Line loss analysis method, device and equipment

Abstract

The application provides a line loss analysis method, device and equipment, and relates to the technical field of power system automation. The method comprises the following steps: acquiring topology detection data reported by a plurality of metering devices; generating a physical topology structure diagram of a to-be-detected line based on the topology detection data; the to-be-detected line comprises a plurality of main lines and branch lines under each main line; each line in the physical topology structure diagram is provided with a metering device, which is used for representing the master-slave topology relationship of each metering device. Receiving power consumption data reported by each metering device. Generating a target internal resistance calculation model according to the physical topology structure diagram and the power consumption data; determining line loss data of the to-be-detected line according to the target internal resistance calculation model. The method is used to reduce the difficulty of detecting the load at the end of the power supply network.

Description

Technical Field

[0001] This application relates to the field of power system automation technology, and more specifically, to a method, apparatus and equipment for line loss analysis. Background Technology

[0002] Currently, at the end of the power supply network, there are usually no intelligent sensing methods or load analysis. User electricity consumption and line conditions are unknown and may only be noticed when an accident occurs. Therefore, it is necessary to detect the load conditions at the end of the power supply network.

[0003] In existing technologies, a large number of metering devices are typically installed to monitor the load at the end of the power supply network. However, for some older facilities or remote areas, this method involves significant investment, difficult construction, and challenging maintenance, making load monitoring at the end of the power supply network quite difficult. Summary of the Invention

[0004] The purpose of this application is to provide a line loss analysis method, apparatus, and device to solve the above-mentioned problems existing in the prior art, and to greatly reduce the difficulty of load detection at the end of the power supply network.

[0005] Firstly, a method for analyzing line loss is provided, which may include: The system acquires topology detection data reported by multiple metering devices; based on the topology detection data, it generates a physical topology diagram of the line to be tested; the line to be tested includes multiple main lines and branches under each main line; each line in the physical topology diagram is equipped with a metering device to characterize the master-slave topology relationship of each metering device. Receive electricity consumption data reported by each of the aforementioned metering devices; Based on the physical topology diagram and the power consumption data, a target internal resistance calculation model is generated; based on the target internal resistance calculation model, the line loss data of the line to be tested is determined.

[0006] Secondly, a line loss analysis device is provided, which may include: A generation module is used to acquire topology detection data reported by multiple metering devices; based on the topology detection data, a physical topology diagram of the line to be tested is generated; the line to be tested includes multiple main lines and branch lines under each main line; each line in the physical topology diagram is equipped with a metering device, which is used to characterize the master-slave topology relationship of each metering device; A receiving module is used to receive electricity consumption data reported by each of the metering devices; The determination module is used to generate a target internal resistance calculation model based on the physical topology diagram and the power consumption data; and to determine the line loss data of the line to be tested based on the target internal resistance calculation model.

[0007] Thirdly, a server is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.

[0008] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0009] This application provides a line loss analysis method, apparatus, and device that acquires topology detection data reported by multiple metering devices; based on the topology detection data, a physical topology diagram of the line to be tested is generated; the line to be tested includes multiple main lines and branch lines under each main line; each line in the physical topology diagram is equipped with a metering device to characterize the master-slave topology relationship of each metering device. The device receives electricity consumption data reported by each metering device. Based on the physical topology diagram and electricity consumption data, a target internal resistance calculation model is generated; based on the target internal resistance calculation model, the line loss data of the line to be tested is determined. In this solution, the metering device can quickly establish the power supply network topology, i.e., generate a physical topology diagram. It receives electricity consumption data reported by each metering device, generates a target internal resistance calculation model based on the physical topology diagram and electricity consumption data, and calculates the line loss data of the line to be tested based on the target internal resistance calculation model. Therefore, the metering device can be flexibly installed and removed, is convenient and quick, and can quickly establish the physical topology diagram of the line to be tested. Based on the physical topology diagram and the target internal resistance calculation model, the difficulty of load detection at the end of the power supply network can be greatly reduced. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A schematic flowchart of a line loss analysis method provided in an embodiment of this application; Figure 2 A flowchart illustrating yet another line loss analysis method provided in this application embodiment; Figure 3 This is a schematic diagram of the structure of a line loss analysis device provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a server provided in an embodiment of this application. Detailed Implementation

[0012] 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 a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art. The words "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are only used to distinguish different components. The words "comprising" or "including," etc., mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but do not exclude other elements or objects. The words "connected," "coupled," or "connected," etc., are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up," "down," "left," "right," etc., are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0013] Currently, at the end of the power supply network, there are usually no intelligent sensing methods or load analysis. User electricity consumption and line conditions are unknown and may only be noticed when an accident occurs. Therefore, it is necessary to detect the load conditions at the end of the power supply network.

