Method and system for identifying weak points in power grid operation based on transmission and distribution network topology

Abstract

The present invention discloses a method and system for identifying weak points in power grid operation based on the topological structure of a transmission and distribution network, relating to the technical field of power grid operation. The method effectively reduces computing costs, utilizes graph theory and network simplification technology to reduce model complexity, optimizes the use of computing resources, and improves data processing efficiency, thereby achieving accurate identification under limited computing performance; by reading and evaluating data quality indicators, and performing data enhancement and dimensionless processing, the integrity and accuracy of the data are improved, ensuring the stability of the system when data quality fluctuates; adopts a deep learning algorithm, optimizes the model training process through cross-validation and hyperparameter tuning, and uses fault and operation history data for supervised learning, reducing dependence on a large amount of labeled data and computing resources, thereby achieving efficient and accurate identification of weak points in power grid operation.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid operation, and in particular to a method and system for identifying weak points in power grid operation based on a transmission and distribution network topology. Background Art

[0002] Grid operational vulnerability identification methods aim to identify weak links within the grid that could lead to system instability or failure. As grids grow in size and complexity, traditional grid analysis methods are increasingly exposed to limitations. To address this issue, researchers and engineers have introduced advanced analytical tools and technologies, including big data analytics, machine learning, and deep learning algorithms. These technologies can extract valuable information from massive amounts of grid operational data, enabling more accurate identification of potential risk points and system bottlenecks.

[0003] Although the grid operation weak point identification technology has many advantages, it also has some technical disadvantages when applied to the subsequent planning and optimization of the grid;

[0004] 1. High computing cost. Identifying weak points in power grid operation requires processing large amounts of real-time and historical data, which places high demands on computing performance.

[0005] 2. Insufficient data quality requirements. Existing methods have high requirements for data integrity and accuracy. If the collected data is missing, erroneous, or noisy, it will directly affect the accuracy and reliability of the identification results. Fluctuations in data quality will have a negative impact on the stability of the system.

[0006] 3. Model training is complex. Although advanced technologies such as deep learning have significantly improved recognition accuracy, the model training and tuning process is complex and requires a large amount of labeled data and computing resources. Summary of the Invention

[0007] In view of the above-mentioned problems, the present invention is proposed.

[0008] Therefore, the technical problem solved by the present invention is that the existing recognition methods have optimization problems such as high cost, poor data quality, and huge calculation amount.

[0009] To solve the above technical problems, the present invention provides the following technical solution: a method for identifying weak points in power grid operation based on the topology of the transmission and distribution network, comprising:

[0010] Collect and pre-process the transmission and distribution network topology data of the power grid, establish a power grid topology model, and use graph theory and network simplification technology to simplify the model;

[0011] Read data quality information from monitoring devices and sensors, calculate and evaluate the data quality information, and adjust the data quality information based on the evaluation results;

[0012] Based on the simplified grid topology model and adjusted data quality information, key features are extracted and a feature representation method is used to convert the topology information into feature vectors that can be processed by the model.

[0013] Use deep learning algorithms to train the model on the extracted feature matrix; adjust the model parameters through cross-validation and hyperparameter tuning;

[0014] Deploy the trained model in the real-time monitoring system of the power grid, and analyze and process the real-time collected operation data; calculate and obtain the operation defects based on the model's prediction results;

[0015] Based on the assessment results, the operational weaknesses of the power grid can be identified in real time, early warnings and recommended measures can be provided, and fault handling and prevention can be carried out.

[0016] As a preferred solution of the method for identifying weak points in power grid operation based on the transmission and distribution network topology structure of the present invention, the method of collecting the transmission and distribution network topology structure data of the power grid and performing preprocessing includes collecting the transmission and distribution network topology structure related data from the GIS system, the SCADA system, the equipment manual and the historical records, and constructing the transmission and distribution network data set after data cleaning and dimensionless processing;

[0017] Based on the collected data, the node and line models of the power grid are constructed and the electrical parameters of the equipment are entered;

[0018] The grid topology model includes the connection relationship and electrical parameters of each node, line, and device;

[0019] The use of graph theory and network simplification technology to simplify the model includes identifying and retaining key nodes and lines, and deleting redundant parts, thereby forming a simplified topology model for reflecting the actual situation of the power grid.

[0020] As a preferred solution of the method for identifying weak points in power grid operation based on the transmission and distribution network topology structure of the present invention, the data quality information includes data missing rate, data calibration frequency, data offset frequency and data redundancy rate;

[0021] The computational evaluation of the data quality information includes performing dimensionless processing on the data quality information and then calculating and obtaining a quality index Zlzs:

[0022]

[0023] Among them, Lqs represents the data missing rate, Ljz represents the data calibration frequency, Lpy represents the data offset frequency, and Lry represents the data redundancy rate;

[0024] The evaluation result is generated by comparing the preset quality threshold Q with the quality index Zlzs:

[0025] If the preset quality threshold Q ≤ the quality index Zlzs, it means that the data quality is qualified, and a first quality result is generated at this time; if the preset quality threshold Q > the quality index Zlzs, it means that the data quality is unqualified, and a second quality result is generated at this time;

[0026] When the quality index Zlzs is the first quality result, it directly enters the subsequent analysis and processing stage without additional adjustment; when the quality index Zlzs is the second quality result, the data quality is adjusted, including adjusting the data collection frequency, adjusting the data calibration frequency, adjusting the data synchronization mechanism, performing data merging, data interpolation and data synthesis;

[0027] After the data quality adjustment is completed, the evaluation quality index Zlzs is calculated again until the quality index Zlzs is higher than the preset quality threshold Q.

