Power grid information control method based on digital twinborn model

Abstract

The invention discloses a power grid information control method based on a digital twinborn model, and relates to the technical field of power grid information control methods, and the method comprises the steps: carrying out the data collection of a power grid node through a sensor, and obtaining original power grid operation data; preprocessing the original power grid operation data by adopting an edge calculation processing method to obtain preprocessed power grid operation data; fusing the preprocessed power grid operation data by adopting a multi-source heterogeneous data fusion algorithm to obtain a power grid operation data set; constructing a power grid state evaluation model based on a regression analysis method, inputting the power grid operation data set into the power grid state evaluation model, and outputting a power grid state evaluation result; and monitoring the power grid equipment by adopting a monitoring method based on a power grid state evaluation result, identifying a fault point, obtaining power grid equipment fault information, optimizing the power grid state evaluation model by adopting a reinforcement learning algorithm based on the power grid equipment fault information, and obtaining an optimized power grid state evaluation model.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid information control methods, and particularly to a power grid information control method based on a digital twin model. Background Art

[0002] The power grid information control method refers to a series of measures and strategies for real-time monitoring, analysis, and regulation of the operation of the power system through modern information technology, communication technology, and control technology. Its purpose is to improve the safety, stability, and economy of the power grid, ensure the reliability and quality of power supply. The methods include data acquisition and monitoring systems, energy management systems, distribution management systems, etc. Through real-time perception and intelligent analysis of the operation state of the power grid, functions such as rapid fault response, optimal resource allocation, and automated operation are realized, thus supporting the development of the smart grid and meeting the power demand of modern society.

[0003] In the technical field of power grid information control methods, traditional power grid data acquisition lacks high precision and real-time performance, and it is difficult to accurately reflect the operation state of the power grid. Secondly, untimely data processing and low data quality lead to inaccurate power grid state assessment, and it is difficult to fuse multi-source heterogeneous data, which affects the overall monitoring and management efficiency of the power grid. At the same time, the existing technology lacks an effective fault prediction and rapid response mechanism, reducing the safety and stability of the power grid. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a power grid information control method based on a digital twin model, which solves the problems in the traditional method that untimely data processing and low data quality lead to inaccurate power grid state assessment and difficulty in fusing multi-source heterogeneous data.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a power grid information control method based on a digital twin model, which includes: collecting data of power grid nodes by using sensors to obtain original power grid operation data;

[0008] Preprocessing the original power grid operation data by using an edge computing processing method to obtain preprocessed power grid operation data;

[0009] Fusing the preprocessed power grid operation data by using a multi-source heterogeneous data fusion algorithm to obtain a power grid operation data set;

[0010] Constructing a power grid state assessment model based on a regression analysis method, inputting the power grid operation data set into the power grid state assessment model, and outputting a power grid state assessment result;

[0011] Based on the power grid status assessment results, a monitoring method is used to monitor power grid equipment, and fault points are identified to obtain power grid equipment fault information. Based on the power grid equipment fault information, a reinforcement learning algorithm is used to optimize the power grid status assessment model, and an optimized power grid status assessment model is obtained;

[0012] Based on the optimized power grid status assessment model, an intelligent scheduling algorithm is used to formulate a power grid control strategy.

[0013] As a preferred solution of the power grid information control method based on the digital twin model of the present invention, wherein: the sensors are used to collect data of power grid nodes to obtain the original power grid operation data, and the specific steps are as follows:

[0014] Deploy high-precision intelligent sensors at the substations, distribution boxes and transmission lines of the power grid;

[0015] Use high-precision intelligent sensors to collect voltage, current and power data of substations and distribution boxes;

[0016] Use the Network Time Protocol NTP to provide timestamps for the voltage, current and power data of substations and distribution boxes. When no new data arrives within a certain period of time, mark that there is data missing in that period;

[0017] Collect the voltage, current and power data as the original power grid operation data.