[0014] In one example, a large number of metering devices are typically installed to monitor the load at the end of the power supply network. However, for some older facilities or remote areas, this method involves significant investment, difficult construction, and challenging maintenance, making load monitoring at the end of the power supply network quite difficult.

[0015] In one example, existing technologies rarely include control systems with topology network construction and line loss calculation capabilities. Instead, they are primarily customized maintenance systems for specific scenarios. These systems manage line loss through dedicated line loss calculation meters combined with upper-level computer prediction algorithms. However, customized maintenance systems suffer from the following problems: high design costs, long-term meter mounting, insensitivity to topology changes requiring redeployment by the operations and maintenance team, and high maintenance costs for a large number of meters over a long period.

[0016] The line loss analysis method provided in this application embodiment can be applied to a server, which can be a data processing master station. In this embodiment, the execution entity is described as the data processing master station.

[0017] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0018] Figure 1 This is a flowchart illustrating a line loss analysis method provided in an embodiment of this application. Figure 1 As shown, the method may include: Step S101: Obtain topology detection data reported by multiple metering devices; Based on the topology detection data, generate a physical topology diagram of the line to be tested; The line to be tested includes multiple main lines and branch lines under each main line; Each line in the physical topology diagram is equipped with a metering device to characterize the master-slave topology relationship of each metering device.

[0019] For example, metering devices equipped with switchable current transformers are installed as many as possible on the power supply line to be tested. The metering devices are used for sampling via current inductance. The line to be tested includes multiple main lines and branches below each main line. Each metering device injects a small current signal into the line to be tested using its own battery and collects the propagation path of this small current signal along the line to be tested, generating topology detection data, which is then sent to the data processing master station. The data processing master station acquires the topology detection data reported by multiple metering devices; based on the topology detection data, it generates a physical topology diagram of the line to be tested; wherein each line in the physical topology diagram is equipped with a metering device, representing the master-slave topology relationship of each metering device.

[0020] Step S102: Receive the electricity consumption data reported by each metering device.

[0021] For example, the system receives electricity consumption data reported by various metering devices, including current data and voltage data.

[0022] Step S103: Generate a target internal resistance calculation model based on the physical topology diagram and power consumption data; determine the line loss data of the line to be tested based on the target internal resistance calculation model.

[0023] For example, the target internal resistance calculation model refers to a model used to calculate the line resistance value of the line group between the master metering device and the slave metering device. The target internal resistance calculation model is generated based on the physical topology diagram, current data, and voltage data. Based on the target internal resistance calculation model, the line resistance value of each line group between the master and slave metering devices with a master-slave topology is calculated, and the line loss value of that line is calculated based on the line resistance value. The line loss values ​​of all groups of lines between the master and slave metering devices are summarized to obtain the line loss data of the line to be tested.

[0024] The method provided in this application embodiment acquires topology detection data reported by multiple metering devices; based on the topology detection data, a physical topology diagram of the line to be tested is generated; the line to be tested includes multiple main lines and branch lines under each main line; each line in the physical topology diagram is equipped with a metering device to characterize the master-slave topology relationship of each metering device. Electricity consumption data reported by each metering device is received. Based on the physical topology diagram and electricity consumption data, a target internal resistance calculation model is generated; based on the target internal resistance calculation model, the line loss data of the line to be tested is determined. In this solution, the power supply network topology can be quickly established through the metering devices, i.e., a physical topology diagram is generated. Electricity consumption data reported by each metering device is received, and a target internal resistance calculation model is generated based on the physical topology diagram and electricity consumption data; based on the target internal resistance calculation model, the line loss data of the line to be tested is calculated. Therefore, the metering devices can be flexibly installed and removed, are convenient and quick, and can quickly establish the physical topology diagram of the line to be tested. Based on the physical topology diagram and the target internal resistance calculation model, the difficulty of load detection at the end of the power supply network can be greatly reduced.

[0025] Figure 2 A flowchart illustrating another line loss analysis method provided in this application is shown below. Figure 2 As shown, in this embodiment... Figure 1 Based on the embodiments, the method is described in detail below, and the method includes: Step S201: Obtain topology detection data reported by multiple metering devices.