[0028] As a preferred solution of the method for identifying weak points in power grid operation based on the topology of the transmission and distribution network described in the present invention, the key features include the electrical characteristics of the nodes, the load conditions of the lines and the fault history records;

[0029] The electrical characteristics of the node include voltage, current and power factor; the load condition of the line includes load current, line resistance and reactance; the fault history records include the number of faults, fault type and fault time of each node and line;

[0030] By extracting the features of voltage, current and power factor of each node, a node feature set is formed;

[0031] By extracting the characteristics of the load current, resistance and reactance of each line, a line feature set is formed;

[0032] By counting the number of faults, fault types and fault time of each node and line, feature extraction is performed to form a fault feature set;

[0033] The characteristic vector includes: the characteristic representation of the node characteristic vector is: the electrical characteristic parameters of each node are represented as the characteristic vector V a =[v a ,i a , pf a ], where v a represents the voltage at node a, I a represents the current at node a, pf a represents the power factor of node a;

[0034] The characteristic representation of the line characteristic vector is: the load condition parameter of each line is represented as the characteristic vector I b =[i b , r b , x b ], where i b represents the load current of line b, r b represents the resistance of circuit b, x b represents the reactance of line b;

[0035] The characteristic representation of the fault feature vector is: the fault history of each node and line is represented as the feature vector F c =[f c , t c , n c ], where f c represents the number of failures on line c, t c Indicates the fault type of line c, n c represents the fault time of line c;

[0036] The node feature vectors, line feature vectors, and fault feature vectors are combined with topology information to generate a comprehensive feature vector. Using graph theory, the feature vectors of nodes, lines, and faults are connected according to the grid topology to construct an overall feature graph G = (D, E), where D is the node set and E is the line set.

[0037] Format the comprehensive feature vector; and normalize the feature vector to generate the final model-processable feature vector set X=[V, I, F];

[0038] V=[V1, V2, …, V q ]; I = [I1, I2, …, I m ]; F=[F1, F2, …, F e ];

[0039] Among them, V q Represents the eigenvector of the qth node, I m represents the feature vector of the mth line, F e represents the fault feature vector of the e-th node or line, V represents the feature vector set of the node, I represents the feature vector set of the line, and F represents the fault feature vector set.

[0040] As a preferred solution of the method for identifying weak points in power grid operation based on the topology of the transmission and distribution network described in the present invention, the deep learning algorithm includes setting initial model parameters by selecting the convolutional neural network structure and the number of layers;

[0041] Use the extracted feature matrix as training data, use the back propagation algorithm to train the model, and update the model parameters to minimize the loss function;

[0042] During the training process, cross-validation technology is applied to divide the data into training set and validation set to evaluate the generalization ability of the model;

[0043] Adjust the learning rate, batch size, and regularization parameters through hyperparameter tuning;

[0044] Use existing fault and operation history data for supervised learning;

[0045] Through multiple rounds of training and validation, the model parameters are continuously adjusted until the model reaches the expected accuracy and stability;

[0046] The trained model is applied to actual power grid operation data to identify and predict weak points in the power grid.

[0047] As a preferred solution of the method for identifying weak points in power grid operation based on the topology of the transmission and distribution network described in the present invention, the node feature vector V is extracted. d =[v d ,i d , pf d ]、Line characteristic vector I j =[ i j , r j , x j ] and fault feature vector F k =[f k , t k , n k ], and after dimensionless processing, the operating defect value Yxqx is calculated:

[0048]

[0049] Where, e represents the number of node feature vectors, r represents the number of line feature vectors, and t represents the number of fault feature vectors; V d Represents the value of the d-th node feature vector, I j represents the value of the j-th line feature vector, F k represents the value of the kth fault feature vector;

[0050] By comparing the preset defect threshold W with the operating defect value Yxqx, the result is generated:

[0051] If the operating defect value Yxqx is less than the defect threshold W, it indicates that the defect is normal or there is no defect, and a first defect result is generated; if the operating defect value Yxqx is greater than or equal to the defect threshold W, it indicates that the defect is abnormal, and a second defect result is generated to identify the operating weak points of the power grid.

[0052] As a preferred embodiment of the method for identifying weak points in power grid operation based on the transmission and distribution network topology structure of the present invention, the method includes: identifying weak points in power grid operation in real time based on the evaluation results, providing early warnings and recommended measures, and performing fault processing and prevention, including: when the second defect result is generated, recording relevant node and line information, and determining the specific area or equipment in the power grid where the problem exists;

[0053] Establish an early warning mechanism to notify relevant operation and maintenance personnel and management personnel through system alarms when operational weaknesses are detected. Detailed warning information is also provided, including the specific location, type, possible cause, and recommended treatment measures. Recommended treatment measures include load adjustment, equipment maintenance, line adjustment and modification, and configuration adjustment.

