A method, device, terminal equipment and storage medium for correcting power grid topology

Abstract

The present invention discloses a method, apparatus, terminal equipment and storage medium for correcting a power grid topology structure. A telesignaling adjacency matrix for characterizing the connection status between nodes in the current power grid topology structure and a telemetering matrix for characterizing the voltage value of each node can be input into a preset neural network model, so that the neural network model can be based on the extracted node features and then, based on the similarity comparison results of the node features between two adjacent nodes, modify the nodes with incorrect connection status in the telesignaling adjacency matrix. This identification and correction process is automated and does not require manual intervention, which not only improves the error correction efficiency and accuracy, but also, based on the modified telesignaling adjacency matrix, can detect and correct the connection status of each node in the telesignaling data in real time and accurately, making the topological analysis of the power grid more accurate and ensuring the stable operation of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of topology correction, and in particular to a method, device, terminal equipment and storage medium for correcting a power grid topology structure. Background Art

[0002] An accurate topology ensures stable grid operation under all operating conditions, preventing power failures and blackouts caused by topology errors. In a smart grid, power dispatch decisions must be made based on the grid's real-time status. If the grid topology is incorrect, resulting in inaccurate analysis, scheduling decisions will be biased, potentially triggering a chain reaction that further impacts the grid's security and stability.

[0003] During actual operation, telesignaling data may contain errors due to data collection errors, equipment failures, and other reasons, resulting in inaccurate topology analysis results. Therefore, it is necessary to promptly analyze and correct errors in telesignaling data to provide an accurate basis for subsequent power grid management and decision-making. However, when processing telesignaling and telemetering data from power grids, existing technologies often rely on manual judgment of data accuracy and completeness. When errors or anomalies are discovered, professionals manually correct them. However, this method is not only time-consuming and labor-intensive, but also prone to errors. As a result, it is impossible to accurately detect and correct the connection status of each node in the telesignaling data in real time, reducing the accuracy of power grid topology analysis and failing to ensure stable power grid operation. Summary of the Invention

[0004] The embodiments of the present invention provide a method, apparatus, terminal device and storage medium for correcting the topology of a power grid. By automatically identifying and correcting nodes with incorrect connection states in a telesignaling adjacency matrix, the connection states of each node in the telesignaling data can be detected and corrected in real time and accurately, making the topology analysis of the power grid more accurate. This can effectively solve the problem in the prior art that the accuracy and completeness of the data are often judged manually, and corrections are performed manually, resulting in the inability to detect and correct the telesignaling data in real time and accurately, resulting in low accuracy of the power grid topology analysis.

[0005] An embodiment of the present invention provides a method for correcting a power grid topology, comprising:

[0006] Obtaining a telesignaling adjacency matrix and a telemetering matrix; wherein the telesignaling adjacency matrix is ​​used to represent the connection status between each node in the current power grid topology; and the telemetering matrix is ​​used to represent the voltage value of each node;

[0007] The remote signaling adjacency matrix and the remote sensing matrix are input into a preset neural network model, so that the neural network model extracts node features corresponding to each node according to the connection status of the nodes in the remote signaling adjacency matrix and the voltage value of the nodes in the remote sensing matrix, and modifies the incorrect connection status in the remote signaling adjacency matrix according to the node feature similarity comparison result between the nodes, thereby generating a corrected remote signaling adjacency matrix;

[0008] Correcting the current power grid topology according to the modified remote signaling adjacency matrix;

[0009] The training process of the preset neural network model includes:

[0010] The telesignaling adjacency matrix sample data and the corresponding telemetering matrix sample data are used as corresponding training samples, and the actual corrected telesignaling adjacency matrix of each training sample is set;

[0011] Taking each training sample and the actual corrected telesignaling adjacency matrix of each training sample as input and the corrected telesignaling adjacency matrix predicted by each training sample as output, the neural network model to be trained is iteratively trained until the neural network model converges to generate a preset neural network model.

[0012] Preferably, the neural network model extracts node features corresponding to each node based on the connection status of the nodes in the telesignaling adjacency matrix and the voltage values ​​of the nodes in the telemetry matrix, including:

[0013] The neural network model converts the remote signaling adjacency matrix and the remote sensing matrix into a graph structure;

[0014] Based on the connection status and voltage value of each node in the graph structure, the node features of each node are generated; wherein the node features are used to characterize the node's own attribute information and the interaction information between nodes.