[0026] In one example, topology detection data is generated by each metering device injecting a small current signal into the line under test and collecting the propagation path of the small current signal along the line under test.

[0027] For example, this step is described in step S101, and will not be repeated here.

[0028] Step S202: Based on the topology detection data, generate a physical topology diagram of the line to be tested; the line to be tested includes multiple main lines and branches under each main line; each line in the physical topology diagram is equipped with a metering device to characterize the master-slave topology relationship of each metering device.

[0029] In one example, S202 includes: generating a preliminary topology diagram based on topology detection data; the preliminary topology diagram includes the connection relationships between various metering devices and the hierarchical relationships between main lines and branches; identifying line segments not covered by metering devices based on the preliminary topology diagram; outputting a replacement prompt message based on the line segments not covered by metering devices; if it is confirmed that topology detection data for line segments not covered by metering devices has been received, generating a physical topology diagram of the line to be detected; each line in the physical topology diagram is equipped with a metering device; the hierarchical relationships in the physical topology diagram represent the master-slave topology relationships between various metering devices.

[0030] For example, topology detection data includes the metering device ID, whether a signal was injected, the detected signal source ID, signal strength / time of arrival, timestamp, and terminal marker, etc.; the metering device ID is used to identify the metering device, whether a signal was injected indicates whether the metering device is acting as a signal injector; the detected signal source ID indicates which metering device injected the characteristic current signal; the signal strength / time of arrival is used to help determine the distance and direction of the connection; the timestamp indicates the timestamp of the acquisition time; and the terminal marker indicates the device's own type marker or location marker.

[0031] The data processing master station generates a preliminary topology diagram based on the topology detection data, which determines the physical connections and hierarchical structure of the entire power supply network. This preliminary topology diagram includes the connections between metering devices and the hierarchical relationships between main lines and branches. Based on the preliminary topology diagram and the topology detection data, it identifies line segments not covered by metering devices and outputs supplementary installation prompts for these segments. Users then install metering devices on the corresponding line segments according to the prompts. The installed metering devices generate topology detection data and send it to the data processing master station, ensuring that data from all subsequent lines has been collected by metering devices for at least a period of time, facilitating subsequent line loss analysis. If the data processing master station confirms receipt of topology detection data for line segments not covered by metering devices, it generates a physical topology diagram for the line to be tested. At this point, each line in the physical topology diagram is equipped with a metering device, and the hierarchical relationships in the diagram represent the master-slave topology relationships between the metering devices.

[0032] Optionally, for each main line with branches, metering devices on the main line and on the branches are automatically matched through topology identification. One main metering device can correspond to multiple slave metering devices. During topology matching, since different main metering devices use different wireless channels, after a slave metering device identifies the topology signal of the main metering device, it automatically configures its own wireless parameters to be the same as the main metering device to facilitate the reception of wireless signals and prevent co-channel interference at close range. Specifically, each metering device listens to an initial wireless channel by default after powering on. When any metering device detects a characteristic current signal injected by another metering device, it identifies the source device ID of the characteristic current signal and obtains the wireless channel configuration information of the main metering device from the wireless networking signal broadcast by the main metering device corresponding to that source device ID. Based on the wireless channel configuration information, the slave metering device automatically configures its own wireless communication parameters to be the same as the main metering device, thereby accessing the wireless communication group of the main metering device. This facilitates the wireless transmission of subsequent collected data and prevents co-channel interference between slave metering devices belonging to adjacent main metering devices.

[0033] Optionally, after the metering device collects a small current signal sent by another metering device, it adds a time stamp and a terminal stamp, and broadcasts it to the outside world via wireless communication, which can be broadcast to the data processing master station.

[0034] For example, firstly, metering devices are installed and topology detection signals are injected. Specifically, metering devices are installed at multiple nodes of the line to be tested. Each metering device is snapped onto the line using a switchable current transformer, without needing to disconnect the line or remove the insulation. After installation, each metering device, powered by its own battery, injects a characteristic current signal as a topology detection signal into the snapped-on line. The characteristic current signals injected by different metering devices have different frequency or coding characteristics to distinguish the signal source. The amplitude of this characteristic current signal is less than a preset threshold to avoid affecting the normal operation of the line. After injecting the topology detection signal, each metering device continuously monitors its location to see if it receives characteristic current signals injected by other metering devices. When one metering device detects a characteristic current signal injected by another metering device, it determines that an electrical connection path exists between the two and records the source device ID, signal strength, and arrival time of the detected characteristic current signal.