[0054] Locate the fault point, combine historical data and model prediction results, determine the cause of the fault, and implement an emergency repair plan; at the same time, record the fault handling process and results and update the fault database; formulate a detailed preventive maintenance plan, including inspection and maintenance of key equipment and lines within a fixed period.

[0055] A power grid operation weak point identification system based on the transmission and distribution network topology structure using any method described in the present invention, wherein:

[0056] The acquisition unit collects and pre-processes the transmission and distribution network topology data of the power grid, establishes a power grid topology model, and uses graph theory and network simplification technology to simplify the model;

[0057] An adjustment unit reads data quality information from monitoring devices and sensors, calculates and evaluates the data quality information, and adjusts the data quality information based on the evaluation results;

[0058] The processing unit extracts key features based on the simplified power grid topology model and the adjusted data quality information, and uses a feature representation method to convert the topology structure information into a feature vector that can be processed by the model;

[0059] The model optimization unit uses deep learning algorithms to train the model on the extracted feature matrix; it adjusts the model parameters through cross-validation and hyperparameter tuning;

[0060] The computing unit deploys the trained model in the real-time monitoring system of the power grid and analyzes and processes the real-time collected operating data. It calculates and obtains operating defects based on the model's prediction results.

[0061] The output unit identifies the operational weaknesses of the power grid in real time based on the assessment results, provides early warnings and recommended measures, and performs fault handling and prevention.

[0062] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein: when the processor executes the computer program, the steps of any one of the methods of the present invention are implemented.

[0063] A computer-readable storage medium stores a computer program, wherein: when the computer program is executed by a processor, the steps of any one of the methods of the present invention are implemented.

[0064] The present invention provides the following beneficial effects: The method for identifying weak points in power grid operation based on the transmission and distribution network topology effectively reduces model complexity by applying graph theory and network simplification techniques. By identifying and retaining key nodes and lines and removing redundant components, the resulting power grid topology model becomes more concise, reducing computational complexity. Furthermore, by combining deep learning algorithms with efficient feature vector representation methods, the method optimizes computing resource utilization, improves data processing and analysis efficiency, and thus reduces the system's computational cost. Ultimately, the method achieves the goal of accurately and rapidly identifying weak points in power grid operation under limited computing power conditions. Data integrity and accuracy are ensured by reading data quality information from monitoring devices and sensors and performing comprehensive computational assessments and adjustments to the data quality. This includes monitoring and adjusting key indicators such as data loss rate, data calibration frequency, data offset frequency, and data redundancy rate. Dimensionless data processing and data enhancement techniques improve data quality, reducing the impact of data noise and errors, thereby increasing the accuracy and reliability of identification results and ensuring stable operation of the system even when data quality fluctuates. A deep learning algorithm was adopted, and the model training process was optimized through cross-validation and hyperparameter tuning techniques. Existing fault and operation history data were used for supervised learning to improve the model's recognition accuracy. At the same time, the normalization and unified processing of feature vectors made the construction of the feature matrix more efficient, reducing the dependence on large amounts of labeled data and computing resources. Through multiple rounds of training and verification, the model parameters were continuously adjusted to ensure that the model achieved the expected accuracy and stability, thus overcoming the complex difficulties of model training and achieving efficient and accurate identification of weak points in power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0066] Figure 1 This is an overall flow chart of the method for identifying weak points in power grid operation based on the transmission and distribution network topology provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0067] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0068] Example 1, with reference to Figure 1 , which is an embodiment of the present invention, provides a method for identifying weak points in power grid operation based on the topology of the transmission and distribution network, including: first, by applying graph theory and network simplification technology, the complexity of the power grid topology model is reduced and the computing cost is reduced; second, comprehensive evaluation and adjustment of data quality are performed to improve the integrity and accuracy of the data, and ensure the stability of the system when the data quality fluctuates; through feature extraction and feature representation methods, complex topology structure information is converted into feature vectors that can be processed by the model, thereby improving data processing efficiency; using a deep learning algorithm, through cross-validation and hyperparameter tuning, the model training process is optimized, and supervised learning is performed using fault and operation history data, thereby improving identification accuracy; finally, the trained model is deployed in a real-time monitoring system, realizing dynamic analysis and processing of real-time data, timely identifying weak points in power grid operation, providing early warnings and recommended measures, effectively preventing and handling faults, and comprehensively improving the safety and reliability of the power grid.

[0069] Step 1: Collect and pre-process the transmission and distribution network topology data of the power grid to establish a detailed power grid topology model, including the connection relationship and electrical parameters of each node, line and equipment; at the same time, use graph theory and network simplification technology to simplify the model.

[0070] First, data related to the transmission and distribution network topology is collected from the GIS system, SCADA system, equipment manuals and historical records. After data cleaning and dimensionless processing, a transmission and distribution network data set is constructed. Secondly, based on the collected data, a node and line model of the power grid is constructed and the electrical parameters of the equipment are entered. Then, graph theory and network simplification techniques are applied to identify and retain key nodes and lines, and delete redundant parts, thereby forming a simplified topology model that reflects the actual situation of the power grid.

[0071] In this embodiment, the node model includes each device in the power grid, including transformers and switches, and records their electrical parameters, including voltage, current, and power factor; the line model includes each line in the power grid, and also records its electrical parameters, including resistance, reactance, and load; these model data are input into the system to lay the foundation for topological analysis and feature extraction of the power grid.

