CN118017479A - A line loss analysis method and system for technical line loss rate correction - Google Patents

Abstract

The present invention discloses a line loss analysis method and system for correcting the technical line loss rate, the method comprising: collecting power parameters of electric energy transmitted by a power grid; constructing a topological structure of a power grid, and performing specific segmentation on the electric energy transmitted by the power grid according to the topological structure of the power grid; using a pre-trained power grid line loss model to perform real-time monitoring and prediction of the technical line loss rate for each segmented electric energy; and performing real-time line loss rate correction for electric energy that has experienced line loss according to the technical line loss rate. By using the embodiments of the present invention, it is possible to achieve comprehensive monitoring and prediction of the line loss rate in the power system through a machine learning model, improve power transmission efficiency, reduce energy waste, and provide effective support for the intelligent operation and management of the power system.

Description

A line loss analysis method and system for technical line loss rate correction

Technical Field

The invention belongs to the field of electric power technology, and in particular to a line loss analysis method and system for correcting technical line loss rate.

Background technique

The line loss rate in the power system has always been an important issue, affecting the stability and economy of the power system. Line loss is caused by factors such as the resistance, inductance and capacitance of the transmission line, the short-circuit impedance of the transformer, and nonlinear current. Line loss not only brings energy waste, but also affects the basic parameters of the power system such as voltage stability, current quality and power supply reliability. Therefore, reducing the line loss rate and improving the efficiency of power transmission have become one of the important topics in the operation and management of the power system.

Traditional line loss rate calculation methods usually use simple formulas, which result in low accuracy, fail to provide real-time line status information, and are difficult to support intelligent operation and management of power systems.

Summary of the invention

The purpose of the present invention is to provide a line loss analysis method and system for technical line loss rate correction to address the deficiencies in the prior art. It can achieve comprehensive monitoring and prediction of line loss rates in power systems through machine learning models, improve power transmission efficiency, reduce energy waste, and provide effective support for the intelligent operation and management of power systems.

An embodiment of the present application provides a line loss analysis method for technical line loss rate correction, the method comprising:

Collect power parameters of electric energy transmitted by the power grid;

Construct the topological structure of the power grid and divide the electric energy transmitted by the power grid into specific segments according to the topological structure of the power grid;

Using the pre-trained grid line loss model, the technical line loss rate of each segmented power is monitored and predicted in real time;

According to the technical line loss rate, real-time line loss rate correction is performed on the electric energy that has line loss.

Optionally, the specific segmentation of the electric energy transmitted by the power grid according to the topological structure of the power grid includes:

Using the grid topology diagram, identify the main subnets in the grid;

Segment each major subnet into multiple partitions;

The nodes in each partition are subjected to power flow analysis, and the nodes in each partition are divided into different segments according to the power flow analysis results.

Optionally, the prediction formula for the technical line loss rate is:

Technical_loss_rate = Total_loss/Power_loss

Among them, Technical_loss_rate is the technical line loss rate, and Total_loss is the total loss of the line:

Total_loss＝R_loss+C_loss+L_loss

Power_loss is the active power loss of the line:

Power_loss＝I^2xR_loss

Among them, the resistance loss of the line R_loss = I^2xRxL, I is the current, R is the resistance, and L is the line length; the capacitance loss C_loss = I^2xXcxL, Xc is the capacitance impedance; the inductive loss L_loss = I^2x Xl x L, Xl is the inductive impedance.

Optionally, the line loss rate correction is obtained according to the following formula:

Lc＝Technical_loss_rate*sqrt[(1+k)^2/(1-k)^2]/L

Among them, Lc is the corrected line loss rate, k is the correction coefficient, and L is the line length.

Optionally, the calculation formula of the correction coefficient is:

k＝(Ua/Ub)*(Ib/Ia)*(cosθa/cosθb)

Among them, Ua and Ia are the voltage and current at the starting point of the line, Ub and Ib are the voltage and current at the end point of the line, and θa and θb are the power factors at the starting point and end point of the line, respectively.