[0035] Second, each metering device reports topology detection data. Specifically, after completing signal injection and signal detection, each metering device generates topology detection data from the detection results. Topology detection data includes: the metering device's own device ID, an identifier indicating whether the metering device injected a characteristic current signal, a list of source device IDs for the characteristic current signal detected by the metering device, and the signal strength and arrival time corresponding to each source device ID. After adding time stamps and terminal markers to the topology detection data, each metering device reports the topology detection data to the data processing master station through its respective wireless communication module.

[0036] Third, the data processing master station generates a physical topology diagram. Specifically, the data processing master station receives topology detection data reported by multiple metering devices and performs the following operations based on the topology detection data: (1) Analyze the topology detection data of each device and extract the signal propagation pairing relationship between each metering device. Specifically: for any first metering device and second metering device, if the topology detection data reported by the first metering device contains the device ID of the second metering device and is recorded as a characteristic current signal injected by the second metering device, or the topology detection data reported by the second metering device contains the device ID of the first metering device and is recorded as a characteristic current signal injected by the first metering device, then it is determined that there is an electrical connection path between the first metering device and the second metering device.

[0037] (2) Based on all pairing relationships, the metering device that has been injected with the characteristic current signal and detected by the most other metering devices is designated as the backbone node; the metering device that has detected the characteristic current signal of the backbone node but has not been detected by the backbone node is designated as the first-level branch node; the metering device that has detected the characteristic current signal of the first-level branch node but has not been detected by the first-level branch node is designated as the second-level branch node, and so on, to determine the hierarchical connection relationship between each metering device and generate a tree-like physical topology diagram. The physical topology diagram includes the connection relationship between each metering device and the hierarchical relationship between the backbone and branches.

[0038] (3) Determine the master-slave correspondence between each metering device based on the hierarchical relationship in the physical topology diagram. Specifically, the metering device located at the upper level node is identified as the master metering device of the metering device at the lower level node, and the metering device located at the lower level node is identified as the slave metering device of the metering device at the upper level node.

[0039] Fourth, output the physical topology diagram. Specifically, the data processing master station stores the generated physical topology diagram in the basic database of the digital twin platform and outputs it to the human-computer interaction interface for operation and maintenance personnel to view and confirm.

[0040] Step S203: Receive the electricity consumption data reported by each metering device.

[0041] It receives electricity consumption data reported by various metering devices, including current and voltage data.

[0042] Step S204: Generate a target internal resistance calculation model based on the physical topology diagram and power consumption data.

[0043] In one example, S204 includes: determining the baseline resistance value between each group of master and slave metering devices based on the master-slave topology relationship and power consumption data in the physical topology diagram; selecting any branch in the physical topology diagram and obtaining the influencing factor information of that branch; generating a target internal resistance calculation model based on the influencing factor information, the baseline resistance value, and a preset theoretical calculation model of line internal resistance; wherein, the preset theoretical calculation model of line internal resistance is established based on the internal environmental factors, electrical parameters, and external environmental factors of the conductor.

[0044] In one example, the electricity consumption data includes the actual master voltage value of the master metering device, the actual slave voltage value of the slave metering device, and the actual slave current value. Based on the master-slave topology relationship in the physical topology diagram and the electricity consumption data, the baseline resistance value between each group of master-slave metering devices is determined, including: determining the baseline resistance value between each group of master-slave metering devices based on the master-slave topology relationship in the physical topology diagram, the actual master voltage value, the actual slave voltage value, and the actual slave current value.

[0045] In one example, the influencing factors include the actual internal environmental factors of the conductor, the actual electrical parameters, and the actual external environmental factors. Based on the electricity consumption data, the baseline resistance value, and the preset theoretical calculation model of the line resistance, a target internal resistance calculation model is generated, including: inputting the actual internal environmental factors of the conductor, the actual electrical parameters, and the actual external environmental factors into the preset theoretical calculation model of the line resistance, and outputting the theoretical resistance value between the master and slave metering devices; determining the difference between the baseline resistance value and the theoretical resistance value; and generating the target internal resistance calculation model based on the difference and the theoretical resistance value.

[0046] For example, the electricity consumption data includes the actual main voltage value of the main metering device, the actual slave voltage value of the slave metering device, and the actual slave current value. Based on the reported electricity consumption data and the master-slave topology in the physical topology diagram, the master station calculates the baseline resistance R0 between each main metering device and slave metering device, level by level, starting from the bottom layer.