[0072] By applying network simplification techniques, critical nodes and lines are identified and retained, while redundant or low-impact components are removed. Specific techniques include: key node identification, which uses network centrality metrics, including betweenness centrality and closeness centrality, to identify nodes critical to grid operation; line screening, which selects lines with a significant impact on grid stability based on their load and electrical parameters; and redundancy removal, which uses network compression techniques to remove nodes and lines with minimal impact on grid function, simplifying the network structure.

[0073] The simplified node and line information is integrated to form a topological model that more clearly reflects the actual grid situation. The model's structure is further optimized, and through iterative adjustments and verification, it ensures that it accurately describes the grid's operating status and adapts to future data changes and expansion needs. This process helps improve the accuracy of grid analysis and weak point identification.

[0074] Step 2: Read data quality-related information from monitoring devices and sensors, perform calculations and evaluations on the data quality-related information, and adjust the data quality-related information based on the evaluation results, including preprocessing and data enhancement of the collected data.

[0075] By reading data quality related information, including data missing rate, data calibration frequency, data offset frequency and data redundancy rate, and performing dimensionless processing, the quality index Zlzs is calculated. The specific calculation formula is as follows:

[0076]

[0077] Where Lqs represents the data missing rate, Ljz represents the data calibration frequency, Lpy represents the data offset frequency, and Lry represents the data redundancy rate.

[0078] By comparing the preset quality threshold Q with the quality index Zlzs, the following evaluation results are generated:

[0079] If the preset quality threshold Q ≤ the quality index Zlzs, it means the data quality is qualified and a first quality result is generated. If the preset quality threshold Q > the quality index Zlzs, it means the data quality is unqualified and a second quality result is generated.

[0080] Based on the evaluation results of the quality index Zlzs, the following adjustments are made:

[0081] When the quality index Zlzs is the first quality result, the subsequent analysis and processing phase is directly entered without additional adjustments. When the quality index Zlzs is the second quality result, data quality adjustments are performed on the data, including adjusting the data collection frequency, adjusting the data calibration frequency, adjusting the data synchronization mechanism, performing data merging, data interpolation, and data synthesis.

[0082] After the data quality adjustment is completed, the evaluation quality index Zlzs is calculated again until the quality index Zlzs is higher than the preset quality threshold Q.

[0083] In this example, the data missing rate (Lqs) indicates the percentage of data lost or not recorded during the data collection process. A high missing rate can compromise data integrity and lead to inaccurate analysis results. By monitoring and reducing the data missing rate (Lqs), dataset quality can be improved, thereby ensuring the effectiveness of model training and power grid analysis.

[0084] Data calibration frequency, Ljz, refers to the frequency of data calibration and correction. Regular calibration helps correct for drift and errors in sensors or measurement equipment, ensuring data accuracy and consistency. A higher data calibration frequency, Ljz, ensures greater data accuracy, thereby improving the predictive performance and reliability of the model.

[0085] The data offset frequency Lpy indicates the frequency of data deviation, that is, the degree of deviation of the data from the actual value. Frequent offsets may lead to data inconsistency, affecting data quality and the accuracy of analysis results. Controlling and reducing the data offset frequency Lpy helps improve data stability and the prediction accuracy of the model.

[0086] The data redundancy rate Lry refers to the proportion of duplicate records in a data set; a high redundancy rate may lead to inefficient data processing and analysis; moderate redundancy can sometimes provide backup and redundant verification, but excessive redundancy wastes storage resources and may introduce noise; reducing the data redundancy rate Lry helps improve the efficiency and accuracy of data processing.

[0087] When the quality index Zlzs is the second quality result, the data quality is adjusted. The specific implementation process is as follows:

[0088] Adjust the data collection frequency: evaluate whether the current data collection time interval is too large or too small; if the collection frequency is too low, increase the collection frequency to obtain more intensive data; if the collection frequency is too high and generates too much redundant data, appropriately reduce the collection frequency to optimize data storage and processing efficiency; recalibrate the quality index Zlzs after adjustment.

[0089] Adjust data calibration frequency: Check whether the data calibration mechanism is timely, especially for possible drift or error problems of sensors or measurement equipment; optimize the calibration time interval based on historical error trends to ensure a balance between data calibration timeliness and accuracy; regenerate data after calibration and verify the improvement of the quality index Zlzs.

[0090] Adjust the data synchronization mechanism: verify the timestamps of multi-source data to ensure time synchronization between different data sources; if time offset or data loss is found, use the time alignment algorithm to correct it; recalculate the quality index Zlzs of the updated synchronized data.

[0091] Data merging and processing: De-duplication and integration of duplicate or correlated data from multiple sources to ensure that only unique versions of data for the same object are stored; weighted averaging, optimization algorithms and other technologies are used to fuse multi-source data to improve data accuracy and consistency; the merged data is used to re-evaluate Zlzs.

[0092] Data interpolation: For missing values ​​in the data collection process, linear interpolation, spline interpolation or time series-based prediction models are used to fill the missing parts; after the interpolation is completed, local verification is performed to ensure that the interpolation results do not introduce additional errors.