Another embodiment of the present application provides a line loss analysis system for technical line loss rate correction, the system comprising:

A collection module, used to collect power parameters of the electric energy transmitted by the power grid;

The segmentation module is used to construct the topological structure of the power grid and to perform specific segmentation on the electric energy transmitted by the power grid according to the topological structure of the power grid;

The monitoring module is used to use the pre-trained power grid line loss model to perform real-time monitoring and prediction of the technical line loss rate of each segment of electric energy;

The correction module is used to perform real-time line loss rate correction for electric energy that has line loss according to the technical line loss rate.

Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

Compared with the prior art, the present invention provides a line loss analysis method for technical line loss rate correction, which collects power parameters of electric energy transmitted by the power grid; constructs the topological structure of the power grid, and specifically segments the electric energy transmitted by the power grid according to the topological structure of the power grid; uses a pre-trained power grid line loss model to perform real-time monitoring and prediction of the technical line loss rate for each segmented electric energy; and performs real-time line loss rate correction for the electric energy that has line loss according to the technical line loss rate, thereby enabling comprehensive monitoring and prediction of the line loss rate in the power system through a machine learning model, improving power transmission efficiency, reducing energy waste, and providing effective support for the intelligent operation and management of the power system.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic flow chart of a line loss analysis method for technical line loss rate correction provided by an embodiment of the present invention;

FIG2 is a schematic diagram of the structure of a line loss analysis system for line loss rate correction provided by an embodiment of the present invention;

FIG3 is a hardware structure block diagram of a computer terminal of a line loss analysis method for line loss rate correction provided by an embodiment of the present invention.

Detailed ways

The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

Referring to FIG. 1 , an embodiment of the present invention provides a line loss analysis method for technical line loss rate correction, and the method may include the following steps:

S101, collecting power parameters of electric energy transmitted by the power grid;

Specifically, one implementation method includes: deploying electric field sensing sensors at key locations of the power grid, such as busbars, substations, and distribution lines. These sensors use the principle of electromagnetic induction to sense changes in the electric field strength in the power grid in real time. The electric field sensing sensors sense changes in the electric field strength in the power grid and extract power parameter information, such as voltage and frequency, by processing the electric field signals.

Nano energy harvesters are installed at key locations of the power grid, such as cable connection points and transformers. These harvesters use nanotechnology to collect tiny amounts of energy from the surrounding environment and convert them into electrical energy that can be used by sensors. Nano energy harvesters use wireless energy transmission technology to wirelessly transmit the collected energy to electric field sensing sensors to meet the electrical energy required for their operation.

The electric field sensing sensor senses the change in electric field strength, converts the collected power parameter information into digital signals, and transmits the data to the data receiving end through wireless communication technology. At the data receiving end, the received power parameter information is processed and analyzed, such as data decoding, verification and denoising. Then, the processed data is stored in a local database or cloud platform.

Through the above steps, the electric field sensing sensor and nano energy harvester are used to collect power parameters. This method does not require the use of traditional batteries or wired power supply, but uses nano energy harvesters to collect tiny energy around for power supply, thereby realizing wireless collection of power parameters. At the same time, it can improve the convenience and flexibility of data collection, reduce energy consumption, and conform to the concept of green energy saving.

S102, constructing a topological structure of the power grid, and dividing the electric energy transmitted by the power grid into specific segments according to the topological structure of the power grid;

Specifically, the power grid topology diagram can be used to identify the main subnets in the power grid; each main subnet is subdivided and divided into multiple partitions; the nodes in each partition are analyzed for power flow, and the nodes in each partition are divided into different segments according to the results of the power flow analysis.

In one implementation, based on the grid topology diagram, the main subnets in the grid are first identified. This can be achieved by analyzing the connection relationship between the nodes and lines of the grid. The traditional method may only consider the backbone network, while a more detailed approach is used here to consider all the main subnets in the grid.

Each major subnet is further subdivided into multiple partitions. This can be achieved by considering the different power flow paths in the subnet and the connections between nodes. The concepts of connected components or cut points in graph theory can be used to identify partitions within the subnet.

Perform power flow analysis on the nodes in each partition, and divide the nodes in each partition into different segments based on the results of the power flow analysis. In this step, factors such as the power flow direction and power size between nodes need to be considered to divide nodes with similar characteristics into the same segment.