[0018] As a preferred solution of the power grid information control method based on the digital twin model of the present invention, wherein: the edge computing processing method is used to preprocess the original power grid operation data to obtain the preprocessed power grid operation data, and the specific steps are as follows:

[0019] Transmit the original power grid operation data to the edge computing device through the high-speed communication network 5G;

[0020] Start the edge computing device, and use the sliding window technology to calculate the local mean and standard deviation of the original power grid operation data, perform data cleaning on the original power grid operation data, and filter out the data points outside the range;

[0021] Perform standardization processing on the cleaned original power grid operation data as the preprocessed power grid operation data;

[0022] Store the preprocessed power grid operation data in the database of the edge computing device and upload it to the central server.

[0023] As a preferred solution of the power grid information control method based on the digital twin model of the present invention, wherein: the multi-source heterogeneous data fusion algorithm is used to fuse the preprocessed power grid operation data to obtain a power grid operation data set, and the specific steps are as follows:

[0024] Further clean the pre - processed power grid operation data, and use the Z - Score method to identify and remove outliers in the pre - processed power grid operation data;

[0025] Use interpolation method to fill in the time periods with missing data, so that the pre - processed power grid operation data is aligned on the same time axis;

[0026] Extract the voltage, current and power data from the pre - processed power grid operation data, and use the weighted average method to fuse the voltage, current and power data to obtain the power grid operation data set.

[0027] As a preferred solution of the power grid information control method based on the digital twin model of the present invention, wherein: construct a power grid state evaluation model based on the regression analysis method, input the power grid operation data set into the power grid state evaluation model, and output the power grid state evaluation result. The specific steps are as follows:

[0028] Select the power grid operation data set as the characteristic variable for constructing the model;

[0029] Construct a power grid state evaluation model based on the regression analysis method combined with the power grid operation data set;

[0030] Input the power grid operation data set into the power grid state evaluation model and output the power grid state evaluation result.

[0031] As a preferred solution of the power grid information control method based on the digital twin model of the present invention, wherein: based on the power grid state evaluation result, adopt a monitoring method to monitor the power grid equipment and identify the fault points to obtain the power grid equipment fault information. The specific steps are as follows:

[0032] Based on the pre - processed power grid operation data, set the power grid state threshold;

[0033] Compare the power grid state evaluation result with the power grid state threshold, and take the value greater than the power grid state threshold as an outlier;

[0034] Use the statistical method IQR to detect the location information of the fault points corresponding to the outliers;

[0035] According to the location information of the fault points, use the fault diagnosis algorithm for clustering analysis, learn from the historical fault data, establish a fault pattern library, compare the new fault data with the patterns in the library, and determine the fault type;

[0036] Organize the location, fault type and related information of the fault points into a report to obtain the power grid equipment fault information.

[0037] As a preferred solution of the power grid information control method based on the digital twin model of the present invention, wherein: based on the power grid equipment fault information, an enhanced learning algorithm is used to optimize the power grid state evaluation model, and the optimized power grid state evaluation model is obtained. The specific steps are as follows:

[0038] Based on the power grid state evaluation model, a digital twin model of the power grid equipment is established using digital twin technology, a state space is set, and the performance of the power grid equipment under various operating conditions is dynamically simulated;

[0039] The state space includes voltage level, load rate, and temperature;

[0040] The operating conditions include load changes, voltage and current fluctuations, and equipment faults and maintenance conditions;

[0041] The DQN algorithm is used to define the reward function;

[0042] A positive reward is given when the power grid equipment is in a safe and stable state;

[0043] A negative penalty is given when a fault occurs in the power grid equipment;

[0044] An additional reward is given when a fault in the power grid equipment is detected and repaired;

[0045] The power grid equipment fault information is added as a feature to the state space, and the updated state space is output;

[0046] The updated state space is input into the power grid state evaluation model, and the optimized power grid state evaluation model and the optimized power grid state evaluation result are obtained.