[0093] Data synthesis: In specific scenarios, multi-dimensional data is synthesized according to needs. For example, voltage and current data are calculated to generate power data to improve the depth of analysis. After data synthesis, Zlzs is recalculated to confirm the effect of the synthesis results on the overall quality improvement.

[0094] Quality adjustment measures make data more reliable, thereby improving the effectiveness of model training and prediction accuracy, and reducing the risks caused by data defects. Through appropriate adjustment measures, such as data synchronization and synthesis, data redundancy and inconsistency are avoided, thereby improving the overall stability and operational efficiency of the system. When data quality meets the requirements, unnecessary additional adjustments and processing are avoided, saving computing resources and processing time.

[0095] Step 3: Based on the topology and preprocessed operating data, extract key features, including the electrical characteristics of the nodes, the load conditions of the lines, and the fault history records; and use feature representation methods to convert the topology information into feature vectors that can be processed by the model.

[0096] The pre-processed operational data is adjusted data quality information. Node electrical characteristics include voltage, current, and power factor; line load conditions include load current, line resistance, and reactance; and fault history records include the number of faults, fault type, and fault duration for each node and line.

[0097] By collecting electrical parameters such as voltage, current, and power factor of each node, feature extraction is performed to form a node feature set; by collecting load parameters such as load current, resistance, and reactance of each line, feature extraction is performed to form a line feature set; by counting the number of faults, fault type, and fault time of each node and line, feature extraction is performed to form a fault feature set.

[0098] The characteristic representation of the node feature vector is: the electrical characteristic parameters of each node are represented as the feature vector V a =[v a ,i a , pf a ,…], where v a represents the voltage at node a, I a represents the current at node a, pf a Represents the power factor of node a, and so on.

[0099] The characteristic representation of the line characteristic vector is: the load condition parameter of each line is represented as the characteristic vector I b =[i b , r b , x b ,…], where i b represents the load current of line b, r b represents the resistance of circuit b, x b represents the reactance of line b, and so on.

[0100] The characteristic representation of the fault feature vector is: the fault history of each node and line is represented as the feature vector F c =[f c , t c , n c ,…], where f c represents the number of failures on line c, t c Indicates the fault type of line c, n c Indicates the fault time of line c, and so on.

[0101] The node feature vectors, line feature vectors, and fault feature vectors are fused with topology information to generate a comprehensive feature vector. Graph theory methods are used to connect the feature vectors of nodes, lines, and faults according to the grid topology to construct an overall feature graph G=(D, E), where D is the node set and E is the line set.

[0102] The comprehensive feature vector is formatted and normalized to generate the final model-processable feature vector set X=[V, I, F].

[0103] V=[V1, V2, …, Vq ]; I = [I1, I2, …, I m ]; F=[F1, F2, …, F e ].

[0104] Among them, V q Represents the eigenvector of the qth node, I m represents the feature vector of the mth line, F e represents the fault feature vector of the e-th node or line, V represents the feature vector set of the node, I represents the feature vector set of the line, and F represents the fault feature vector set.

[0105] In this embodiment, the electrical characteristics of the nodes, the load conditions of the lines, and the fault history records are extracted in detail and converted into feature vectors. This comprehensive data collection ensures an in-depth understanding of the operating status of the power grid and provides a rich information basis for subsequent analysis; the node feature vectors, line feature vectors, and fault feature vectors are combined with the power grid topology information to generate a comprehensive feature vector. This feature fusion effectively integrates data from different sources, so that the power grid model can fully reflect the operating status and potential problems of all aspects; the overall feature graph G=(V, E) is constructed through graph theory methods, which realizes the effective modeling of the power grid topology. This modeling method facilitates feature correlation analysis in complex power grid structures and provides a clearer view of power grid operation; through systematic feature extraction, fusion, and normalization, the stability and reliability of the model are significantly improved, and the interference of data noise on the results is reduced, thereby effectively improving the ability to identify weak points in the power grid and the accuracy of prediction.

[0106] Step 4: Use a deep learning algorithm to train a model on the extracted feature matrix; adjust the model parameters through cross-validation and hyperparameter tuning; and use existing fault and operation history data for supervised learning.

[0107] The deep learning model is initialized, and the initial model parameters are set by selecting the convolutional neural network structure and number of layers. Then, the extracted feature matrix is ​​used as training data, and the model is trained using the backpropagation algorithm, and the model parameters are updated to minimize the loss function. During the training process, cross-validation technology is applied to divide the data into training and validation sets to evaluate the generalization ability of the model. The learning rate, batch size and regularization parameters are adjusted through hyperparameter tuning. During the training process, supervised learning is performed using existing fault and operation history data. Through multiple rounds of training and verification, the model parameters are continuously adjusted until the model reaches the expected accuracy and stability. Finally, the trained model is applied to actual power grid operation data to identify and predict power grid weaknesses.

[0108] In this example, by initializing a deep learning model, selecting an appropriate convolutional neural network structure, and using a feature matrix for training, the accuracy of identifying grid weak points was effectively improved. Backpropagation and cross-validation techniques ensured the model's stability and generalization capabilities across training and validation sets, while hyperparameter tuning further optimized the model's performance. Ultimately, through multiple rounds of training and validation, the model's high accuracy and reliability were ensured. When applied to actual grid operation data, this technical solution was able to accurately identify grid weak points and enhance the stability and security of grid operations.