[0047] Where R0 is the baseline resistance between the main metering device and the slave metering device, i.e., the calculated actual line group value, U1 is the voltage (RMS value) of the main metering device, U2 is the voltage (RMS value) of the slave metering device, and I2 is the current (RMS value) of the slave metering device.

[0048] Then, a full life-cycle data model of the circuit materials is established. Specifically, considering all factors that may affect the internal resistance of the conductor, the influencing factors are categorized as follows: (1) Internal environmental factors: service time / years of conductor t line The temperature T of the conductor during operation line ; (2) Electrical parameters: current I, power factor, harmonic content, etc.; (3) External environmental factors: ambient temperature T env Average humidity / precipitation, deformation factor S line (Caused by external vibrations, thermal expansion and contraction, etc.)

[0049] In summary, the theoretical calculation model for line internal resistance R 理论 It can be represented as: R 理论 =f(N 内 N 电 N 外 ) Where, N 内 Indicates internal environmental factors; N 电 Indicates electrical parameters; N 外 External environmental factors are represented; the theoretical calculation model of line internal resistance can be corrected through actual experimental measurements or through big data algorithms such as neural networks, and there is no limitation on this.

[0050] From any layer of the topology, select a branch and obtain its actual conductor internal environmental factors, actual electrical parameters, and actual external environmental factors. For example, a branch with a long distance, heavy load, or harsh environment can be selected. Input the actual conductor internal environmental factors, actual electrical parameters, and actual external environmental factors of this branch into a preset line resistance theoretical calculation model, and output the theoretical resistance value between the master and slave metering devices. Calculate the difference 'c' between the baseline resistance value and the theoretical resistance value. Based on this difference 'c' and the theoretical resistance value, correct the line resistance theoretical calculation model to obtain the target internal resistance calculation model. R 实际 : R 实际 =f(R 理论 )+c The data processing master station can distribute updated target internal resistance calculation models to each metering device via a communication module. After the metering device completes the measurement, it can be disassembled and used for building line loss models in other locations.

[0051] Step S205: Determine the line loss data of the line to be tested based on the target internal resistance calculation model.

[0052] In one example, the electricity consumption data includes the actual current value from the metering device; step S205 includes: obtaining the hierarchical relationship information of each level in the physical topology diagram; based on the hierarchical relationship information, starting from the lowest-level slave metering device, performing the following operations layer by layer upwards for each group of metering devices with master-slave topology relationships: obtaining the predicted current value of the line segment corresponding to the slave metering device in the group; inputting the predicted current value into the target internal resistance calculation model to obtain the current line resistance value of the line segment between the master metering device and the corresponding slave metering device in the group; determining the line loss value of the line segment based on the predicted current value and the current line resistance value; summarizing the line loss values ​​of each line segment and the corresponding line segment location information to generate the line loss data of the line to be tested.

[0053] For example, the hierarchical relationship information of each level in the physical topology diagram is obtained. Based on the hierarchical relationship information, starting from the slave metering device at the bottom layer, the following operations are performed layer by layer upwards for each group of master-slave metering devices with a master-slave topology relationship: The predicted current value of the corresponding line segment of the slave metering device in that group is obtained; the predicted current value is input into the target internal resistance calculation model to obtain the current line resistance value of the line segment between the master metering device and the corresponding slave metering device in that group; the line loss value of the line segment is determined based on the predicted current value and the current line resistance value. Wherein, when the slave metering device is located in the middle layer of the topology, the predicted current value is the predicted current of the branch where the slave metering device is located, and the predicted current value is equal to the sum of the predicted currents of all branches below the branch where it is located. The line loss values ​​of each line segment and the corresponding line segment location information are summarized to generate the line loss data of the line to be tested.

[0054] Optionally, during the modeling and calibration phase, each metering device is mounted on the line under test to collect and report historical current data for each line segment in real time. The data processing master station receives and stores the historical current data reported by each metering device, establishing a load database for each line segment. After the metering devices are removed from the line under test, the data processing master station performs the following operations to obtain the predicted current values ​​for each line segment: Method 1 (Prediction Based on Historical Load Data): The data processing master station reads historical current data of the target line segment from the load database for several sampling periods prior to the current moment; based on the historical current data, it uses a load forecasting algorithm to predict the current value of the line segment at the current moment, which is then used as the predicted current value. Load forecasting algorithms include, but are not limited to, time series forecasting algorithms, regression analysis algorithms, or neural network-based load forecasting algorithms.

