grid topology. If two or more target sensors are located on the same branch (i.e., they have a common upstream node), they will be classified into the same sensor group. This is because these sensors may be affected by the same fault source.

[0094] For each sensor group, the system will filter out the target sensor identification at the farthest upstream, because the sensor at the farthest upstream is closest to the fault source, and controlling its adjacent upstream relay to disconnect can most effectively cut off the fault source.

[0095] After determining the most upstream target sensors of each sensor group, the system sends disconnection commands to the adjacent upstream relays of these sensors. This can quickly isolate the possible fault source and protect other parts of the power grid from being affected.

[0096] Finally, the system goes through all sensor groups to ensure that each group has been properly addressed. If all anomalies are resolved, the system returns to normal operation; if anomalies still exist, the system continues to perform the above steps until all problems are resolved.

[0097] Through this process, the system can quickly locate the problem when an abnormal situation occurs in the power grid, and take effective measures to isolate and handle it to ensure the stable operation of the power grid.

[0098] S4. Identify the fault type based on the abnormal monitoring data and preset fault identification rules.

[0099] If the abnormal monitoring data is current, the previous current value of the target sensor is obtained. If the difference between the current value and the previous current value exceeds the set fluctuation threshold, the fault type is determined to be a short circuit. If the difference between the current value and the previous current value does not exceed the set fluctuation threshold, the fault type is determined to be an overcurrent. If the abnormal monitoring data is zero-sequence current, the fault type is determined to be a ground fault. If the abnormal monitoring data is voltage, the fault type is determined to be voltage instability.

[0100] Specifically, if the abnormal monitoring data is current:

[0101] First, the system obtains the previous current value of the target sensor, which is usually recorded under normal conditions and used to compare with the current current value.

[0102] Next, the system calculates the difference between the current value and the previous current value. This difference represents the change in current in a short period of time.

[0103] The system then compares the calculated difference with a pre-set current fluctuation threshold, which is set based on the current fluctuation range during normal grid operation.

[0104] If the difference between the current value and the previous current value exceeds the set fluctuation threshold, the system will determine the fault type as a short circuit. A short circuit usually causes a sudden increase in current, exceeding the normal range.

[0105] If the difference between the current value and the previous current value does not exceed the set fluctuation threshold, but the current value itself exceeds the set overcurrent threshold (a different threshold used to determine overcurrent conditions), the system will determine the fault type as overcurrent. Overcurrent may be caused by excessive load or equipment failure.

[0106] If the abnormal monitoring data is zero-sequence current:

[0107] The system will detect the presence of zero-sequence current. Zero-sequence current is generated when the three-phase current is unbalanced, and usually indicates some kind of asymmetric fault. After detecting the zero-sequence current, the system will directly determine the fault type as a ground fault. Ground fault is a common fault in power systems and may cause equipment damage or electric shock to personnel.

[0108] If the abnormal monitoring data is voltage:

[0109] The system will detect abnormal fluctuations in voltage or deviations from normal values. Voltage instability may manifest as voltage being too high, too low, or fluctuating too much. After detecting voltage anomalies, the system will directly determine the fault type as voltage instability. Voltage instability may be caused by power supply problems, load changes, or grid structure problems.

[0110] Through the above steps, the system can accurately determine the fault type according to different types of abnormal monitoring data, and take corresponding measures to deal with it, ensuring the stable operation of the power grid.

[0111] S5. Generate a drone inspection route based on the fault type and fault location, and send the drone inspection route to the corresponding drone.

[0112] (1) The inspection range corresponding to the fault type is pre-set. The inspection range of ground fault is the fault location point, the inspection range of short circuit and voltage instability includes the branch where the fault location is located, and the inspection range of overcurrent includes the downstream power equipment of the fault location.

[0113] In the power grid management system, pre-setting appropriate inspection scopes for different fault types is the key to ensuring timely and effective fault handling.

[0114] Ground fault: Since ground faults usually occur at a specific fault location, the inspection scope should be directed to that location to ensure that the fault can be directly inspected and repaired.

[0115] Short circuit and voltage instability: These two faults may affect the stable operation of the entire branch. Therefore, the inspection scope should cover the entire branch where the fault is located, including all key nodes and equipment on the branch.

[0116] Overcurrent: Overcurrent faults may be caused by excessive load or equipment failure of downstream power equipment. Therefore, the inspection scope should focus on the downstream power equipment at the fault location to ensure that potential problems can be discovered and resolved in a timely manner.

[0117] (2) Identify the target drone nest that is closest to the fault location and randomly select a drone from the target drone nest as the target drone.

[0118] In order to quickly respond and handle the fault, it is necessary to select the drone nest closest to the fault location.

[0119] Determine the fault location: First, determine the specific location of the fault based on the fault monitoring data.

[0120] Drone nest location matching: The system then quickly matches the drone nest closest to the fault location based on the grid topology and the geographical distribution of drone nests.

[0121] Drone selection: From the selected drone nest, the system will randomly select a drone as the target drone. This ensures the rational use of drone resources and reduces mission delays caused by the failure of a specific drone.