[0109] Step 5: Deploy the trained model in the real-time monitoring system of the power grid and analyze and process the real-time collected operation data; calculate the operation defect value Yxqx through the prediction results of the model and evaluate it.

[0110] By extracting the node feature vector V d =[v d ,i d , pf d ]、Line characteristic vector I j =[ i j , r j , x j ] and fault feature vector F k =[f k , t k , n k ], and after dimensionless processing, the operating defect value Yxqx is calculated:

[0111]

[0112] Where, e represents the number of node feature vectors, r represents the number of line feature vectors, and t represents the number of fault feature vectors; V d Represents the value of the d-th node feature vector, I j represents the value of the j-th line feature vector, F k Represents the value of the kth fault feature vector.

[0113] By comparing the preset defect threshold W with the operating defect value Yxqx, the following results are generated:

[0114] If the operating defect value Yxqx is less than the defect threshold W, it indicates that the defect is normal or there is no defect, and a first defect result is generated. If the operating defect value Yxqx is greater than or equal to the defect threshold W, it indicates that the defect is abnormal, and a second defect result is generated. At this time, the operating weak points of the power grid are identified.

[0115] In this embodiment, the calculation of the running defect value Yxqx is performed by extracting the node feature vector V d , line characteristic vector I jand fault feature vector F k , and dimensionless processing is performed, which helps to comprehensively evaluate the operating status of the power grid; the specific calculation formula provides a comprehensive defect value measurement indicator; by comparing with the preset defect threshold W, it can clearly distinguish between normal and abnormal operating states.

[0116] Step 6: Based on the assessment results, identify the operational weaknesses of the power grid in real time, provide early warnings and recommended measures, and perform fault handling and prevention.

[0117] Based on the second defect results, the grid's operational weaknesses are identified, warnings are issued, measures are recommended, and fault prevention and handling are carried out. The specific contents are as follows:

[0118] When the second defect result is generated, the relevant node and line information is recorded, and the specific area or equipment with the problem in the power grid is determined; secondly, an early warning mechanism is established. When an operation weakness is detected, the system alarm is used to notify the relevant operation and maintenance personnel and management personnel, and detailed early warning information is provided, including the specific location, type, possible cause and recommended treatment measures of the weakness; recommended treatment measures include load adjustment, equipment maintenance, line adjustment and modification, and configuration adjustment.

[0119] Then locate the fault point, combine historical data and model prediction results to determine the cause of the fault, and formulate and implement an emergency repair plan; at the same time, record the fault handling process and results, and update the fault database; finally, formulate a detailed preventive maintenance plan, including inspection and maintenance of key equipment and lines within a fixed period.

[0120] By establishing an early warning mechanism, once an abnormal defect is identified, the system alarm can be used to promptly notify operation and maintenance personnel, provide detailed early warning information and recommended measures, and effectively reduce the occurrence of accidents; formulate emergency repair plans and record the fault handling process, thereby improving fault handling efficiency; at the same time, through preventive maintenance plans, regular inspections and maintenance of key equipment enhance the overall stability and security of the power grid.

[0121] On the other hand, this embodiment also provides a power grid operation weak point identification system based on the transmission and distribution network topology structure, which includes:

[0122] The acquisition unit collects the transmission and distribution network topology data of the power grid and performs preprocessing, establishes the power grid topology model, and uses graph theory and network simplification technology to simplify the model.

[0123] The adjustment unit reads data quality information from monitoring equipment and sensors, calculates and evaluates the data quality information, and adjusts the data quality information based on the evaluation results.

[0124] The processing unit extracts key features based on the simplified power grid topology model and the adjusted data quality information, and adopts a feature representation method to convert the topology structure information into a feature vector that can be processed by the model.

[0125] The model optimization unit uses deep learning algorithms to train the model on the extracted feature matrix and adjusts the model parameters through cross-validation and hyperparameter tuning.

[0126] The computing unit deploys the trained model in the real-time monitoring system of the power grid and analyzes and processes the real-time collected operation data; it calculates and obtains operation defects through the prediction results of the model.

[0127] The output unit identifies the operational weaknesses of the power grid in real time based on the assessment results, provides early warnings and recommended measures, and performs fault handling and prevention.

[0128] If the above functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0129] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0130] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, and then editing, interpreting, or processing in another suitable manner as necessary, and then storing it in a computer memory.

[0131] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0132] Example 2 is an embodiment of the present invention, which provides a method for identifying weak points in power grid operation based on the topology of the transmission and distribution network. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0133] Experimental methods

[0134] 1. Subjects:

[0135] Select actual data of power grid operation in a certain region, including:

[0136] Grid topology data (number of nodes: 120, number of lines: 200).

[0137] Historical operation data (time span: 1 year, sampling frequency: 10 minutes).

[0138] Historical fault records (total: 50 times, involving line faults, node overloads, and voltage instability).

[0139] 2. Experimental and control group settings:

[0140] Experimental group: The weak point identification method based on the transmission and distribution network topology structure was adopted.

[0141] Control group: The traditional method based on static fault tree and manual rule analysis was used.