[0055] Method 2 (based on digital twin simulation): The data processing master station reads the load simulation model of the line segment from the digital twin platform; inputs the environmental parameters (such as temperature and humidity) and time parameters (such as weekdays / holidays, time periods) at the current moment into the load simulation model, and outputs the current simulation value of the line segment at the current moment through the load simulation model as the predicted current value.

[0056] Method 3 (based on user declarations or historical data): The data processing master station obtains the declared power load of users supplied by the line segment at the current moment, or extracts the typical load value at that moment from historical data as the predicted current value.

[0057] In practice, the above three methods can be used individually or in combination (for example, using method two as the main method and method one or method three as verification and correction).

[0058] Optionally, the process of training the load simulation model includes: During the modeling and calibration phase when the metering devices are mounted on the line, the data processing master station receives real-time current data reported by each metering device according to a preset sampling period, and establishes a current history database for each line segment. For each line segment, the data processing master station extracts the historical current data for that segment and obtains the external influencing factor data corresponding to the collection time of each historical current data. The external influencing factor data includes temperature, humidity, precipitation, weekday / holiday identifiers, and time period identifiers. Using the external influencing factor data as input features and the historical current data as labels, the data processing master station trains a preset machine learning model to obtain the load simulation model for that line segment. Specifically, during the modeling and calibration phase (i.e., while the metering devices are mounted on the line), each metering device collects and reports the real-time current value of its line segment to the data processing master station according to a preset sampling period (e.g., once every 15 minutes). The data processing master station receives and stores the current data reported by each metering device, forming a current history database for each line segment. For each line segment, the data processing master station extracts historical current data from the historical current database and divides this data into training and validation sets. The master station also acquires external influencing factor data for the time of data collection corresponding to each historical current data point. These external influencing factors include, but are not limited to, temperature, humidity, precipitation, weekday / holiday status, and the time period (e.g., peak, off-peak, or low-peak electricity consumption). This external influencing factor data can be obtained from meteorological data interfaces and calendar data. Using this external influencing factor data as input features and the corresponding historical current data as labels, the master station trains a pre-defined neural network or regression model to obtain a load simulation model for that line segment. This load simulation model can output corresponding predicted current values ​​based on the input external influencing factor data.

[0059] After the metering device is removed from the power supply line, the data processing master station performs the following operations to obtain the predicted current value: The data processing master station reads the load simulation model of the line segment from the digital twin platform. The data processing master station obtains the external influencing factor data at the current moment, including the current temperature, current humidity, whether the current date is a weekday, and the time period to which the current moment belongs. The data processing master station inputs the external influencing factor data at the current moment into the load simulation model, and the load simulation model outputs the predicted current value of the line segment at the current moment.

[0060] Optionally, a database of line distribution data, including line loss, internal resistance, current, and fault conditions, can be established. Using a digital twin platform simulating the installation site of the metering device, line loss can be estimated through simulation before the device is installed. Furthermore, cluster analysis can be performed on the electricity consumption data reported by the metering device and database cases. If data points similar to known faults are detected, early warning information is generated. This warning can then alert users to dispatch maintenance personnel for repairs, preventing accidents. With metering devices installed, the digital twin platform can calculate line loss data for the entire electricity topology based on the current and voltage data collected by the metering devices.

[0061] Therefore, by utilizing easily installed metering devices, a physical topology diagram of the power supply network can be quickly established, and a digital twin platform for line loss analysis can be built by combining the collected data. In some emergency situations, even if there are no metering devices in the power supply lines, the simulation platform can still be used to estimate line loss data, preventing situations where accidents occur but monitoring is inadequate.

[0062] Step S206: After running the preset cycle, receive the measured electricity consumption data reported by the metering device, and use the measured electricity consumption data to correct the target internal resistance calculation model; send the parameters of the corrected target internal resistance calculation model to each metering device so that the metering device can detect the measured electricity consumption data according to the parameters, and send an early warning information to the preset data processing master station when abnormal information is detected.

[0063] For example, after completing the above steps, the metering device can be disassembled. After the digital twin platform has been running for a period of time, several metering devices can be randomly reinstalled in the power lines corresponding to the digital twin platform. Based on the measured power consumption data obtained from the reinstalled metering devices, the target internal resistance calculation model of the digital twin platform is corrected. After correction, the metering devices are moved to other locations in the line to verify the correction results. The parameters of the corrected target internal resistance calculation model are sent to each metering device. The metering devices can perform real-time detection of the collected measured power consumption data according to the parameters, and send early warning information to the preset data processing master station when abnormal information is detected. The early warning information generated at this time is more timely than the data processing master station's hierarchical detection. The digital twin platform can also learn from the operation and maintenance records to dynamically plan the detection nodes for long-term mounted metering devices.