[0122] (3) Based on the fault location, the inspection range corresponding to the fault type, and the drone nest location, the shortest path algorithm is used to generate an inspection route for the target drone.

[0123] In order to guide the target UAV to complete the inspection task efficiently, it is necessary to generate a suitable inspection route for it.

[0124] Input parameters: The system takes the fault location, the inspection range corresponding to the fault type, and the drone nest location as input parameters.

[0125] Shortest path algorithm: Using the shortest path algorithm (such as Dijkstra algorithm), the system will calculate the shortest path from the drone nest to the fault location based on the input parameters, and consider the requirements of the inspection range to generate a complete inspection route.

[0126] Route optimization: When generating inspection routes, the system also takes into account factors such as the drone’s flight capabilities, battery life, obstacles, etc., and optimizes the route to ensure that the drone can complete its mission safely and efficiently.

[0127] (4) Sending the inspection route to the target UAV.

[0128] The system will send the generated inspection route to the target drone.

[0129] Communication connection: First, the system will establish a communication connection with the target drone to ensure real-time data transmission.

[0130] Route transmission: The system will then send detailed information of the inspection route (including starting point, end point, waypoints, flight altitude, speed, etc.) to the target drone.

[0131] Drone response: After receiving the inspection route, the target drone will confirm and prepare to execute the task. During the execution process, the drone will also send back the inspection data in real time for the system to analyze and make decisions.

[0132] Figure 2 The present invention provides a schematic diagram of the structure of a terminal 200 provided in an embodiment of the present invention. The terminal 200 can be used to execute the power grid line fault inspection method provided in an embodiment of the present invention.

[0133] The terminal 200 may include: a processor 210, a memory 220 and a communication unit 230. These components communicate via one or more buses. Those skilled in the art will appreciate that the server structure shown in the figure does not limit the present invention, and it may be a bus structure or a star structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0134] The memory 220 can be used to store the execution instructions of the processor 210, and the memory 220 can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. When the execution instructions in the memory 220 are executed by the processor 210, the terminal 200 can perform some or all of the steps in the following method embodiments.

[0135] The processor 210 is the control center of the storage terminal, and uses various interfaces and lines to connect various parts of the entire electronic terminal. It runs or executes software programs and / or modules stored in the memory 220, and calls data stored in the memory to perform various functions of the electronic terminal and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 210 can only include a central processing unit (CPU). In the embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.

[0136] The communication unit 230 is used to establish a communication channel so that the storage terminal can communicate with other terminals, receive user data sent by other terminals or send user data to other terminals.

[0137] The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and when the program is executed, the program may include some or all of the steps in each embodiment provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0138] Therefore, the present invention builds a communication foundation for power grid monitoring and maintenance through the Internet of Things, and further combines drone control to realize automatic inspection of faulty lines, thereby improving the efficiency of power grid line fault inspection. The technical effects that can be achieved by this embodiment can be found in the description above and will not be repeated here.

[0139] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention, in essence or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes, including several instructions for enabling a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention.

[0140] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

[0141] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or modules, which can be electrical, mechanical or other forms.

[0142] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0143] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0144] Although the present invention has been described in detail with reference to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person of ordinary skill in the art may easily think of changes or substitutions within the technical scope disclosed by the present invention, and these shall be within the scope of protection of the present invention.