Verify and optimize the segmentation results. The rationality and accuracy of the segmentation results can be ensured by monitoring and comparing the power parameters after segmentation. If necessary, iterative optimization can be performed until the best segmentation result is obtained.

In another implementation, the topological structure diagram of the power grid, including the connection relationship between all nodes and lines, is collected and organized, which can be obtained through on-site surveys, power grid design drawings, or data in a power grid management system.

Using graph theory algorithms, such as depth-first search or breadth-first search, the topology of the power grid is traversed. Based on the connection relationship between nodes and lines, the main subnets in the power grid, that is, the groups of nodes that are closely connected to each other, are identified.

Each major subnet is subdivided into multiple partitions. Community partitioning algorithms, such as the Louvain algorithm or the GN algorithm, can be used to partition nodes with close connections within the subnet into the same partition.

Perform power flow analysis on nodes in each partition. Calculate the power flow size and direction between nodes based on parameters such as voltage, current, and power between nodes.

According to the results of power flow analysis, the nodes in each partition are divided into different segments. You can set a threshold or use a clustering algorithm, such as the K-means algorithm or the DBSCAN algorithm, to identify nodes with similar power flow characteristics and divide them into the same segment.

Verify and optimize the segmentation results. The rationality and accuracy of the segmentation results can be ensured by monitoring and comparing the power parameters after segmentation. The segmentation results can be verified by simulation calculation or actual data comparison.

S103, using a pre-trained power grid line loss model, real-time monitoring and prediction of the technical line loss rate of each segmented electric energy;

Specifically, historical data can be used to pre-train the power grid line loss model using machine learning or other modeling techniques. The input of the model includes the power parameters of each segment of electric energy after segmentation, and the output is the technical line loss rate of the segment of electric energy.

According to the power parameters of the segmented electric energy, they are input into the pre-trained grid line loss model to obtain the technical line loss rate of the segmented electric energy. The real-time data stream can be used to monitor the technical line loss rate of each segment of electric energy in the grid in real time based on the prediction ability of the model.

According to the power parameters of the segmented electric energy, they are input into the pre-trained power grid line loss model, and the technical line loss rate of each segment of electric energy in the power grid is predicted in real time through the prediction ability of the model. The prediction results can be used to evaluate and optimize the operation of the power grid.

The present invention proposes a training method for a power grid line loss model as follows:

1. State representation: The state of the power grid is represented as a state vector, including power parameters, topology information, line loss data, etc.

2. Action determination: Define a set of possible actions, such as adjusting line parameters, adding capacitors or resistors, etc.

3. Reward design: Design a reward function to reward the system at each time step based on the change in line loss rate and other performance indicators. The reward function can be customized according to actual needs to guide the training model to better control line loss.

4. Establish a deep reinforcement learning model: Use a deep neural network as the basis of the model, use the state vector as input, and output an action value function (Q value function) to evaluate the value of each action.

5. Reinforcement learning training: Through interaction with the power system environment, the deep reinforcement learning model is trained using reinforcement learning algorithms (such as deep Q learning, advantage actor critic, etc.). During the training process, the model will continuously try different actions, observe the feedback rewards from the environment, and optimize the model's strategy through reward signals.

6. Model application: Apply the trained deep reinforcement learning model for real-time monitoring and prediction. Based on the current power parameters and topology information, use the trained model to select the best action to minimize line loss and optimize the performance of the power grid.

By using a deep reinforcement learning model, the control strategy of the power grid system can be automatically learned and optimized during the training process to minimize the line loss rate and improve the efficiency of the power grid. This training method will make full use of the advantages of deep learning and reinforcement learning to more accurately predict and control the line loss of the power grid.

In another implementation, a prediction formula for a technology line loss rate is:

Technical_loss_rate = Total_loss/Power_loss

Among them, Technical_loss_rate is the technical line loss rate, and Total_loss is the total loss of the line:

Total_loss＝R_loss+C_loss+L_loss

Power_loss is the active power loss of the line:

Power_loss＝I^2x R_loss

Among them, the resistance loss of the line R_loss = I^2x R x L, I is the current, R is the resistance, and L is the line length; the capacitance loss C_loss = I^2x Xc x L, Xc is the capacitance impedance; the inductive loss L_loss = I^2x Xl x L, Xl is the inductive impedance.