[0064] Therefore, by using a digital twin platform to simulate line loss, early warnings of abnormal data can be provided. In the future, a small number of devices can be connected to correct the simulation results, thereby achieving intelligent perception of the power consumption / load conditions at the end of the power grid.

[0065] The method provided in this application embodiment acquires topology detection data reported by multiple metering devices. Based on the topology detection data, a physical topology diagram of the line to be tested is generated; the line to be tested includes multiple main lines and branches under each main line; each line in the physical topology diagram is equipped with a metering device to characterize the master-slave topology relationship of each metering device. Electricity consumption data reported by each metering device is received. A target internal resistance calculation model is generated based on the physical topology diagram and the electricity consumption data. Line loss data of the line to be tested is determined based on the target internal resistance calculation model. After a preset running cycle, measured electricity consumption data reported by the metering devices is received, and the target internal resistance calculation model is corrected using the measured electricity consumption data; the parameters of the corrected target internal resistance calculation model are sent to each metering device so that the metering device can detect the measured electricity consumption data according to the parameters, and send early warning information to a preset data processing master station when abnormal information is detected. In this scheme, the power supply network topology can be quickly established through the metering devices, i.e., a physical topology diagram can be generated. The system receives electricity consumption data reported by various metering devices, generates a target internal resistance calculation model based on the physical topology diagram and the electricity consumption data, and calculates the line loss data of the line under test based on the target internal resistance calculation model. Therefore, the metering device can be flexibly installed and disassembled, which is convenient and quick. It can quickly establish the physical topology diagram of the line under test. Based on the physical topology diagram and the target internal resistance calculation model, the difficulty of load detection at the end of the power supply network can be greatly reduced.

[0066] Corresponding to the above method, embodiments of this application also provide a line loss analysis device, such as... Figure 3 As shown, the device includes: The generation module 41 is used to acquire topology detection data reported by multiple metering devices; based on the topology detection data, it generates a physical topology diagram of the line to be tested; the line to be tested includes multiple main lines and branch lines under each main line; each line in the physical topology diagram is equipped with a metering device, which is used to characterize the master-slave topology relationship of each metering device. Receiver module 42 is used to receive electricity consumption data reported by each metering device; The determination module 43 is used to generate a target internal resistance calculation model based on the physical topology diagram and power consumption data; and to determine the line loss data of the line to be tested based on the target internal resistance calculation model.

[0067] The functions of each functional unit of the line loss analysis device provided in the above embodiments of this application can be implemented through the above methods and steps. Therefore, the specific working process and beneficial effects of each unit in the line loss analysis device provided in the embodiments of this application will not be repeated here.

[0068] This application also provides a server, such as... Figure 4 As shown, it includes a processor 510, a communication interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540.

[0069] Memory 530 is used to store computer programs; The processor 510 performs the above steps when executing the program stored in the memory 530.

[0070] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0071] The communication interface is used for communication between the aforementioned server and other devices.

[0072] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0073] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0074] The implementation methods and beneficial effects of the server's various components in the above embodiments for solving the problem can be found in [reference needed]. Figure 1 The steps in the illustrated embodiments are used to implement the process. Therefore, the specific working process and beneficial effects of the server provided in this application will not be repeated here.

[0075] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform any of the line loss analysis methods described in the above embodiments.

[0076] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the line loss analysis methods described in the above embodiments.

[0077] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0079] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0080] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0081] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0082] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims in this application and their equivalents, then this application also intends to include these modifications and variations.

Claims

1. A method for analyzing line loss, characterized in that, The method includes: The system acquires topology detection data reported by multiple metering devices; based on the topology detection data, it generates a physical topology diagram of the line to be tested; the line to be tested includes multiple main lines and branches under each main line; each line in the physical topology diagram is equipped with a metering device to characterize the master-slave topology relationship of each metering device. Receive electricity consumption data reported by each of the aforementioned metering devices; Based on the physical topology diagram and the power consumption data, a target internal resistance calculation model is generated; based on the target internal resistance calculation model, the line loss data of the line to be tested is determined.