[0015] Preferably, the node features include: node pair features, node degree features, and node status features;

[0016] The node features for representing the node's own attribute information and the interaction information between nodes are generated based on the connection status and voltage value of each node in the graph structure, including:

[0017] According to the connection status between each node in the graph structure, a node pair feature is generated in the graph structure to characterize the strength of the relationship between two connected nodes;

[0018] The connection status of each node in the graph structure and the voltage value of each node are aggregated to generate node degree features and node status features; wherein the node degree feature is used to characterize the number of nodes connected to a node; the node status feature is used to characterize the degree of voltage difference between a node and other adjacent nodes.

[0019] Preferably, the modifying of the incorrect connection state in the remote signaling adjacency matrix according to the node feature similarity comparison result between the nodes, and then generating the modified remote signaling adjacency matrix, comprises:

[0020] For each node, generating an embedding vector representation corresponding to the node based on the attention weight between the node and the target node used to represent the connection with the node, and the node feature of the node;

[0021] The embedding vector representation of the node and the embedding vector representation corresponding to the target node are respectively compared for similarity, and then the connection state between the node and the target node in the remote signaling adjacency matrix is ​​modified according to each similarity comparison result, and then a modified remote signaling adjacency matrix is ​​generated.

[0022] Preferably, generating an embedding vector representation corresponding to the node based on an attention weight between the node and a target node representing a connection with the node, and a node feature of the node, includes:

[0023] The node connected to the node is taken as the target node, and the attention weights corresponding to the node and each target node are calculated through the attention mechanism;

[0024] Each attention weight, as well as the node pair feature, node degree feature, and node state feature of the node are aggregated to generate an embedding vector representation corresponding to the node.

[0025] Preferably, the connection status includes: whether a connection exists or not;

[0026] The node embedding vector representation and the target node embedding vector representation are respectively compared for similarity, and then the connection state between the node and the target node in the remote signaling adjacency matrix is ​​modified according to each similarity comparison result, including:

[0027] For each target node, determining a cosine similarity value between the embedding vector representation of the target node and the embedding vector representation of the node;

[0028] Comparing the cosine similarity value with a preset similarity threshold;

[0029] When it is determined that the cosine similarity value is less than a preset similarity threshold, if the connection state between the target node and the node is connected, the connection state between the target node and the node in the remote signaling adjacency matrix is ​​modified to not be connected; if the connection state between the target node and the node is not connected, the connection state between the target node and the node in the remote signaling adjacency matrix is ​​kept unchanged;

[0030] When it is determined that the cosine similarity value is greater than a preset similarity threshold, if the connection status between the target node and the node is that there is no connection, the connection status between the target node and the node in the telesignaling adjacency matrix is ​​modified to be connected; if the connection status between the target node and the node is that there is a connection, the connection status between the target node and the node in the telesignaling adjacency matrix remains unchanged.

[0031] Preferably, during each iterative training, a training sample is input into the neural network model so that the neural network model generates a predicted corrected telesignaling adjacency matrix corresponding to the training sample based on node features in the training sample;

[0032] The predicted corrected telesignaling adjacency matrix is ​​compared with the actual corrected telesignaling adjacency matrix corresponding to the training samples, and the network parameters of the neural network model are adjusted according to the comparison results.

[0033] Based on the above method embodiments, the present invention provides corresponding device embodiments.

[0034] An embodiment of the present invention provides a device for correcting a power grid topology structure, comprising: a matrix acquisition module, a matrix correction module, and a topology structure correction module;

[0035] The matrix acquisition module is used to obtain a telesignaling adjacency matrix and a telemetry matrix; wherein the telesignaling adjacency matrix is ​​used to represent the connection status between each node in the current power grid topology; and the telemetry matrix is ​​used to represent the voltage value of each node;

[0036] The matrix correction module is used to input the telesignaling adjacency matrix and the telemetry matrix into a preset neural network model, so that the neural network model extracts the node features corresponding to each node according to the connection status of the nodes in the telesignaling adjacency matrix and the voltage values ​​of the nodes in the telemetry matrix, and according to the node feature similarity comparison results between the nodes, corrects the incorrect connection status in the telesignaling adjacency matrix, and then generates a corrected telesignaling adjacency matrix;

[0037] The topology correction module is used to correct the current power grid topology according to the modified remote signaling adjacency matrix;

[0038] The training process of the preset neural network model includes:

[0039] The training process of the preset neural network model includes:

[0040] The telesignaling adjacency matrix sample data and the corresponding telemetering matrix sample data are used as corresponding training samples, and the actual corrected telesignaling adjacency matrix of each training sample is set;

[0041] Taking each training sample and the actual corrected telesignaling adjacency matrix of each training sample as input and the corrected telesignaling adjacency matrix predicted by each training sample as output, the neural network model to be trained is iteratively trained until the neural network model converges to generate a preset neural network model.