[0047] As a preferred solution of the power grid information control method based on the digital twin model of the present invention, wherein: based on the optimized power grid state evaluation model, an intelligent scheduling algorithm is used to formulate a power grid control strategy. The specific steps are as follows:

[0048] The genetic algorithm GA is selected as the intelligent scheduling algorithm;

[0049] The GA algorithm is executed, and based on the state evaluation result provided by the optimized power grid state evaluation model, the optimal control strategy is searched;

[0050] When the load rate of a certain area is detected to be close to the rated capacity, the power generation in that area is increased and part of the load is transferred to other areas;

[0051] When the voltage level of a certain area is detected to be lower than the set power grid state threshold, the tap position of the transformer is adjusted, and the reactive power compensation equipment is started to increase the voltage level;

[0052] When a power grid fault is detected, isolate the fault area and adjust the load distribution.

[0053] In a second aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program is executed by the processor, any step of the power grid information control method based on the digital twin model as described in the first aspect of the present invention is implemented.

[0054] In a third aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, any step of the power grid information control method based on the digital twin model as described in the first aspect of the present invention is implemented.

[0055] The beneficial effects of the present invention are as follows: By performing local mean and standard deviation calculations and standardization processing on the received original power grid operation data through edge computing devices, data cleaning and preliminary analysis are achieved. Not only are outliers filtered out, but the data is also standardized, facilitating further processing and analysis. By further cleaning the preprocessed power grid operation data and using the Z-Score method to remove outliers, the interpolation method is used to fill in missing data, and finally the weighted average method is used to fuse voltage, current, and power data, the unification and optimization of the data set are achieved, the problem of incomplete data is solved, and all data points are ensured to be aligned on the same time axis, which is beneficial to constructing a more accurate power grid state assessment model. Selecting the power grid operation data set as a feature variable and constructing a power grid state assessment model based on the regression analysis method combined with the power grid operation data set realizes the quantitative assessment of the power grid state. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0057] Figure 1 It is a flowchart of the power grid information control method based on the digital twin model in Embodiment 1.

[0058] Figure 2 It is a flowchart of the preprocessed power grid operation data in Embodiment 1. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings in the specification.

[0060] In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways than those specifically described herein. Those skilled in the art can make similar extensions without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0061] Secondly, as used herein, "one embodiment" or "an embodiment" refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other.

[0062] Embodiment 1, referring to Figure 1 and Figure 2 , is the first embodiment of the present invention. This embodiment provides a power grid information control method based on a digital twin model, including the following steps:

[0063] S1. Use sensors to collect data from power grid nodes to obtain original power grid operation data;

[0064] Furthermore, deploy high-precision intelligent sensors at the substations, distribution boxes, and transmission lines of the power grid to ensure that the sensors cover all key monitoring points to comprehensively obtain the power grid operation status;

[0065] Use high-precision intelligent sensors to collect voltage, current, and power data of substations and distribution boxes;

[0066] During the collection process, the sampling frequency of the high-precision intelligent sensors is not less than once per second;

[0067] Use the Network Time Protocol (NTP) to provide timestamps for the voltage, current, and power data of substations and distribution boxes to ensure that the time synchronization error does not exceed ±1 ms;

[0068] Embed time information in each data packet. When no new data arrives within a certain period, mark that there is a data missing in that period. The expression is:

[0069]

[0070] where M(t) is the marking value and D(t) represents the data presence at time t;

[0071] Aggregate the voltage, current, and power data as the original power grid operation data Q;

[0072] It should be noted that by deploying high-precision intelligent sensors at key positions in substations, distribution boxes, and transmission lines, the accuracy and real-time nature of data can be ensured. The NTP protocol is used to add timestamps to all collected data points, which not only improves the time synchronization of the data but also makes it easier to identify and mark data missing situations during subsequent processing. This method is crucial for ensuring the integrity and accuracy of the original power grid operation data, thus supporting the effectiveness and reliability of the subsequent analysis process.