[0142] 3. Experimental steps:

[0143] 1) Data preprocessing:

[0144] Simplified modeling of the power grid topology.

[0145] Process the collected historical operation data, calculate the quality index Zlzs, and adjust the data quality until it reaches the preset threshold Q.

[0146] 2) Feature extraction and model training:

[0147] Experimental group: Extracted feature vectors based on node, line, and fault history records, constructed a feature graph G=(V,E)G=(V,E)G=(V,E), and used deep learning model training and tuning.

[0148] Control group: Based on fault tree modeling, a rule set for building nodes and lines was constructed.

[0149] 3) Real-time data testing:

[0150] Input real-time operation data (120 hours) for analysis, and record the accuracy of defect identification, response time, and warning effect.

[0151] Quantitative indicators:

[0152] Defect recognition accuracy:

[0153]

[0154] Response time: The time from real-time operation data input to the system output of defect identification results, unit: seconds.

[0155] Early warning coverage:

[0156] Operational weak point positioning error: the positional deviation between the weak point located by the system and the actual weak point, unit: kilometer.

[0157] Experimental results and analysis:

[0158] Please refer to Table 1 for the experimental results.

[0159] analyze:

[0160] Improved accuracy: The new method automatically extracts multi-dimensional features through a deep learning model, with a recognition rate 14.2% higher than the traditional method (based on rule analysis); Shortened response time: The simplified topology model and efficient algorithm significantly shorten data analysis time and improve real-time performance; Improved warning coverage: The fusion of feature extraction and supervised learning mechanisms of historical data makes warning capabilities more comprehensive; Reduced positioning error: The fusion of topology map modeling and feature map enables more accurate positioning of weak points.

[0161] Table 1 Data comparison table

[0162]

[0163] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for identifying weak points in power grid operation based on the topology of the transmission and distribution network, characterized in that: include: Collect and pre-process the transmission and distribution network topology data of the power grid, establish a power grid topology model, and use graph theory and network simplification technology to simplify the model; Read data quality information from monitoring devices and sensors, calculate and evaluate the data quality information, and adjust the data quality information based on the evaluation results; Based on the simplified grid topology model and adjusted data quality information, key features are extracted and a feature representation method is used to convert the topology information into feature vectors that can be processed by the model. Use deep learning algorithms to train the model on the extracted feature matrix; adjust the model parameters through cross-validation and hyperparameter tuning; Deploy the trained model in the real-time monitoring system of the power grid, and analyze and process the real-time collected operation data; calculate and obtain the operation defects based on the model's prediction results; Based on the assessment results, the system can identify the operational weaknesses of the power grid in real time, provide early warnings and recommended measures, and carry out fault handling and prevention. The collecting and preprocessing of the transmission and distribution network topology data of the power grid includes collecting the transmission and distribution network topology related data from the GIS system, the SCADA system, the equipment manual and the historical records, and constructing the transmission and distribution network data set after data cleaning and dimensionless processing; Based on the collected data, the node and line models of the power grid are constructed and the electrical parameters of the equipment are entered; The grid topology model includes the connection relationship and electrical parameters of each node, line, and device; The use of graph theory and network simplification technology to simplify the model includes identifying and retaining key nodes and lines, and deleting redundant parts, thereby forming a simplified topological model for reflecting the actual situation of the power grid; The data quality information includes data missing rate, data calibration frequency, data offset frequency and data redundancy rate; The computational evaluation of the data quality information includes performing dimensionless processing on the data quality information and then calculating and obtaining a quality index Zlzs: Among them, Lqs represents the data missing rate, Ljz represents the data calibration frequency, Lpy represents the data offset frequency, and Lry represents the data redundancy rate; The evaluation result is generated by comparing the preset quality threshold Q with the quality index Zlzs: If the preset quality threshold Q ≤ the quality index Zlzs, it means that the data quality is qualified, and a first quality result is generated at this time; if the preset quality threshold Q > the quality index Zlzs, it means that the data quality is unqualified, and a second quality result is generated at this time; When the quality index Zlzs is the first quality result, it directly enters the subsequent analysis and processing stage without additional adjustment; when the quality index Zlzs is the second quality result, the data quality is adjusted, including adjusting the data collection frequency, adjusting the data calibration frequency, adjusting the data synchronization mechanism, performing data merging, data interpolation and data synthesis; After the data quality adjustment is completed, the evaluation quality index Zlzs is calculated again until the quality index Zlzs is higher than the preset quality threshold Q.