[0042] Based on the above method embodiments, the present invention provides corresponding terminal device embodiments.

[0043] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for correcting the power grid topology structure described in the above-mentioned embodiment of the invention is implemented.

[0044] Based on the above method embodiments, the present invention provides corresponding storage medium embodiments.

[0045] Another embodiment of the present invention provides a storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a method for correcting a power grid topology structure as described in the above-mentioned embodiment of the invention.

[0046] The following beneficial effects are achieved by implementing the present invention:

[0047] Embodiments of the present invention provide a method, apparatus, terminal device, and storage medium for correcting a power grid topology. The present invention can input a telesignaling adjacency matrix and a telemetering matrix into a preset neural network model, so that the neural network model, based on extracted node features, can modify nodes in the telesignaling adjacency matrix that have incorrect connection states based on the node feature similarity comparison results between two adjacent nodes. That is, by comparing the similarity results, it is possible to identify which nodes may have incorrect connection states, and thus, based on the similarity comparison results, the nodes in the telesignaling adjacency matrix that have incorrect connection states can be modified and corrected. This identification and correction process of the present invention is automated, requiring no human intervention, thereby improving error correction efficiency and accuracy. Furthermore, based on the modified adjacency correction matrix, the connection states of each node in the telesignaling data can be detected and corrected in real time and accurately, making the topological analysis of the power grid more accurate and ensuring the stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 The figure is a flow chart of a method for correcting a power grid topology structure provided by an embodiment of the present invention.

[0049] Figure 2 The figure is a schematic structural diagram of a power grid topology correction device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0051] like Figure 1 FIG. 1 is a flow chart of a method for correcting a power grid topology structure provided by an embodiment of the present invention. The method for correcting a power grid topology structure includes:

[0052] Step S1: Obtaining a telesignaling adjacency matrix and a telemetering matrix; wherein the telesignaling adjacency matrix is ​​used to represent the connection status between nodes in the current power grid topology; and the telemetering matrix is ​​used to represent the voltage value of each node;

[0053] Step S2: inputting the telesignaling adjacency matrix and the telemetering matrix into a preset neural network model, so that the neural network model extracts node features corresponding to each node according to the connection status of the nodes in the telesignaling adjacency matrix and the voltage value of the nodes in the telemetering matrix, and modifies the incorrect connection status in the telesignaling adjacency matrix according to the node feature similarity comparison result between the nodes, thereby generating a corrected telesignaling adjacency matrix;

[0054] The training process of the preset neural network model includes:

[0055] The telesignaling adjacency matrix sample data and the corresponding telemetering matrix sample data are used as corresponding training samples, and the actual corrected telesignaling adjacency matrix of each training sample is set;

[0056] Taking each training sample and the actual corrected telesignaling adjacency matrix of each training sample as input and the corrected telesignaling adjacency matrix predicted by each training sample as output, the neural network model to be trained is iteratively trained until the neural network model converges, thereby generating a preset neural network model;

[0057] Step S3: Correcting the current power grid topology according to the modified remote signaling adjacency matrix.

[0058] For step S1, in a preferred embodiment, the deep neural network model of the present invention can output a corrected adjacency matrix based on the input of the telesignaling adjacency matrix and the telemetry matrix.

[0059] Schematically, the dimension of the input telesignaling adjacency matrix is ​​N×N, and the dimension of the telemetry matrix is ​​N×M, where N is the number of nodes in the power grid and M is the number of telemetry indicators. The dimension of the output corrected adjacency matrix is ​​also N×N.

[0060] Assuming a small power system has five nodes, the corresponding telesignaling adjacency matrix and the corrected adjacency matrix for the grid topology have dimensions of 5×5. The telemetry matrix, on the other hand, contains one telemetry indicator for each node and has dimensions of 5×1. Therefore, through training, the neural network model can learn the mapping relationship between the telesignaling adjacency matrix and the telemetry matrix, enabling accurate calculation and correction of errors in the adjacency matrix.