Total loss (Total_loss) represents the total loss of all lines in the power system, including resistance loss (R_loss), capacitance loss (C_loss) and inductive loss (L_loss). These losses are the energy loss that occurs when the current in the line flows through the resistance, capacitance and inductive components, resulting in the loss of line power.

Active power loss (Power_loss) represents the loss of active power in the line, that is, the energy loss caused by the current passing through the resistance element. The formula uses the square of the current (I^2) multiplied by the resistance loss (R_loss) to calculate the active power loss.

By calculating the total loss and active power loss, the technical line loss rate can be obtained, which is the ratio of total loss to active power loss. The technical line loss rate is an indicator to measure the degree of line loss in the power system and can be used to evaluate the efficiency and operation status of the power grid.

S104: Based on the technical line loss rate, real-time line loss rate correction is performed on the electric energy that has line loss.

Specifically, by comparing with a preset threshold, the electric energy with a technical line loss rate higher than the threshold is identified, that is, the electric energy with line loss. According to the technical line loss rate of the electric energy with line loss, the line loss rate correction coefficient is calculated to achieve the correction of the line loss rate. The calculation can be performed using a statistical method or model based on the change trend of the line loss rate and historical data.

Specifically, a calculation formula for the correction coefficient is:

k＝(Ua/Ub)*(Ib/Ia)*(cosθa/cosθb)

Among them, Ua and Ia are the voltage and current at the starting point of the line, Ub and Ib are the voltage and current at the end point of the line, and θa and θb are the power factors at the starting point and end point of the line. The calculation method of the correction coefficient is intended to evaluate the correction requirements of the line based on the parameters of the power grid such as voltage, current and power factor. The calculation of the correction coefficient takes into account the difference in voltage and current at the starting point and end point of the line and the influence of the power factor, thereby obtaining the coefficient value that describes the correction requirements of the line.

According to the calculated line loss rate correction coefficient, the line loss rate of the electric energy with line loss is corrected in real time. The grid parameters such as resistance, capacitance, etc. can be adjusted according to the correction coefficient, or the transmission path of the electric energy can be adjusted. One type of line loss rate correction is obtained according to the following formula:

Lc＝Technical_loss_rate*sqrt[(1+k)^2/(1-k)^2]/L

Where Lc is the corrected line loss rate, k is the correction coefficient, and L is the line length. The original technical line loss rate is corrected according to a specific correction factor to obtain a more accurate line loss rate result. The correction factor includes the square of the correction coefficient. By performing square and square root operations on the correction coefficient, it can be ensured that the value of the correction factor is always positive and the line loss rate is kept correct during the correction process.

Continuously monitor the corrected technical line loss rate of electric energy and provide real-time feedback to the control center or operator. Through monitoring and feedback, timely adjust the correction operation to maintain the line loss rate of the power grid within a reasonable range. The correction of the line loss rate can help evaluate the operation status of the power grid, guide the optimization of the line and the improvement of energy utilization. The corrected line loss rate can more accurately reflect the line loss situation in the power grid and help improve the efficiency and energy utilization of the power grid.

It can be seen that by collecting the power parameters of the electric energy transmitted by the power grid; constructing the topological structure of the power grid, and dividing the electric energy transmitted by the power grid into specific segments according to the topological structure of the power grid; using the pre-trained power grid line loss model, real-time monitoring and prediction of the technical line loss rate of each segmented electric energy are performed; according to the technical line loss rate, real-time line loss rate correction is performed for the electric energy with line loss, so that the machine learning model can be used to comprehensively monitor and predict the line loss rate in the power system, improve the power transmission efficiency, reduce energy waste, and provide effective support for the intelligent operation and management of the power system.