Claims

1. A method for inspecting power line faults, characterized in that: include: Establishing communication connection with sensors deployed on power grid lines through the Internet of Things, and establishing communication connection with relays deployed on power grid lines through the Internet of Things; Acquire monitoring data collected by sensors through the Internet of Things, the monitoring data including voltage, current and temperature, and identify abnormal monitoring data based on the monitoring data and a pre-set data threshold; The fault location is located based on the target sensor to which the abnormal monitoring data belongs, and a disconnection instruction is sent to the relay upstream of the fault location through the Internet of Things; Identify the fault type based on the abnormal monitoring data and the preset fault identification rules; Generate a drone inspection route based on the fault type and fault location, and send the drone inspection route to the corresponding drone; Acquire monitoring data collected by sensors through the Internet of Things, the monitoring data including voltage, current and temperature, and identify abnormal monitoring data based on the monitoring data and a preset data threshold, including: Pre-set data thresholds corresponding to different monitoring data items; Count the monitoring data items of the sensors, and divide the sensors with the same corresponding data thresholds into the same monitoring group; Creating a message queue for the monitoring group, caching the monitoring data of the sensors in the monitoring group into the message queue, wherein the monitoring data carries the ID of the sensor; Creating a data processing thread for the message queue, the data processing thread extracting cached monitoring data from the message queue and marking monitoring data exceeding a corresponding data threshold as abnormal monitoring data; Creating a message queue for the monitoring group and caching the monitoring data of the sensors in the monitoring group into the message queue includes: Create a RabbitMQ client at the data sending end of the sensor; Create a switch in RabbitMQ, configure the bound data sending address as a message producer in the switch, and configure the thread as a message consumer; Creating a queue, wherein the queue is used to cache monitoring data; Identifying the fault type based on the abnormal monitoring data and the preset fault identification rules includes: If the abnormal monitoring data is current: Obtaining a previous current value of the target sensor, where the previous current value is recorded in a normal state and is used for comparison with the current current value; Calculate the difference between the current value and the previous current value. This difference represents the change in current in a short period of time. The calculated difference is compared with a preset current fluctuation threshold, which is set based on the current fluctuation range during normal operation of the power grid; If the difference between the current value and the previous current value exceeds the set fluctuation threshold, the fault type is determined to be a short circuit; If the difference between the current value and the previous current value does not exceed the set fluctuation threshold, but the current value itself exceeds the set overcurrent threshold, the fault type is determined to be overcurrent; If the abnormal monitoring data is zero-sequence current: The existence of zero-sequence current is detected. Zero-sequence current is generated when the three-phase current is unbalanced, indicating an asymmetrical fault. After the zero-sequence current is detected, the fault type is directly determined to be a ground fault. If the abnormal monitoring data is voltage: Abnormal voltage fluctuations or deviations from normal values ​​are detected. Voltage instability is manifested as voltage being too high, too low, or fluctuating too much. After detecting voltage abnormalities, the fault type is directly determined to be voltage instability. Generate a drone inspection route based on the fault type and fault location, and send the drone inspection route to the corresponding drone, including: Since ground faults usually occur at a specific fault location, the inspection scope should be directed to that location to ensure that the fault can be directly inspected and repaired; For short circuit and voltage instability, these two faults may affect the stable operation of the entire branch. Therefore, the inspection scope covers the entire branch where the fault is located, including all key nodes and equipment on the branch. The fault factors of overcurrent fault include excessive load or equipment failure of downstream power equipment. Therefore, the inspection scope includes the downstream power equipment at the fault location. Identify the target drone nest closest to the fault location and randomly select drones from the target drone nest as target drones, including: Determine the specific location of the fault based on the fault monitoring data; According to the grid topology and the geographical distribution of drone nests, the drone nest closest to the fault location is matched; From the selected drone nest, a drone is randomly selected as the target drone; Based on the fault location, the inspection range corresponding to the fault type, and the drone nest location, the shortest path algorithm is used to generate an inspection route for the target drone, including: The fault location, the inspection range corresponding to the fault type, and the drone nest location are taken as input parameters; Using the shortest path algorithm, the shortest path from the drone nest to the fault location is calculated based on the input parameters, and the requirements of the inspection range are considered to generate a complete inspection route; When generating inspection routes, the flight capability, battery life, and obstacles of the drone are taken into consideration to optimize the routes; Send the inspection route to the target drone: Establish a communication link with the target drone to ensure real-time data transmission; Sending detailed information of the inspection route to the target drone, the detailed information including the starting point, the end point, the passing points, the flight altitude, and the speed; After receiving the inspection route, the target drone will confirm it and prepare to perform the mission. During the execution process, the drone will also send back the inspection data in real time for the system to analyze and make decisions.

2. The method according to claim 1, characterized in that Establishing communication connection with sensors deployed on power grid lines through the Internet of Things, and establishing communication connection with relays deployed on power grid lines through the Internet of Things, including: Preliminarily constructing a power grid topology based on the geographical locations of nodes of the power grid and the connection relationships between the nodes, wherein the nodes include substation equipment, distribution equipment and relays; Based on the actual position of the sensor in the power grid line, adding a sensor identifier at a corresponding position in the power grid topology, the sensor including a voltage sensor, a current sensor and a temperature sensor; Establish communication connections with sensors and relays through the Internet of Things, and assign unique IDs to each sensor and each relay.

3. The method according to claim 2, characterized in that The fault location is located based on the target sensor to which the abnormal monitoring data belongs, and a disconnection instruction is sent to the relay upstream of the fault location through the Internet of Things, including: Get the ID of the target sensor; Locating a target sensor identifier in the power grid topology based on the ID of the target sensor; An upstream relay closest to the target sensor identifier is selected from the power grid topology.

4. The method according to claim 2, characterized in that: The fault location is located based on the target sensor to which the abnormal monitoring data belongs, and a disconnection instruction is sent to the relay upstream of the fault location through the Internet of Things, including: If there are multiple target sensors, obtaining multiple target sensor identifiers corresponding to the multiple target sensors in the power grid topology; The target sensors in the same branch are classified into the same sensor group; Filter out the most upstream target sensor identifier in the sensor group, use the adjacent upstream relay of the most upstream target sensor identifier as the target relay corresponding to the sensor group, and send a disconnection instruction to the target relay; Iterate over all sensor groups.

5. A terminal, characterized in that: include: A memory, used for storing a power grid line fault inspection program; A processor is used to implement the steps of the power grid line fault inspection method according to any one of claims 1 to 4 when executing the power grid line fault inspection program.

6. A computer-readable storage medium storing a computer program, characterized in that: The readable storage medium stores a power grid line fault inspection program, which, when executed by a processor, implements the steps of the power grid line fault inspection method according to any one of claims 1 to 4.

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