[0061] Regarding step S2, in a preferred embodiment, the training process of the preset neural network model includes:

[0062] First, each telesignaling adjacency matrix sample data and the telemetering matrix sample data corresponding to each telesignaling adjacency matrix sample data are used as corresponding training samples, and each training sample corresponds to an actual corrected telesignaling adjacency matrix. It can be understood that when the telesignaling adjacency matrix in the training sample is error-free, the corresponding actual corrected telesignaling adjacency matrix is ​​the telesignaling adjacency matrix itself; when the telesignaling adjacency matrix in the training sample contains errors, the corresponding actual corrected telesignaling adjacency matrix is ​​the modified telesignaling adjacency matrix corresponding to the telesignaling adjacency matrix.

[0063] The neural network model to be trained is then iteratively trained using a number of training samples and the actual corrected telesignaling adjacency matrix corresponding to each training sample as input, and the predicted corrected telesignaling adjacency matrix of each training sample as output, until the neural network model converges and a preset neural network model is generated. Specifically, telesignaling data and telemetering data of the power grid are collected, and features are extracted from the telesignaling data and telemetering data, including the on / off status of the line and the voltage value of the node, to generate telesignaling adjacency matrix samples and telemetering matrix samples, thereby marking known correct or incorrect connection states in the telesignaling adjacency matrix samples, and using the marked actual corrected telesignaling adjacency matrix as input to the model, as well as the telesignaling adjacency matrix samples and telemetering matrix samples as input to the model, so that when the predicted corrected telesignaling adjacency matrix of each training sample is output, iterative training of the neural network model can be achieved.

[0064] In a preferred embodiment, during each iterative training, a training sample is input into the neural network model so that the neural network model generates a predicted corrected telesignaling adjacency matrix corresponding to the training sample based on node features in the training sample;

[0065] The predicted corrected telesignaling adjacency matrix is ​​compared with the actual corrected telesignaling adjacency matrix corresponding to the training samples, and the network parameters of the neural network model are adjusted according to the comparison results.

[0066] In principle, the hyperparameters of the neural network model (such as learning rate, batch size, number of iterations, etc.) can be adjusted through methods such as cross-validation, and the performance of the neural network model can be evaluated using a test set during and after training, including indicators such as accuracy, recall, and F1 score.

[0067] Through the above steps, a neural network model can be trained to accurately correct the incorrect connection status in the power grid topology and applied to the actual power grid topology correction task.

[0068] In a preferred embodiment, the neural network model is composed of a graph neural network, a convolutional neural network, and a recurrent neural network. Specifically, a graph neural network (GNN) can be used to process graph structure data, and a convolutional neural network (CNN) and a recurrent neural network (RNN) can be combined to extract spatial and temporal features, thereby extracting the node features corresponding to each node, and based on the calculated node feature similarity, a comparison between each node is achieved, so that the incorrect connection status in the remote signal adjacency matrix can be modified and the corrected matrix can be output.

[0069] Furthermore, based on the trained neural network model, the node features of each node can be extracted from the input telesignaling adjacency matrix and telemetry matrix. The specific extraction process is as follows:

[0070] The neural network model converts the remote signaling adjacency matrix and the remote sensing matrix into a graph structure;

[0071] Based on the connection status and voltage value of each node in the graph structure, the node features of each node are generated; wherein the node features are used to characterize the node's own attribute information and the interaction information between nodes.

[0072] Specifically, the node features include: node pair features, node degree features, and node state features. When identifying and convolving the graph structure, the corresponding node features can be obtained based on the connection status between the nodes in the graph structure and the voltage value of each node. Then, we have:

[0073] Based on the connection status between each node in the graph structure, a node pair feature is generated in the graph structure to characterize the strength of the relationship between two connected nodes. Schematically, the node pair feature can directly reflect the actual connection status and electrical interaction between nodes in the power grid, so that incorrect connection status can be more accurately identified based on this feature.

[0074] The connection status of each node in the graph structure and the voltage value of each node are aggregated to generate node degree features and node status features. It can be understood that the node degree feature is used to characterize the quantity feature of a node connected to other nodes; the node status feature is used to characterize the degree of voltage difference between a node and other adjacent nodes.