[0073] S2. Use edge computing processing methods to preprocess the original power grid operation data to obtain preprocessed power grid operation data;

[0074] Furthermore, transmit the original power grid operation data to the edge computing device through the high-speed communication network 5G;

[0075] Start the edge computing device and use the sliding window technique to calculate the local mean and standard deviation of the original power grid operation data to identify and filter out abnormal data points, and perform data cleaning on the original power grid operation data to filter out data points outside the range. The expression is:

[0076]

[0077] Among them, Q(t) represents the data value at time t, w is the width of the sliding window, and μ local (t) is the local mean of the original power grid operation data, and σ local (t) is the standard deviation of the original power grid operation data;

[0078] Use the Z-Score standardization method to standardize the cleaned original power grid operation data as the preprocessed power grid operation data, making the data conform to a specific distribution for subsequent analysis;

[0079] Store the preprocessed power grid operation data in the database of the edge computing device and upload it to the central server;

[0080] It should be noted that using the 5G network to transmit the original power grid operation data to the edge computing device can significantly reduce data transmission latency and improve the response speed. The application of the sliding window technique allows the method to dynamically evaluate the power grid status based on the data in the recent period, which is particularly important for timely detecting and handling abnormal situations. The standardization process not only helps to eliminate the differences between data of different magnitudes but also improves the efficiency and accuracy of model training.

[0081] S3. Use a multi-source heterogeneous data fusion algorithm to fuse the preprocessed power grid operation data to obtain a power grid operation data set;

[0082] Furthermore, the preprocessed power grid operation data is further cleaned, and the Z-Score method is used to identify and remove outliers in the preprocessed power grid operation data;

[0083] The linear interpolation method is used to fill in the time periods with missing data, so that the preprocessed power grid operation data is aligned on the same time axis;

[0084] Assume that there are missing values between time points t1 and t2, and the linear interpolation formula is:

[0085]

[0086] where t1 and t2 are two known time points before and after the missing time period, Q(t1) and Q(t2) are the data values at these two time points respectively, and t is the time point;

[0087] Extract the voltage, current, and power data from the preprocessed power grid operation data, and assign weights according to the importance of voltage, current, and power;

[0088] Voltage stability is crucial for the safe operation of the entire power grid, so voltage is given a higher weight;

[0089] Current is directly related to the load capacity and heating of equipment, and is also one of the key monitoring indicators, with importance second only to voltage;

[0090] Power reflects the energy transmission efficiency and has a lower weight;

[0091] Assume that the weights of voltage, current, and power are w V 、w I and w P , and satisfy w V +w I +w P =1;

[0092] The weighted average method is used to fuse the voltage, current, and power data to obtain the power grid operation data set, and the expression is:

[0093] D f (t)=w V ·V(t)+w I ·I(t)+w P ·P(t);

[0094] where D f (t) is the fused power grid operation data value at time t, V(t), I(t), and P(t) are the voltage, current, and power data values at time t respectively, w V 、w I 、w PThe weights of voltage, current, and power respectively;

[0095] It should be noted that after further cleaning the preprocessed power grid operation data and using the Z-Score method to identify and remove outliers, the interpolation method is used to fill in the missing data to ensure that the data points in all power grid operation data are aligned on the same time axis, effectively improving the consistency and reliability of the data set. This makes the power grid operation data set generated by fusing voltage, current, and power data based on the weighted average method more accurate. By assigning weights according to the importance of different parameters, it can more accurately reflect the actual operation state of the power grid, laying a foundation for constructing an accurate power grid state evaluation model.