Another embodiment of the present invention provides a line loss analysis system for technical line loss rate correction. Referring to FIG. 2 , the system may include:

The acquisition module 201 is used to acquire the power parameters of the electric energy transmitted by the power grid;

The segmentation module 202 is used to construct the topological structure of the power grid and to perform specific segmentation on the electric energy transmitted by the power grid according to the topological structure of the power grid;

The monitoring module 203 is used to use the pre-trained power grid line loss model to perform real-time monitoring and prediction of the technical line loss rate for each segment of electric energy;

The correction module 204 is used to perform real-time line loss rate correction for the electric energy that has line loss according to the technical line loss rate.

It can be seen that by collecting the power parameters of the electric energy transmitted by the power grid; constructing the topological structure of the power grid, and dividing the electric energy transmitted by the power grid into specific segments according to the topological structure of the power grid; using the pre-trained power grid line loss model, real-time monitoring and prediction of the technical line loss rate of each segmented electric energy are performed; according to the technical line loss rate, real-time line loss rate correction is performed for the electric energy with line loss, so that the machine learning model can be used to comprehensively monitor and predict the line loss rate in the power system, improve the power transmission efficiency, reduce energy waste, and provide effective support for the intelligent operation and management of the power system.

The following is a detailed description of it by taking the operation on a computer terminal as an example. FIG3 is a hardware structure block diagram of a computer terminal of a line loss analysis method for line loss rate correction provided by an embodiment of the present invention. As shown in FIG3 , the computer terminal may include one or more (only one is shown in FIG3 ) processors 302 (the processor 302 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 304 for storing data. Optionally, the computer terminal may also include a transmission device 306 for communication functions and an input and output device 308. It will be understood by those skilled in the art that the structure shown in FIG3 is only for illustration and does not limit the structure of the computer terminal. For example, the computer terminal may also include more or fewer components than those shown in FIG3 , or have a configuration different from that shown in FIG3 .

The memory 304 can be used to store software programs and modules of application software, such as the program instructions/modules corresponding to the line loss analysis method of the technical line loss rate correction in the embodiment of the present application. The processor 302 executes various functional applications and data processing by running the software programs and modules stored in the memory 304, that is, to implement the above method. The memory 304 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 304 may further include a memory remotely arranged relative to the processor 302, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

The transmission device 306 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 306 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 306 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps:

S101, collecting power parameters of electric energy transmitted by the power grid;

S102, constructing a topological structure of the power grid, and dividing the electric energy transmitted by the power grid into specific segments according to the topological structure of the power grid;

S103, using a pre-trained power grid line loss model, real-time monitoring and prediction of the technical line loss rate of each segmented electric energy;

S104: Based on the technical line loss rate, real-time line loss rate correction is performed on the electric energy that has line loss.

Specifically, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

It can be seen that by collecting the power parameters of the electric energy transmitted by the power grid; constructing the topological structure of the power grid, and dividing the electric energy transmitted by the power grid into specific segments according to the topological structure of the power grid; using the pre-trained power grid line loss model, real-time monitoring and prediction of the technical line loss rate of each segmented electric energy are performed; according to the technical line loss rate, real-time line loss rate correction is performed for the electric energy with line loss, so that the machine learning model can be used to comprehensively monitor and predict the line loss rate in the power system, improve the power transmission efficiency, reduce energy waste, and provide effective support for the intelligent operation and management of the power system.

An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

Specifically, the electronic device may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.

Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program:

S101, collecting power parameters of electric energy transmitted by the power grid;

S102, constructing a topological structure of the power grid, and dividing the electric energy transmitted by the power grid into specific segments according to the topological structure of the power grid;

S103, using a pre-trained power grid line loss model, real-time monitoring and prediction of the technical line loss rate of each segmented electric energy;

S104: Based on the technical line loss rate, real-time line loss rate correction is performed on the electric energy that has line loss.

Specifically, the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

It can be seen that by collecting the power parameters of the electric energy transmitted by the power grid; constructing the topological structure of the power grid, and dividing the electric energy transmitted by the power grid into specific segments according to the topological structure of the power grid; using the pre-trained power grid line loss model, real-time monitoring and prediction of the technical line loss rate of each segmented electric energy are performed; according to the technical line loss rate, real-time line loss rate correction is performed for the electric energy with line loss, so that the machine learning model can be used to comprehensively monitor and predict the line loss rate in the power system, improve the power transmission efficiency, reduce energy waste, and provide effective support for the intelligent operation and management of the power system.