[0075] Schematically, for each node in a graph, the number of directly connected neighboring nodes is counted. This number is the node's degree, and is used as the node degree feature. The node degree feature is a fundamental and important attribute of the graph structure. By calculating the number of connections, it can reflect the importance and influence of a node in the network.

[0076] In the graph structure, for each node, statistics such as the average, maximum, and minimum voltage differences between it and all adjacent nodes are calculated to obtain node state features. Node state features reflect the electrical state and operating conditions of a node in the power grid. By considering the degree of voltage variation, node state features can provide more detailed electrical information, helping the neural network model more accurately understand and correct errors in the power grid topology.

[0077] Therefore, by extracting node pair features, node degree features, and node state features, rich node feature information can be provided for the neural network model to achieve accurate correction and optimization of the power grid topology.

[0078] Furthermore, after the above-mentioned node feature extraction is completed, the neural network model of the present invention can also obtain the embedded vector representation of each node based on each node feature, thereby realizing similarity comparison and matrix modification.

[0079] Specifically, the method of modifying the incorrect connection state in the remote signaling adjacency matrix according to the node feature similarity comparison result between the nodes, and then generating the modified remote signaling adjacency matrix, specifically includes:

[0080] For each node, generating an embedding vector representation corresponding to the node based on the attention weight between the node and the target node used to represent the connection with the node, and the node feature of the node;

[0081] Specifically, the nodes connected to the node are first taken as target nodes, and the attention weights corresponding to the node and each target node are calculated through the attention mechanism;

[0082] After aggregating the attention weights, the node pair features, the node degree features, and the node state features of the node, an embedding vector representation corresponding to the node can be generated.

[0083] It can be understood that by generating embedded vector representations, high-dimensional features (such as node pair features, node degree features, and node state features) can be mapped into a low-dimensional space while retaining key information and relationships. This not only reduces computational complexity but also makes comparisons between features more efficient and intuitive.

[0084] Moreover, the embedding vector can capture the complex relationships between nodes. After the attention weights are calculated by the attention mechanism, the present invention can further emphasize the neighboring nodes that have a greater impact on a specific node, thereby better reflecting these relationships in the embedding vector and achieving accurate representation of the vector.

[0085] In the actual operation of the power grid, the status and connection relationship of nodes may change. By generating an embedded vector representation, the characteristics of the nodes can be easily updated and the embedded vector can be recalculated, thereby achieving dynamic correction of the power grid topology.

[0086] In a preferred embodiment, the node features in the graph can be weighted and aggregated, that is, by introducing an attention mechanism, the weights between different nodes can be dynamically adjusted to more accurately capture important neighborhood information. In the previous step, the model has extracted node features. The introduction of the attention mechanism and further feature aggregation operations can more accurately capture the dependencies between nodes, thereby generating an embedding vector. The embedding vector representation h of each node can be generated according to the following formula i :

[0087] h i =σ(∑ j∈N(i) α ij W*h j );

[0088] Among them, h i represents the embedding vector representation of the i-th node, α ij is the attention weight α between node i and target node j ij , N is the number of target nodes connected to the node, σ is the activation function, and W is the weight matrix.

[0089] α ij The weight reflects the importance of target node j to node i, and a higher attention weight α ijThis indicates that target node j is more important to node i and may need to be connected, so the similarity between the two will be higher.

[0090] Furthermore, the embedding vector representation of the node and the embedding vector representation corresponding to the target node can be compared for similarity, and then the connection state between the node and the target node in the remote signaling adjacency matrix is ​​modified according to each similarity comparison result, and then a modified adjacency correction matrix is ​​generated.

[0091] Specifically, the connection status between nodes includes: whether a connection exists or not;

[0092] Then, for each target node, firstly, the cosine similarity value between the embedding vector representation of the target node and the embedding vector representation of the node is calculated and determined;

[0093] Next, the cosine similarity value is compared with a preset similarity threshold;

[0094] When it is determined that the cosine similarity value is less than a preset similarity threshold, if the connection state between the target node and the node is connected, the connection state between the target node and the node in the remote signaling adjacency matrix is ​​modified to not be connected; if the connection state between the target node and the node is not connected, the connection state between the target node and the node in the remote signaling adjacency matrix is ​​kept unchanged;

[0095] When it is determined that the cosine similarity value is greater than a preset similarity threshold, if the connection status between the target node and the node is that there is no connection, the connection status between the target node and the node in the telesignaling adjacency matrix is ​​modified to be connected; if the connection status between the target node and the node is that there is a connection, the connection status between the target node and the node in the telesignaling adjacency matrix remains unchanged.