[0096] S4. Construct a power grid state evaluation model based on the regression analysis method, input the power grid operation data set into the power grid state evaluation model, and output the power grid state evaluation result;

[0097] Furthermore, select the power grid operation data set as the characteristic variable for constructing the model;

[0098] Construct a power grid state evaluation model based on the regression analysis method combined with the power grid operation data set, and adjust the model parameters to optimize the goodness of fit. The expression is:

[0099]

[0100] where y(t) is the power grid state evaluation model, β0 is the intercept term, β i is the coefficient of the i-th feature, is the value of the i-th feature variable of time t extracted from the data set Q, ∈ is the error term, and i is the index variable;

[0101] Input the power grid operation data set into the power grid state evaluation model, and output the power grid state evaluation result. The expression is:

[0102]

[0103] where, is the value of the i-th feature variable of time t extracted from the data set Q, β0 is the intercept term, B i is the model training parameter, and R(t j ) is the power grid state evaluation result;

[0104] The evaluation result includes indicators such as prediction error;

[0105] It should be noted that by selecting the linear regression analysis method to construct the power grid state evaluation model, the future change trend of the power grid state can be predicted based on historical data. By continuously adjusting the model parameters to optimize the fitting degree, the prediction accuracy of the model can be improved. Inputting new power grid operation data into the constructed model can not only obtain the evaluation results of the current power grid state, but also improve the model performance according to error analysis.

[0106] S5. Based on the power grid state evaluation results, adopt a monitoring method to monitor power grid equipment, identify fault points, obtain power grid equipment fault information. Based on the power grid equipment fault information, adopt a reinforcement learning algorithm to optimize the power grid state evaluation model, and obtain an optimized power grid state evaluation model;

[0107] Furthermore, based on the preprocessed power grid operation data, set the power grid state threshold;

[0108] Compare the power grid state evaluation result R(t j ) with the power grid state threshold, and take the value greater than the power grid state threshold as an outlier. The expression is:

[0109]

[0110] where A(t) is the outlier mark at time t;

[0111] Adopt the statistical method IQR to detect the location information of the fault point corresponding to the outlier;

[0112] According to the location information of the fault point, use the fault diagnosis algorithm for clustering analysis, learn from historical fault data, establish a fault mode library, compare the new fault data with the patterns in the library, and determine the fault type;

[0113] Organize the location of the fault point, the fault type and related information into a report to obtain the power grid equipment fault information;

[0114] Based on the power grid state evaluation model, adopt digital twin technology to establish a digital twin model of power grid equipment, set the state space, and dynamically simulate the performance of power grid equipment under various operating conditions;

[0115] The state space includes voltage level, load rate and temperature;

[0116] The operating conditions include load changes, voltage and current fluctuations, and equipment faults and maintenance conditions;

[0117] Adopt the DQN algorithm to define the reward function. The expression is:

[0118]

[0119] where B is the value of the reward function;

[0120] Give positive rewards when the power grid equipment is in a safe and stable state;

[0121] Give negative punishments when faults occur in the power grid equipment;

[0122] Give additional rewards when faults in the power grid equipment are detected and repaired;

[0123] Add the power grid equipment fault information as a feature to the state space and output the updated state space;

[0124] Input the updated state space into the power grid state evaluation model to obtain the optimized power grid state evaluation model and the optimized power grid state evaluation result;

[0125] It should be noted that based on the preprocessed power grid operation data, set the power grid state threshold, compare the power grid state evaluation result with it to identify outliers, then use the statistical method IQR to detect the location information of the fault point, and then establish a fault mode library through clustering analysis of the fault diagnosis algorithm, which can effectively improve the recognition accuracy of fault types. In addition, by using digital twin technology and DQN algorithm to optimize the power grid state evaluation model, the dynamic simulation of the power grid performance under different operating conditions is realized, and the ability of the model to cope with complex environments is improved. This not only enhances the adaptability and flexibility of the power grid state evaluation model, but also can simulate the performance of the power grid under various operating conditions, so as to realize more effective fault prevention and management strategies.