2. The method as described in claim 1, characterized in that, Based on the physical topology diagram and the power consumption data, a target internal resistance calculation model is generated, including: Based on the master-slave topology relationship in the physical topology diagram and the power consumption data, determine the baseline resistance value between each group of master-slave metering devices; Select any branch in the physical topology diagram and obtain the influencing factor information of the branch; based on the influencing factor information, the baseline resistance value and the preset line internal resistance theoretical calculation model, generate the target internal resistance calculation model; wherein, the preset line internal resistance theoretical calculation model is established based on the conductor's internal environmental factors, electrical parameters and external environmental factors.

3. The method as described in claim 2, characterized in that, The electricity consumption data includes the actual main voltage value of the main metering device, the actual slave voltage value of the slave metering device, and the actual slave current value. Based on the master-slave topology relationship in the physical topology diagram and the power consumption data, determine the baseline resistance value between each group of master-slave metering devices, including: Based on the master-slave topology relationship in the physical topology diagram, the actual master voltage value, the actual slave voltage value, and the actual slave current value, the baseline resistance value between each group of master-slave metering devices is determined.

4. The method as described in claim 3, characterized in that, The influencing factors information includes actual internal environmental factors of the conductor, actual electrical parameters, and actual external environmental factors. Based on the aforementioned influencing factor information, the baseline resistance value, and the preset theoretical calculation model for line internal resistance, a target internal resistance calculation model is generated, including: The actual internal environmental factors of the conductor, the actual electrical parameters, and the actual external environmental factors are input into a preset theoretical calculation model of the line internal resistance, and the theoretical resistance value between the master and slave metering devices is output. Determine the difference between the baseline resistance value and the theoretical resistance value; based on this difference and the theoretical resistance value, generate a target internal resistance calculation model.

5. The method as described in claim 1, characterized in that, The electricity consumption data includes the actual current value from the metering device; Based on the target internal resistance calculation model, the line loss data of the line to be tested is determined, including: Obtain the hierarchical relationship information of each level in the physical topology diagram; Based on the hierarchical relationship information, starting from the lowest-level slave metering device, the following operations are performed sequentially for each group of metering devices with a master-slave topology: obtaining the predicted current value of the line segment corresponding to the slave metering device in the group; inputting the predicted current value into the target internal resistance calculation model to obtain the current line resistance value of the line segment between the master metering device and the corresponding slave metering device in the group; and determining the line loss value of the line segment based on the predicted current value and the current line resistance value. The line loss values ​​of each line segment and the corresponding line segment location information are summarized to generate the line loss data of the line to be tested.

6. The method as described in claim 1, characterized in that, The topology detection data is formed by each of the metering devices injecting a small current signal into the line to be tested and collecting the small current signal along the propagation path of the line to be tested. Based on the topology detection data, a physical topology diagram of the line to be detected is generated, including: Based on the topology detection data, a preliminary topology diagram is generated; the preliminary topology diagram includes the connection relationships between various metering devices and the hierarchical relationships between main lines and branches. Based on the preliminary topology diagram, identify the line segments not covered by the metering device; based on the line segments not covered by the metering device, output a replacement prompt message; If it is confirmed that topology detection data of a line segment not covered by a metering device has been received, a physical topology diagram of the line to be detected is generated; each line in the physical topology diagram is equipped with a metering device; the hierarchical relationship in the physical topology diagram represents the master-slave topology relationship between the metering devices.

7. The method as described in claim 1, characterized in that, The method further includes: After running for a preset period, the measured power consumption data reported by the metering device is received, and the measured power consumption data is used to correct the target internal resistance calculation model. The parameters of the corrected target internal resistance calculation model are sent to each of the metering devices so that the metering devices can detect the measured electricity consumption data according to the parameters and send early warning information to the preset data processing master station when abnormal information is detected.

8. A line loss analysis device, characterized in that, The device includes: A generation module is used to acquire topology detection data reported by multiple metering devices; based on the topology detection data, a physical topology diagram of the line to be tested is generated; the line to be tested includes multiple main lines and branch lines under each main line; each line in the physical topology diagram is equipped with a metering device, which is used to characterize the master-slave topology relationship of each metering device; A receiving module is used to receive electricity consumption data reported by each of the metering devices; The determination module is used to generate a target internal resistance calculation model based on the physical topology diagram and the power consumption data; and to determine the line loss data of the line to be tested based on the target internal resistance calculation model.

9. A server, characterized in that, The server includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus. Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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