[0096] Schematically, by calculating the cosine similarity between the embedding vectors of the nodes, the similarity between the nodes can be quantified, thereby taking into account the comprehensive influence between the features and being able to effectively represent the relationship between the nodes in a low-dimensional space.

[0097] When the similarity is lower than the preset threshold, it means that the similarity between the two nodes is not high, indicating that there is no connection relationship between the two. If the connection status between the target node and the node is connected, then the corresponding connection status needs to be modified. The connection status between the target node and the node in the telesignaling adjacency matrix can be modified to no connection.

[0098] When the similarity is higher than the preset threshold, it means that the similarity between the two nodes is very high, indicating that there should be a connection relationship between the two. If the connection status between the target node and the node is that there is no connection, then the corresponding connection status needs to be modified. The connection status between the target node and the node in the telesignaling adjacency matrix can be modified to be connected.

[0099] In a preferred embodiment, it is assumed that there is an adjacency matrix A of a power grid, which represents the connection relationship between nodes in the power grid (0 represents no connection, and 1 represents connection).

[0100] The embedded features of nodes 1 and 2 are h1 and h2 respectively. If the similarity of the embedded feature vectors h1 and h2 of two nodes is very high, it can be inferred that the two nodes are highly related in function or should be connected to each other, but nodes 1 and 2 have no direct connection in the adjacency matrix (i.e., A 12 =0), so the adjacency matrix is ​​updated to A, so that A 12 =1.

[0101] On the contrary, if the similarity of the embedded feature vectors h1 and h2 of two nodes is very low, it can be inferred that the two nodes are highly unrelated in function, but nodes 1 and 2 have a direct connection in the adjacency matrix (i.e., A 12 =1), then update the adjacency matrix to A, so that A 12 =0.

[0102] Thus, through the above-mentioned method, the similarity comparison is closely combined with the modification of the adjacency matrix, so that the finally generated adjacency matrix is ​​more consistent with the actual network structure and node status.

[0103] Therefore, the neural network model of the present invention processes the data of the power grid topology structure by combining graph neural network (GNN), convolutional neural network (CNN) and recurrent neural network (RNN), that is, converting and identifying the telesignaling adjacency matrix and the telemetry matrix to extract and compare node features, thereby correcting the incorrect connection status.

[0104] The present invention calculates the cosine similarity between node embedding vectors and, in combination with a preset similarity threshold, can accurately identify potential incorrect connection states in the telesignaling adjacency matrix. Furthermore, the entire process is automated, requiring no human intervention. This involves automatically learning node features, generating embedding vectors, performing similarity comparisons, and modifying connection states through a neural network model. This improves the efficiency and accuracy of grid topology corrections and reduces the potential for human error.

[0105] For step S3, in a preferred embodiment, the present invention can compare the original adjacency matrix and the corrected adjacency matrix to find the differences between the two, such as new connections that need to be added and erroneous connections that need to be deleted, so as to actually modify the current power grid topology based on the differences between the two.

[0106] If the connection status between two nodes in the corrected matrix is ​​1 (indicating a connection exists), but is 0 (indicating no connection exists) in the original adjacency matrix, a new connection is added between the two nodes.

[0107] If the connection status between two nodes in the correction matrix is ​​0 (indicating no connection) but is 1 (indicating a connection) in the original adjacency matrix, the incorrect connection between the two nodes is deleted.

[0108] The corrected grid topology is then updated to the grid topology database for subsequent grid management and analysis, ensuring that the database is updated in a timely and accurate manner so that subsequent applications can work based on the latest grid topology.

[0109] Therefore, through the above steps, the current power grid topology can be effectively corrected according to the adjacency correction matrix, thereby improving the reliability and operation efficiency of the power grid.

[0110] Therefore, the present invention can ensure the reliability and stability of the power grid by timely correcting errors in the topology structure, reduce the risk of failures and power outages caused by topology errors, and is of great significance for ensuring the safe and stable operation of the power system.