[0126] S6. Based on the optimized power grid state evaluation model, adopt an intelligent scheduling algorithm to formulate a power grid control strategy;

[0127] Furthermore, select the genetic algorithm GA as the intelligent scheduling algorithm;

[0128] Execute the GA algorithm, and search for the optimal control strategy based on the state evaluation result provided by the optimized power grid state evaluation model;

[0129] When the load rate of a certain area is detected to be close to the rated capacity, increase the power generation in this area and transfer part of the load to other areas;

[0130] When the voltage level of a certain area is detected to be lower than the set power grid state threshold, adjust the tap position of the transformer and start the reactive power compensation equipment to increase the voltage level;

[0131] When a power grid fault is detected, isolate the fault area and adjust the load distribution;

[0132] It should be noted that the genetic algorithm GA is selected as the intelligent scheduling algorithm, and the optimal control strategy is searched based on the state evaluation results provided by the optimized power grid state evaluation model. The power generation allocation can be adjusted or the reactive power compensation equipment can be started according to parameters such as the actual load rate and voltage level. At the same time, when a power grid fault is detected, the fault area can be quickly isolated and the load can be redistributed, significantly enhancing the stability and flexibility of the power grid and ensuring the safety and reliability of the power supply method.

[0133] This embodiment also provides a computer device applicable to the case of the power grid information control method based on the digital twin model, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the power grid information control method based on the digital twin model as proposed in the above embodiment.

[0134] This computer device can be a terminal. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad set on the outer shell of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0135] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method for controlling power grid information based on a digital twin model as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, abbreviated as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, abbreviated as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, abbreviated as EPROM), programmable read-only memory (Programmable Red-Only Memory, abbreviated as PROM), read-only memory (Read-Only Memory, abbreviated as ROM), magnetic memory, flash memory, a magnetic disk or an optical disc.

[0136] In summary, the present invention calculates the local mean and standard deviation of the received original power grid operation data through an edge computing device and performs standardization processing, achieving data cleaning and preliminary analysis. It not only filters out outliers but also standardizes the data, facilitating further processing and analysis. By further cleaning the preprocessed power grid operation data and using the Z-Score method to remove outliers, and filling in the missing data using the interpolation method, and finally fusing the voltage, current, and power data using the weighted average method, the present invention realizes the unification and optimization of the data set, solves the problem of incomplete data, ensures that all data points are aligned on the same time axis, is conducive to constructing a more accurate power grid state evaluation model, selects the power grid operation data set as the feature variable, and constructs a power grid state evaluation model based on the regression analysis method in combination with the power grid operation data set, realizing the quantitative evaluation of the power grid state.

[0137] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A power grid information control method based on a digital twin model, characterized in that: include: Use sensors to collect data from power grid nodes to obtain original power grid operation data; The original power grid operation data is preprocessed by edge computing processing method to obtain preprocessed power grid operation data; A multi-source heterogeneous data fusion algorithm is used to fuse the pre-processed power grid operation data to obtain a power grid operation data set; Construct a power grid state assessment model based on the regression analysis method, input the power grid operation data set into the power grid state assessment model, and output the power grid state assessment result; Based on the grid state assessment results, a monitoring method is used to monitor the grid equipment, and the fault point is identified to obtain the grid equipment fault information. Based on the grid equipment fault information, a reinforcement learning algorithm is used to optimize the grid state assessment model, and an optimized grid state assessment model is obtained; Based on the optimized grid state assessment model, the intelligent dispatching algorithm is used to formulate the grid control strategy.

2. The power grid information control method based on the digital twin model according to claim 1, characterized in that: The specific steps of using sensors to collect data from power grid nodes to obtain original power grid operation data are as follows: Deploy high-precision smart sensors at substations, distribution boxes, and transmission lines in the power grid; Use high-precision intelligent sensors to collect voltage, current and power data of substations and distribution boxes; The Network Time Protocol (NTP) is used to provide timestamps for the voltage, current, and power data of substations and distribution boxes. When no new data arrives within a certain period of time, it is marked that there is data missing in that period of time. The voltage, current and power data are collected as the original grid operation data.

3. The power grid information control method based on the digital twin model according to claim 2, characterized in that: The edge computing processing method is used to preprocess the original power grid operation data to obtain the preprocessed power grid operation data. The specific steps are: Transmitting raw grid operation data to edge computing devices via high-speed communication networks 5G; Start the edge computing device and use the sliding window technology to calculate the local mean and standard deviation of the original power grid operation data, clean the original power grid operation data, and filter out data points that are out of range; The cleaned original power grid operation data is standardized to serve as pre-processed power grid operation data; The preprocessed grid operation data is stored in the database of the edge computing device and uploaded to the central server.