The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modified into equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and the drawings, should be within the protection scope of the present invention.

Claims (10)

1. A line loss analysis method for technical line loss rate correction, the method comprising:

Collecting electric power parameters of electric energy transmitted by a power grid;

Constructing a topological structure of a power grid, and carrying out specific segmentation on electric energy transmitted by the power grid according to the topological structure of the power grid;

carrying out real-time monitoring and prediction on the technical line loss rate of each section of electric energy after segmentation by utilizing a pre-trained power grid line loss model;

And according to the technical line loss rate, correcting the line loss rate of the electric energy with the line loss in real time.

2. The method according to claim 1, wherein the specific segmentation of the power transmitted by the power grid according to the topology of the power grid comprises:

identifying a main subnet in the power grid by using a power grid topology structure diagram;

subdividing each main subnet, and dividing the subnets into a plurality of subareas;

And carrying out electric energy flow analysis on the nodes in each partition, and dividing the nodes in each partition into different segments according to the electric energy flow analysis result.

3. The method of claim 2, wherein the prediction formula of the technical line loss rate is:

Technical_loss_rate＝Total_loss/Power_loss

wherein, technical_loss_rate is the Technical line loss rate, total_loss is the Total loss of the line:

Total_loss＝R_loss+C_loss+L_loss

power_loss is the active Power loss of the line:

Power_loss＝I^2x R_loss

Wherein, the resistance loss R_loss=I≡2xRxL of the line, I is current, R is resistance, L is line length; capacitance loss c_loss=i2xxcxl, xc being capacitance impedance; inductive loss l_loss=i2xxlxl, xl is the inductive impedance.

4. A method according to claim 3, wherein the line loss rate correction is obtained according to the following formula:

Lc＝Technical_loss_rate*sqrt[(1+k)^2/(1-k)^2]/L

wherein Lc is the corrected line loss rate, k is the correction coefficient, and L is the line length.

5. The method of claim 4, wherein the correction factor is calculated by the formula:

k＝(Ua/Ub)*(Ib/Ia)*(cosθa/cosθb)

Where Ua and Ia are the voltage and current of the line start point, ub and Ib are the voltage and current of the line end point, respectively, and θa and θb are the power factors of the line start point and end point, respectively.

6. A line loss analysis system for technical line loss rate correction, the system comprising:

the acquisition module is used for acquiring electric parameters of electric energy transmitted by the power grid;

the segmentation module is used for constructing a topological structure of the power grid and carrying out specific segmentation on the electric energy transmitted by the power grid according to the topological structure of the power grid;

the monitoring module is used for carrying out real-time monitoring and prediction on the technical line loss rate of each section of electric energy after segmentation by utilizing a pre-trained power grid line loss model;

and the correction module is used for correcting the line loss rate of the electric energy with the line loss in real time according to the technical line loss rate.

7. The system according to claim 6, wherein the segmentation module is specifically configured to:

identifying a main subnet in the power grid by using a power grid topology structure diagram;

subdividing each main subnet, and dividing the subnets into a plurality of subareas;

And carrying out electric energy flow analysis on the nodes in each partition, and dividing the nodes in each partition into different segments according to the electric energy flow analysis result.

8. The system of claim 7, wherein the predictive formula for the technical line loss rate is:

Technical_loss_rate＝Total_loss/Power_loss

wherein, technical_loss_rate is the Technical line loss rate, total_loss is the Total loss of the line:

Total_loss＝R_loss+C_loss+L_loss

power_loss is the active Power loss of the line:

Power_loss＝I^2x R_loss

Wherein, the resistance loss R_loss=I≡2xRxL of the line, I is current, R is resistance, L is line length; capacitance loss c_loss=i2xxcxl, xc being capacitance impedance; inductive loss l_loss=i2xxlxl, xl is the inductive impedance.

9. A storage medium having a computer program stored therein, wherein the computer program is arranged to perform the method of any of claims 1-5 when run.

10. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the method of any of claims 1-5.