[0111] like Figure 2 As shown, based on the above-mentioned embodiments of the method for correcting various power grid topologies, the present invention provides corresponding device embodiments;

[0112] An embodiment of the present invention provides a device for correcting a power grid topology structure, comprising: a matrix acquisition module, a matrix correction module, and a topology structure correction module;

[0113] The matrix acquisition module is used to obtain a telesignaling adjacency matrix and a telemetry matrix; wherein the telesignaling adjacency matrix is ​​used to represent the connection status between each node in the current power grid topology; and the telemetry matrix is ​​used to represent the voltage value of each node;

[0114] The matrix correction module is used to input the telesignaling adjacency matrix and the telemetry matrix into a preset neural network model, so that the neural network model extracts the node features corresponding to each node according to the connection status of the nodes in the telesignaling adjacency matrix and the voltage values ​​of the nodes in the telemetry matrix, and according to the node feature similarity comparison results between the nodes, corrects the incorrect connection status in the telesignaling adjacency matrix, and then generates a corrected telesignaling adjacency matrix;

[0115] The topology correction module is used to correct the current power grid topology according to the modified remote signaling adjacency matrix;

[0116] The training process of the preset neural network model includes:

[0117] The training process of the preset neural network model includes:

[0118] The telesignaling adjacency matrix sample data and the corresponding telemetering matrix sample data are used as corresponding training samples, and the actual corrected telesignaling adjacency matrix of each training sample is set;

[0119] Taking each training sample and the actual corrected telesignaling adjacency matrix of each training sample as input and the corrected telesignaling adjacency matrix predicted by each training sample as output, the neural network model to be trained is iteratively trained until the neural network model converges to generate a preset neural network model.

[0120] It should be noted that the device embodiments described above are merely illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without paying any creative effort.

[0121] Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0122] Based on the above-mentioned embodiments of the method for correcting various power grid topologies, the present invention provides corresponding embodiments of terminal equipment.

[0123] An embodiment of the present invention provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a method for correcting a power grid topology structure as described in any method embodiment of the present invention.

[0124] The terminal device may be a computing terminal device such as a desktop computer, a notebook computer, a palmtop computer, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0125] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0126] The memory can be used to store the computer program, and the processor implements various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created based on the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device or other volatile solid-state storage device.

[0127] Based on the above-mentioned embodiments of the method for correcting various power grid topologies, the present invention provides corresponding embodiments of storage media items.

[0128] An embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute a method for correcting a power grid topology structure as described in any method embodiment of the present invention.

[0129] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0130] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for correcting a power grid topology, characterized in that: include: Obtaining a telesignaling adjacency matrix and a telemetering matrix; wherein the telesignaling adjacency matrix is ​​used to represent the connection status between each node in the current power grid topology; and the telemetering matrix is ​​used to represent the voltage value of each node; The telesignaling adjacency matrix and the telemetering matrix are input into a preset neural network model so that the neural network model converts the telesignaling adjacency matrix and the telemetering matrix into a graph structure, generates a node pair feature in the graph structure for characterizing the strength of the relationship between two connected nodes according to the connection status between each node in the graph structure, aggregates the connection status of each node in the graph structure and the voltage value of each node to generate a node degree feature and a node state feature, modifies the incorrect connection status in the telesignaling adjacency matrix according to the similarity comparison result of the node pair feature, the node degree feature and the node state feature between the nodes, and then generates a corrected telesignaling adjacency matrix; wherein the node degree feature is used to characterize the quantity feature of a node connected to other nodes; and the node state feature is used to characterize the degree of voltage difference between a node and other adjacent nodes; Correcting the current power grid topology according to the modified remote signaling adjacency matrix; The training process of the preset neural network model includes: The telesignaling adjacency matrix sample data and the corresponding telemetering matrix sample data are used as corresponding training samples, and the actual corrected telesignaling adjacency matrix of each training sample is set; Taking each training sample and the actual corrected telesignaling adjacency matrix of each training sample as input and the corrected telesignaling adjacency matrix predicted by each training sample as output, the neural network model to be trained is iteratively trained until the neural network model converges to generate a preset neural network model.

2. A method for correcting a power grid topology structure according to claim 1, characterized in that: The method of modifying the incorrect connection state in the remote signaling adjacency matrix based on the similarity comparison results of the node pair features, the node degree features and the node state features between the nodes, and then generating the modified remote signaling adjacency matrix, includes: For each node, generate an embedding vector representation corresponding to the node based on the attention weight between the node and the target node used to represent the connection with the node, as well as the node pair feature, node degree feature, and node state feature of the node; The embedding vector representation of the node and the embedding vector representation corresponding to the target node are respectively compared for similarity, and then the connection state between the node and the target node in the remote signaling adjacency matrix is ​​modified according to each similarity comparison result, and then a modified remote signaling adjacency matrix is ​​generated.