4. The power grid information control method based on the digital twin model according to claim 3, characterized in that: The multi-source heterogeneous data fusion algorithm is used to fuse the pre-processed power grid operation data to obtain a power grid operation data set. The specific steps are: The pre-processed power grid operation data is further cleaned, and the Z-Score method is used to identify and remove outliers in the pre-processed power grid operation data; Use interpolation to fill in the time periods with missing data so that the pre-processed power grid operation data are aligned on the same time axis; The voltage, current and power data are extracted from the preprocessed power grid operation data, and the voltage, current and power data are fused using the weighted average method to obtain the power grid operation data set.

5. The power grid information control method based on the digital twin model according to claim 4, characterized in that: The grid state assessment model is constructed based on the regression analysis method, a grid operation data set is input into the grid state assessment model, and a grid state assessment result is output. The specific steps are as follows: The power grid operation data set is selected as the characteristic variable for building the model; A power grid status assessment model is constructed based on regression analysis method combined with power grid operation data set; The power grid operation data set is input into the power grid status assessment model, and the power grid status assessment results are output.

6. The power grid information control method based on the digital twin model according to claim 5, characterized in that: Based on the grid state assessment result, the monitoring method is used to monitor the grid equipment, identify the fault point, and obtain the grid equipment fault information. The specific steps are: Based on the pre-processed grid operation data, set the grid status threshold; Compare the grid state assessment result with the grid state threshold, and regard the value greater than the grid state threshold as an abnormal value; The statistical method IQR is used to detect the location information of the fault point corresponding to the abnormal value; According to the location information of the fault point, cluster analysis is performed using the fault diagnosis algorithm to learn the historical fault data, establish a fault mode library, compare the new fault data with the mode in the library, and determine the fault type; The location of the fault point, fault type and related information are compiled into a report to obtain the fault information of the power grid equipment.

7. The power grid information control method based on the digital twin model according to claim 6, characterized in that: The method of optimizing the power grid state assessment model based on the power grid equipment fault information by using a reinforcement learning algorithm and obtaining the optimized power grid state assessment model comprises the following specific steps: Based on the grid state assessment model, digital twin technology is used to establish a digital twin model of grid equipment, set the state space, and dynamically simulate the performance of grid equipment under various operating conditions; The state space includes voltage level, load factor and temperature; The operating conditions include load changes, voltage and current fluctuations, and equipment failures and maintenance; Use the DQN algorithm to define the reward function; Positive rewards are given when power grid equipment is in a safe and stable state; Negative penalties are imposed when grid equipment fails; Additional rewards are given when faults in grid equipment are discovered and repaired; Add the power grid equipment fault information as a feature to the state space, and output the updated state space; The updated state space is input into the power grid state assessment model, and an optimized power grid state assessment model and an optimized power grid state assessment result are obtained.

8. The power grid information control method based on the digital twin model according to claim 7, characterized in that: The optimized grid state assessment model is used to formulate a grid control strategy using an intelligent dispatching algorithm. The specific steps are as follows: Select genetic algorithm GA as the intelligent scheduling algorithm; Execute the GA algorithm to search for the optimal control strategy based on the state assessment results provided by the optimized power grid state assessment model; When it is detected that the load rate of a certain area is close to the rated capacity, the power generation of the area is increased and part of the load is transferred to other areas; When it is detected that the voltage level in a certain area is lower than the set grid status threshold, the transformer tap position is adjusted and the reactive power compensation equipment is started to increase the voltage level; When a grid fault is detected, the faulty area is isolated and load distribution is adjusted.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the power grid information control method based on the digital twin model are implemented in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the power grid information control method based on the digital twin model are implemented in any one of claims 1 to 8.

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