2. The method for identifying weak points in power grid operation based on the topology of the transmission and distribution network according to claim 1, characterized in that: The key characteristics include the electrical characteristics of the node, the load condition of the line and the fault history; The electrical characteristics of the node include voltage, current and power factor; The load condition of the line includes load current, line resistance and reactance; The fault history records include the number of faults, fault types and fault time of each node and line; By extracting the features of voltage, current and power factor of each node, a node feature set is formed; By extracting the characteristics of the load current, resistance and reactance of each line, a line feature set is formed; By counting the number of faults, fault types and fault time of each node and line, feature extraction is performed to form a fault feature set; The characteristic vector includes: the characteristic representation of the node characteristic vector is: the electrical characteristic parameters of each node are represented as the characteristic vector V a =[v a ,i a , pf a ], where v a represents the voltage at node a, I a represents the current at node a, pf a represents the power factor of node a; The characteristic representation of the line characteristic vector is: the load condition parameter of each line is represented as the characteristic vector I b =[i b , r b , x b ], where i b represents the load current of line b, r b represents the resistance of circuit b, x b represents the reactance of line b; The characteristic representation of the fault feature vector is: the fault history of each node and line is represented as the feature vector F c =[f c , t c , n c ], where f c represents the number of failures on line c, t c Indicates the fault type of line c, n c represents the fault time of line c; The node feature vectors, line feature vectors, and fault feature vectors are combined with topology information to generate a comprehensive feature vector. Using graph theory, the feature vectors of nodes, lines, and faults are connected according to the grid topology to construct an overall feature graph G = (D, E), where D is the node set and E is the line set. Format the comprehensive feature vector; and normalize the feature vector to generate the final model-processable feature vector set X = [V, I, F]; V=[V1,V2,…,V q ];I=[I1,I2,…,I m ];F=[F1,F2,…,F e ]; Among them, V q Represents the eigenvector of the qth node, I m represents the feature vector of the mth line, F e represents the fault feature vector of the e-th node or line, V represents the feature vector set of the node, I represents the feature vector set of the line, and F represents the fault feature vector set.

3. The method for identifying weak points in power grid operation based on the transmission and distribution network topology structure according to claim 2, characterized in that: The deep learning algorithm includes setting initial model parameters by selecting the convolutional neural network structure and number of layers; Use the extracted feature matrix as training data, use the back propagation algorithm to train the model, and update the model parameters to minimize the loss function; During the training process, cross-validation technology is applied to divide the data into training set and validation set to evaluate the generalization ability of the model; Adjust the learning rate, batch size, and regularization parameters through hyperparameter tuning; Use existing fault and operation history data for supervised learning; Through multiple rounds of training and validation, the model parameters are continuously adjusted until the model reaches the expected accuracy and stability; The trained model is applied to actual power grid operation data to identify and predict weak points in the power grid.

4. The method for identifying weak points in power grid operation based on the topology of the transmission and distribution network according to claim 3, characterized in that: The operation defects include extracting the node feature vector V d =[v d ,i d , pf d ]、Line characteristic vector I j =[i j , r j , x j ] and fault feature vector F k =[f k , t k , n k ], and after dimensionless processing, the operating defect value Yxqx is calculated: Where, e represents the number of node feature vectors, r represents the number of line feature vectors, and t represents the number of fault feature vectors; V d Represents the value of the d-th node feature vector, I j represents the value of the j-th line feature vector, F k represents the value of the kth fault feature vector; By comparing the preset defect threshold W with the operating defect value Yxqx, the result is generated: If the operating defect value Yxqx is less than the defect threshold W, it indicates that the defect is normal or there is no defect, and a first defect result is generated; if the operating defect value Yxqx is greater than or equal to the defect threshold W, it indicates that the defect is abnormal, and a second defect result is generated to identify the operating weak points of the power grid.

5. The method for identifying weak points in power grid operation based on the topology of the transmission and distribution network according to claim 4, characterized in that: The real-time identification of operational weaknesses of the power grid based on the assessment results, providing early warnings and recommended measures, and performing fault handling and prevention includes, when the second defect result is generated, recording relevant node and line information, and determining the specific area or device in the power grid where the problem exists; Establish an early warning mechanism to notify relevant operation and maintenance personnel and management personnel through system alarms when operational weaknesses are detected. Detailed warning information is also provided, including the specific location, type, possible cause, and recommended treatment measures. Recommended treatment measures include load adjustment, equipment maintenance, line adjustment and modification, and configuration adjustment. Locate the fault point, combine historical data and model prediction results, determine the cause of the fault, and implement an emergency repair plan; at the same time, record the fault handling process and results and update the fault database; formulate a detailed preventive maintenance plan, including inspection and maintenance of key equipment and lines within a fixed period.

6. A system for identifying weak points in power grid operation based on the transmission and distribution network topology structure, using the method according to any one of claims 1 to 5, characterized in that: The acquisition unit collects and pre-processes the transmission and distribution network topology data of the power grid, establishes a power grid topology model, and uses graph theory and network simplification technology to simplify the model; An adjustment unit reads data quality information from monitoring devices and sensors, calculates and evaluates the data quality information, and adjusts the data quality information based on the evaluation results; The processing unit extracts key features based on the simplified power grid topology model and the adjusted data quality information, and uses a feature representation method to convert the topology structure information into a feature vector that can be processed by the model; The model optimization unit uses deep learning algorithms to train the model on the extracted feature matrix; it adjusts the model parameters through cross-validation and hyperparameter tuning; The computing unit deploys the trained model in the real-time monitoring system of the power grid and analyzes and processes the real-time collected operating data. It calculates and obtains operating defects based on the model's prediction results. The output unit identifies the operational weaknesses of the power grid in real time based on the assessment results, provides early warnings and recommended measures, and performs fault handling and prevention.

7. A computer device comprising: memory and processor; The memory stores a computer program, wherein the processor implements the steps of the method according to any one of claims 1 to 5 when executing the computer program.

8. 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 method according to any one of claims 1 to 5 are implemented.

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