3. A method for correcting a power grid topology structure according to claim 2, characterized in that: Generating an embedding vector representation corresponding to the node according to the attention weight between the node and a target node used to represent a connection with the node, as well as the node pair feature, the node degree feature, and the node state feature of the node, includes: The node connected to the node is taken as the target node, and the attention weights corresponding to the node and each target node are calculated through the attention mechanism; Each attention weight, as well as the node pair feature, node degree feature, and node state feature of the node are aggregated to generate an embedding vector representation corresponding to the node.

4. A method for correcting a power grid topology structure according to claim 3, characterized in that: The connection status includes: whether a connection exists or not; The node embedding vector representation and the target node embedding vector representation are respectively compared for similarity, and then the connection state between the node and the target node in the remote signaling adjacency matrix is ​​modified according to each similarity comparison result, including: For each target node, determining a cosine similarity value between the embedding vector representation of the target node and the embedding vector representation of the node; Comparing the cosine similarity value with a preset similarity threshold; When it is determined that the cosine similarity value is less than a preset similarity threshold, if the connection state between the target node and the node is connected, the connection state between the target node and the node in the remote signaling adjacency matrix is ​​modified to not be connected; if the connection state between the target node and the node is not connected, the connection state between the target node and the node in the remote signaling adjacency matrix is ​​kept unchanged; When it is determined that the cosine similarity value is greater than a preset similarity threshold, if the connection status between the target node and the node is that there is no connection, the connection status between the target node and the node in the telesignaling adjacency matrix is ​​modified to be connected; if the connection status between the target node and the node is that there is a connection, the connection status between the target node and the node in the telesignaling adjacency matrix remains unchanged.

5. The method for correcting a power grid topology structure according to claim 1, wherein: During each iterative training, a training sample is input into the neural network model so that the neural network model generates a predicted and corrected telesignaling adjacency matrix corresponding to the training sample according to node features in the training sample; The predicted corrected telesignaling adjacency matrix is ​​compared with the actual corrected telesignaling adjacency matrix corresponding to the training samples, and the network parameters of the neural network model are adjusted according to the comparison results.

6. A device for correcting the topology of a power grid, characterized in that: include: Matrix acquisition module, matrix correction module and topology correction module; The matrix acquisition module is used to obtain a telesignaling adjacency matrix and a telemetry matrix; wherein the telesignaling adjacency matrix is ​​used to represent the connection status between each node in the current power grid topology; and the telemetry matrix is ​​used to represent the voltage value of each node; The matrix correction module is used to input the telesignaling adjacency matrix and the telemetry matrix into a preset neural network model so that the neural network model converts the telesignaling adjacency matrix and the telemetry matrix into a graph structure, generates a node pair feature in the graph structure for characterizing the strength of the relationship between two connected nodes according to the connection status between each node in the graph structure, aggregates the connection status of each node in the graph structure and the voltage value of each node to generate a node degree feature and a node state feature, and modifies the erroneous connection status in the telesignaling adjacency matrix according to the similarity comparison results of the node pair features, node degree features and node state features between the nodes, and then generates a corrected telesignaling adjacency matrix; wherein the node degree feature is used to characterize the quantity feature of a node connected to other nodes; and the node state feature is used to characterize the degree of voltage difference between a node and other adjacent nodes; The topology correction module is used to correct the current power grid topology according to the modified remote signaling adjacency matrix; The training process of the preset neural network model includes: The telesignaling adjacency matrix sample data and the corresponding telemetering matrix sample data are used as corresponding training samples, and the actual corrected telesignaling adjacency matrix of each training sample is set; Taking each training sample and the actual corrected telesignaling adjacency matrix of each training sample as input and the corrected telesignaling adjacency matrix predicted by each training sample as output, the neural network model to be trained is iteratively trained until the neural network model converges to generate a preset neural network model.

7. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for correcting a power grid topology structure according to any one of claims 1 to 5 is implemented.

8. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is executed, the device where the storage medium is located is controlled to execute the method for correcting the power grid topology structure according to any one of claims 1 to 5.

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